Gradual Migration from Google Cloud to AWS EKS with Zero Downtime

1. Introduction

Purpose: This report details a comprehensive technical strategy for migrating services currently operating on Google Cloud Platform (GCP), specifically Google CloudRun and Google Kubernetes Engine (GKE), to Amazon Web Services (AWS) Elastic Kubernetes Service (EKS). The primary objective is to achieve this migration gradually, with zero downtime for end-users, leveraging the existing Google Cloud DNS infrastructure for traffic management throughout the transition period.

Context: The current infrastructure involves domain registration managed at GoDaddy, while the authoritative DNS service, including separate zones for production, staging, and UAT environments, resides within Google Cloud DNS. Services are distributed across CloudRun and GKE instances within corresponding GCP projects. The strategic goal is to transition these services to AWS EKS.

Core Requirements: The migration strategy must adhere to several critical requirements:

• Gradual Transition: The migration cannot be a “big bang” cutover. Services must be moved incrementally.
• Concurrent Operation: For a defined period, services must run simultaneously on both GCP (CloudRun/GKE) and AWS (EKS), serving end-user traffic.
• Zero Downtime: End-users must experience no service interruption during any phase of the migration, including the final cutover.
• Incremental Traffic Shifting: Traffic must be shifted progressively from GCP to AWS endpoints, allowing for monitoring and validation at each stage.
• Stability Validation: The AWS EKS environment must be proven stable and performant under production load before completing the full migration.

Approach Overview: This report outlines a phased approach addressing the core requirements. It begins by detailing DNS-based traffic management strategies using Google Cloud DNS. It then explores alternative Global Server Load Balancing (GSLB) and application-layer traffic control options (API Gateways, Service Meshes). Critical considerations for maintaining data consistency for stateful applications are analyzed, followed by strategies for comprehensive multi-cloud monitoring. A detailed, phased migration plan including validation criteria and rollback procedures is presented. Finally, the report provides a comparative analysis of the different traffic management approaches to inform the optimal strategy for this specific scenario.

2. DNS-Based Traffic Management Strategy (Google Cloud DNS)

Context: Given that Google Cloud DNS is the authoritative provider for the domains in question, its features can be directly leveraged to manage traffic distribution between the existing GCP environment and the new AWS EKS environment during the migration. This section focuses on utilizing Google Cloud DNS capabilities, specifically Weighted Routing Policies and Health Checks, combined with strategic Time-To-Live (TTL) management, to achieve a gradual and resilient traffic shift.

Utilizing Weighted Routing Policies (WRR) for Gradual Traffic Splitting:

• Concept: Google Cloud DNS supports Weighted Round Robin (WRR) routing policies.¹ These policies allow administrators to assign different numerical weights to DNS resource records (e.g., A records pointing to IP addresses) associated with the same DNS name.¹ Cloud DNS then distributes incoming DNS queries (and thus, user traffic) to the target endpoints proportionally based on these configured weights.¹ This mechanism is ideal for implementing active-active configurations or gradually shifting traffic between different versions or environments of a service.¹
• Implementation: The migration will utilize WRR policies applied to the A or CNAME records corresponding to the service hostnames being migrated.
  1. Initially, the DNS records within the WRR policy will point to the IP addresses of the existing GCP load balancers or CloudRun service endpoints, assigning them 100% of the weight (e.g., weight 1000). The IP address(es) of the AWS EKS load balancer(s) (e.g., AWS Application Load Balancer or Network Load Balancer) will also be added to the same WRR record set but assigned a weight of 0.²
  2. As the migration progresses (detailed in Section 7), the weights will be adjusted incrementally. For example, traffic can be shifted by changing weights to 90% GCP / 10% AWS, then 50/50, then 10/90, and finally 0/100.² The proportion of traffic directed to a specific target is calculated by dividing its weight by the sum of all weights in the policy.⁴ Configuration can be done via the Google Cloud Console or gcloud CLI.⁴
• Multi-Cloud Applicability: Google Cloud DNS WRR policies explicitly support routing traffic to external IP addresses located outside of Google Cloud, such as the public IP addresses associated with AWS Elastic Load Balancers (ELBs) fronting the EKS cluster.¹ This capability is fundamental to enabling the gradual shift of traffic from GCP to AWS using the existing DNS infrastructure.
• Limitations: It is important to note that a WRR routing policy cannot be combined with a Geolocation routing policy within the same resource record set.¹ Only one policy type can be applied at a time, except when configuring failover policies where Geolocation can act as a backup.⁴

Health Checks for External Endpoints (AWS EKS):

• Importance: To ensure zero downtime and resilience during the migration, health checks are indispensable. Cloud DNS health checks monitor the availability of endpoints defined in routing policies. If an endpoint becomes unhealthy, Cloud DNS automatically stops sending traffic to it, redirecting users to the remaining healthy endpoints.¹ This prevents users from being directed to unresponsive services hosted on AWS EKS during the transition.
• Configuration for External Endpoints: Since the target EKS environment is external to GCP, specific configuration for external endpoint health checks within Cloud DNS (available for public zones) is required.¹ The process involves:
  1. Creating a Health Check Resource: Define a new health check within the GCP project, either via the console or gcloud beta compute health-checks create.⁴
  2. Specifying Parameters: Configure the protocol (TCP, HTTP, or HTTPS are supported for external checks ¹), the port number on the AWS ELB to check, and optionally, specific HTTP request paths or expected response strings for content-based validation.¹
  3. Source Regions: Select three geographically diverse Google Cloud regions from which the health check probes will originate. This ensures reliable monitoring even if one probing region experiences issues.¹
  4. Check Intervals and Thresholds: Define the frequency of checks (check-interval, must be 30-300 seconds for external checks ¹), the timeout period for responses, and the number of consecutive successful (Healthy threshold) or failed (Unhealthy threshold) probes required to change the endpoint’s health status.⁴
  5. Association: Link the created health check to the specific external IP address (AWS ELB IP) within the WRR routing policy configuration.⁴ This is done by selecting the health check when adding or editing the external IP address entry in the policy.⁴
• Firewall Considerations: A critical step is to configure the AWS security groups associated with the EKS load balancer and potentially network ACLs to explicitly allow incoming traffic from Google Cloud’s health check systems on the designated protocol and port.¹ Unlike health checks for internal GCP resources, the source IP ranges for external health checks are not fixed and can change.¹ Therefore, firewall rules must be permissive enough (ideally allowing traffic from 0.0.0.0/0 specifically on the health check port, or using known GCP IP ranges if available and accepting the risk of needing updates). If probes are blocked, the AWS endpoint will be incorrectly marked as unhealthy.¹
• Behavior on Failure: When using WRR with health checks, if an AWS endpoint fails its health checks, Cloud DNS automatically stops routing traffic to it. The traffic share previously allocated to the failed endpoint is redistributed proportionally among the remaining healthy endpoints (which could include other AWS endpoints or the GCP endpoints still receiving traffic).¹ If all endpoints within the WRR policy become unhealthy, Cloud DNS will revert to routing traffic to all configured endpoints, regardless of health status, to avoid a complete outage.¹
• Health Check Granularity and Layering: It’s important to understand the scope of Cloud DNS health checks. They operate by probing the IP address specified in the DNS record, which in this case is the AWS Load Balancer.¹ This provides a check on the availability of the load balancer itself. However, AWS Load Balancers (both ALB and NLB) perform their own, more granular health checks against the individual pods or instances running within the EKS cluster.⁸ A Cloud DNS health check might pass if the AWS LB is responsive, even if some backend EKS pods are failing the LB’s internal health checks. Therefore, relying solely on DNS-level health checks is insufficient. They act as a crucial first layer of defense, particularly against total LB failure or network reachability issues from GCP’s perspective, but must be complemented by robust application and infrastructure health checks configured within the AWS environment (EKS liveness/readiness probes, ELB health checks). Additionally, configuring health checks in GCP incurs operational overhead and potentially minor costs associated with the health checking infrastructure.¹⁰

Evaluating Geolocation/Latency Routing:

• Geolocation Policy: Google Cloud DNS offers Geolocation routing policies that direct traffic based on the geographic origin of the DNS query.¹ Specifically, it uses the Google Cloud region where the query enters Google’s network as the source geography.¹ This allows routing users from, for example, European GCP regions to European service instances and US GCP regions to US instances.² It can be used for traffic originating outside or inside Google Cloud.¹ Configuration involves mapping source geographies (GCP regions) to target IP addresses.³
• Latency Policy: True latency-based routing, which directs users to the endpoint with the lowest measured network latency for that specific user, is a common feature of advanced GSLB systems.¹³ While highly effective for performance optimization ¹³, the provided documentation for Google Cloud DNS does not explicitly list a distinct “Latency” routing policy type akin to AWS Route 53’s offering.¹ Geolocation routing in Cloud DNS might offer a proxy for latency by directing users to the nearest GCP region’s endpoint, but this is not equivalent to real-time latency measurement from the user’s perspective.²
• Applicability to Migration:
  • Geolocation routing could be considered if the migration plan involves deploying EKS clusters in multiple AWS regions intended to serve users geographically aligned with specific GCP regions. However, it cannot be combined with the WRR policy needed for gradual traffic shifting.¹
  • The precision of GCP’s Geolocation policy for multi-cloud routing to external endpoints like AWS is limited. Since routing decisions are based on the GCP region the query enters, not necessarily the user’s actual location (especially for users outside GCP), it may not reliably route users to the geographically closest or lowest-latency AWS endpoint.¹ More sophisticated GSLB solutions often use techniques like EDNS Client Subnet (ECS) or latency probing from resolver locations for more accurate user location determination.¹³
  • Therefore, for the primary goal of a controlled, percentage-based migration from GCP to AWS EKS (potentially in a single region initially), the WRR policy is the more direct, controllable, and suitable mechanism within Google Cloud DNS.

DNS TTL Management for Seamless Transitions:

• Concept: The Time-To-Live (TTL) value set on a DNS record instructs DNS resolvers (like those used by ISPs or public DNS services like Google’s 8.8.8.8) how long they are permitted to cache that record’s information before needing to query the authoritative DNS server (Google Cloud DNS in this case) again.¹⁷ A high TTL (e.g., 86400 seconds / 24 hours) reduces load on authoritative servers and can improve perceived lookup speed due to caching, while a low TTL (e.g., 300 seconds / 5 minutes) ensures that changes to the DNS record propagate more quickly.¹⁷
• Migration Strategy: During DNS-based migrations, especially those involving traffic shifting or failover, it is a critical best practice to significantly lower the TTL values for the DNS records being modified.¹⁷ For this migration, the TTL for the primary service A/CNAME records managed by the WRR policy should be reduced from their current (potentially high) values to a much lower value, such as 300 seconds (5 minutes) or potentially even 60 seconds.¹⁷
• Timing: This TTL reduction must be performed well in advance of making the first change to the WRR weights or performing the final cutover. The lead time should be at least as long as the previous high TTL value.¹⁷ For instance, if the current TTL is 24 hours, it should be lowered to 5 minutes at least 24 hours before initiating traffic shifts. This ensures that all intermediate DNS resolvers expire their caches of the old record with the high TTL.
• Zero Downtime Impact: Low TTLs are fundamental to achieving zero downtime during the WRR weight adjustments and the final 100% cutover.¹⁷ When weights are changed in the WRR policy, or when the final switch to 100% AWS is made, low TTLs ensure that resolvers across the internet quickly pick up these changes. This minimizes the duration during which users might be directed to an endpoint that is being scaled down, decommissioned, or experiencing issues, thus enabling a smoother, near-instantaneous transition from the end-user perspective.¹⁹
• Post-Migration: Once the migration to AWS EKS is complete, validated, and stable, the TTL values should be increased back to more standard, higher levels (e.g., 3600 seconds / 1 hour, or even 86400 seconds / 24 hours, depending on how frequently the underlying IP might change).¹⁷ This reduces the query load on Google Cloud DNS, lowers potential costs associated with high query volumes, and improves DNS resolution performance for end-users by allowing effective caching.¹⁷
• Caveats and Considerations: While low TTLs are beneficial for migration agility, extremely low values (e.g., below 30-60 seconds) might not be honored by all DNS resolvers, as some may enforce a minimum caching time (cache clamping).²⁴ Additionally, very low TTLs significantly increase the number of queries hitting the authoritative DNS servers (Google Cloud DNS), which could have cost implications depending on the query volume and DNS provider pricing.¹⁸ A value between 60 and 300 seconds is generally a reasonable compromise during active migration phases.
• Negative Cache TTL (SOA Minimum): Beyond the TTL for positive records (like A records), the SOA (Start of Authority) record for the DNS zone contains a parameter often referred to as the Minimum TTL or Negative Cache TTL.¹⁸ This value dictates how long resolvers should cache the fact that a record does not exist (an NXDOMAIN response). While the primary focus during WRR shifts is the TTL of the A/CNAME record itself, ensuring the SOA Minimum TTL isn’t excessively high (e.g., days) provides additional agility. If a record were temporarily deleted or renamed during the migration, a high negative TTL could cause users who query during that window to cache the non-existence for an extended period. A common recommendation for the SOA Minimum TTL is between 900 seconds (15 minutes) and 3600 seconds (1 hour).¹⁸ Reviewing and potentially adjusting this value in Cloud DNS alongside the main record TTLs is advisable for maximum responsiveness during the migration.

3. Global Server Load Balancing (GSLB) Options

Context: While Google Cloud DNS WRR offers a foundational level of traffic distribution suitable for the gradual migration, dedicated Global Server Load Balancing (GSLB) solutions provide more advanced capabilities specifically designed for managing traffic across geographically dispersed or multi-cloud environments. Evaluating these options provides context for the capabilities and trade-offs compared to the DNS-native approach.

Role of GSLB: GSLB is a technology used to distribute user traffic across multiple data centers or cloud endpoints, which can be located anywhere globally.⁵ Its primary goals are to enhance application performance by routing users to the geographically nearest or lowest-latency server, improve resilience by automatically failing over traffic away from unhealthy sites, and enable better scalability by leveraging a global resource pool.⁵

GSLB vs. DNS WRR: Compared to standard DNS mechanisms like WRR, GSLB solutions typically offer several advantages ⁵:

• Sophisticated Health Checking: GSLB often performs more intelligent health checks, potentially probing application-level health rather than just IP/port reachability.⁵
• Advanced Routing Algorithms: Beyond simple weighting, GSLB commonly supports routing based on latency, geographic proximity (sometimes with finer granularity than DNS source region), server load, or round-trip time (RTT).⁵
• Faster Failover: Dedicated GSLB platforms may detect endpoint failures and reroute traffic more quickly than relying solely on DNS TTL expiry and propagation.²⁸
• Managed Service/Appliance: GSLB is often delivered as a managed cloud service or via dedicated network appliances (physical or virtual), abstracting some operational complexity.⁵

Analysis of GSLB Solutions:

• Google Cloud Load Balancing (Global): Google Cloud offers powerful global load balancing, capable of distributing traffic across multiple GCP regions using a single Anycast IP address.³¹ It reacts quickly to changes in health and traffic conditions and integrates seamlessly with GCP services.³¹ However, its native ability to perform sophisticated GSLB specifically directing traffic to external endpoints like AWS EKS might be less mature than dedicated multi-cloud GSLB solutions. While Internet Network Endpoint Groups (NEGs) allow targeting external IPs ³², configuring complex multi-cloud routing logic (like latency-based routing to AWS) might be challenging or less feature-rich compared to platforms designed explicitly for this purpose. GKE Multi-cluster Gateways primarily focus on traffic between GKE clusters, potentially including Anthos-managed clusters in other environments, which may not directly apply to a standard EKS target.³³
• AWS Global Accelerator / Route 53: AWS provides robust GSLB capabilities. AWS Global Accelerator optimizes the network path for traffic destined for AWS endpoints by leveraging the AWS global network, improving performance and resilience.³⁵ AWS Route 53 offers DNS-level GSLB with various routing policies (Weighted, Latency, Geolocation, Failover) coupled with integrated health checks.¹³ While powerful, utilizing Route 53 for GSLB would necessitate migrating DNS management away from Google Cloud DNS, contradicting the user’s requirement to leverage their existing setup. Global Accelerator is primarily focused on optimizing ingress to AWS, not distributing traffic between GCP and AWS.
• Cloudflare Load Balancing: Cloudflare offers a cloud-agnostic SaaS load balancing solution explicitly designed for multi-cloud (AWS, GCP, Azure, etc.) and hybrid cloud deployments.²⁹ It operates on Cloudflare’s global edge network.¹⁶ Key features include various routing algorithms (geographic steering, dynamic latency-based routing), configurable health checks for origin servers, rapid failover capabilities, and integration with Cloudflare’s CDN and security suite.¹⁶ This would require pointing the domain’s DNS resolution to Cloudflare’s nameservers.
• F5 BIG-IP DNS / Distributed Cloud DNS Load Balancer: F5 provides enterprise-focused GSLB solutions, available as hardware appliances, virtual editions (VMs), or as a SaaS offering (Distributed Cloud DNS Load Balancer).³⁸ These solutions offer highly sophisticated health monitoring (application-level checks), a wide array of routing methods (geolocation, topology-based, performance-based), automated disaster recovery workflows, and comprehensive security features.³⁸ They are designed to manage traffic across complex multi-cloud and hybrid environments.³⁸ Integration would involve configuring F5 as the intelligent DNS resolver for the relevant hostnames, potentially requiring DNS delegation.

Suitability for User: The decision to use a dedicated GSLB solution versus relying on Google Cloud DNS WRR hinges on the trade-off between capability and complexity/cost. For the defined goal of a gradual, temporary migration involving percentage-based traffic splits and basic health checking, the native Google Cloud DNS WRR approach is likely sufficient, simpler to implement within the existing infrastructure, and avoids introducing additional vendors or costs.¹ If the long-term strategy involves maintaining an active-active multi-cloud presence, requiring more sophisticated routing (e.g., latency-based), faster automatic failover across clouds, or integrated security features at the GSLB layer, then investing in a dedicated solution like Cloudflare or F5 becomes more justifiable despite the added complexity and cost.²⁹

Control Plane vs. Data Plane Implications: Understanding how different GSLB solutions handle traffic is important. DNS-based GSLB methods, including Google Cloud DNS WRR/Geolocation and AWS Route 53 policies, primarily influence the control plane of the connection.⁵ They direct the client’s DNS resolver to return a specific IP address based on the configured policy. Once the client receives the IP, the subsequent data plane traffic flows directly between the client and the selected endpoint (GCP or AWS).⁵ In contrast, solutions like Cloudflare often act as a reverse proxy, meaning both DNS resolution and the actual user data traffic pass through Cloudflare’s edge network.²⁹ AWS Global Accelerator optimizes the path to AWS by routing traffic over the AWS backbone.³⁵ Some ADC-based GSLB solutions might also proxy data traffic.⁵ This architectural difference impacts latency (direct path vs. proxied path), cost (potential for additional egress/ingress charges through the GSLB provider), and where security inspections or transformations can occur. For this migration, the direct traffic flow offered by Google Cloud DNS is architecturally simpler.

Table: Comparison of GSLB Solutions

Feature	Google Cloud DNS (WRR/Geo)	Google Cloud LB (Global w/ NEG)	AWS Route 53 / GA	Cloudflare LB	F5 Distributed Cloud DNS LB
Primary Mechanism	DNS	Network Load Balancer	DNS / AWS Network	Proxy / DNS	DNS / GSLB Platform
Multi-Cloud Support	External IPs Supported	Via Internet NEG (Limited)	Primarily AWS-focused (GA)	Native Multi-Cloud	Native Multi-Cloud/Hybrid
Routing Methods	Weighted, Geo (GCP Region)	Weighted (via Backend Service)	Weighted, Latency, Geo, Failover	Weighted, Geo, Latency, Dynamic	Weighted, Geo, Latency, Perf.
Health Check Sophistication	IP/Port (TCP/HTTP/S)	GCP Native Checks	Advanced (TCP/HTTP/S, String)	Advanced (TCP/HTTP/S, Custom)	Highly Advanced (App-level)
Failover Speed	DNS TTL Dependent	Fast (within GCP)	Fast (R53) / Optimized (GA)	Fast	Very Fast (Automated DR)
Integration (w/ GCP DNS)	Native	Requires Config	Requires DNS Migration	Requires DNS Delegation/NS Change	Requires DNS Delegation/Config
Control Granularity	Basic (Weights)	Moderate	High (DNS Policies)	High	Very High
Relative Cost/Complexity	Low	Moderate	Moderate (R53) / Higher (GA)	Moderate-High	High

¹

4. Application-Layer Traffic Management (API Gateways & Service Meshes)

Context: Beyond DNS and GSLB, traffic management can be implemented closer to the application layer (Layer 7) using API Gateways or Service Meshes. These technologies offer more granular control over how requests are routed and managed, potentially enabling sophisticated traffic splitting strategies during the migration, albeit with increased complexity.

Leveraging API Gateways for Multi-Cloud Routing:

• Concept: An API Gateway serves as a reverse proxy and a single point of entry for client requests targeting backend APIs or microservices.⁴³ It handles tasks like request routing, composition, authentication, authorization, rate limiting, caching, and protocol translation.⁴³ Crucially, gateways can route incoming requests to different backend services based on criteria like the request path, HTTP headers, query parameters, or method.³³
• Multi-Cloud Use Case: During the migration, an API Gateway could theoretically be configured to route incoming requests to service instances running in both the GCP environment (CloudRun/GKE) and the AWS EKS environment.⁴⁵ This would require the gateway itself to have network visibility and connectivity to both cloud environments. The gateway could be deployed within GCP, within AWS, or potentially as a third-party cloud-agnostic service. Clients would target the API Gateway’s endpoint, and the gateway would manage the split between GCP and AWS backends.
• Platform Examples and Considerations:
  • Google Cloud API Gateway: This is a fully managed service primarily designed to front GCP-native backends such as Cloud Functions, Cloud Run, App Engine, and GKE.⁴⁸ While it might be possible to configure it to route to external endpoints (like an AWS ELB) using features like Serverless Network Endpoint Groups (NEGs) configured for internet endpoints ³², this is not its core design principle and could introduce complexity and potential limitations compared to routing within GCP.⁴⁸
  • AWS API Gateway: Similarly, AWS API Gateway is optimized for routing requests to AWS backend services (Lambda, EC2, ECS, EKS, Step Functions, etc.), often utilizing private integrations via VPC Links to connect securely to resources within a VPC.⁹ Routing traffic to external GCP services would be a non-standard configuration, likely requiring the GCP services to be exposed publicly or accessed via a secure tunnel (VPN/Direct Connect) configured between AWS and GCP, adding significant networking complexity.⁵³
  • Kong Gateway / Apigee: These are examples of more cloud-agnostic API management platforms. Kong Gateway (available as open source or enterprise) and Google’s Apigee (available as SaaS or hybrid deployment) can be deployed in various environments, including Kubernetes clusters on GCP or AWS.⁴⁶ They are explicitly designed to manage APIs regardless of where the backend services reside, making them more suitable for orchestrating traffic across multi-cloud boundaries.⁴⁶ Apigee’s hybrid model, for instance, allows runtime services to be hosted in customer-managed Kubernetes clusters (potentially on AWS) while being managed centrally.⁴⁷
• Implementation: Using an API Gateway for multi-cloud splitting would involve deploying the chosen gateway platform, ensuring it has network paths to both GCP and AWS backends (potentially via VPN/Interconnect or public IPs), defining API routes that target services in both environments, and configuring traffic splitting rules within the gateway itself (e.g., weighted backends, header-based routing). Client DNS would then be pointed to the gateway’s endpoint.

Utilizing Service Meshes (Istio/Linkerd) for Fine-Grained Traffic Control:

• Concept: A service mesh is a dedicated infrastructure layer for managing service-to-service communication within a distributed system, typically implemented using lightweight network proxies (like Envoy for Istio or linkerd2-proxy for Linkerd) deployed alongside each service instance (the “sidecar” pattern).⁵⁷ The mesh provides features like reliable traffic control (retries, timeouts, circuit breaking), sophisticated routing (traffic splitting, mirroring), security (automatic mutual TLS – mTLS), and deep observability (metrics, distributed tracing, access logs) without requiring changes to the application code.⁵⁷
• Multi-Cluster/Multi-Cloud Capabilities: Both Istio and Linkerd, the leading open-source service meshes, offer robust support for multi-cluster deployments.⁵⁸ This allows creating a single logical mesh that spans multiple Kubernetes clusters, potentially residing in different clouds (like GKE and EKS).³⁴ Setting up a multi-cluster mesh typically involves:
  1. Installing the service mesh control plane and data plane components on each participating cluster (GKE and EKS).⁶⁶
  2. Establishing a shared trust domain between the clusters, often using a common root Certificate Authority (CA) or identity federation mechanisms like SPIFFE/SPIRE.⁶⁶
  3. Configuring service discovery across clusters, allowing services in one cluster to find endpoints in another. This might involve DNS synchronization or specialized service mirroring components.⁶⁴
  4. Deploying East-West gateways to handle secure (mTLS) traffic routing between clusters.⁶⁶
• Traffic Splitting: Service meshes provide powerful mechanisms for fine-grained traffic splitting. Using mesh-specific configuration objects (like Istio’s VirtualService and DestinationRule, or Linkerd’s integration with the Kubernetes Gateway API via HTTPRoute) ³³, administrators can define rules to:
  • Split traffic between different versions of a service based on weights (e.g., 90% to v1 on GKE, 10% to v2 on EKS).³³
  • Route specific requests based on HTTP headers, cookies, source labels, or other L7 attributes to either the GCP or AWS environment.
  • Mirror traffic (send a copy) to the new environment for testing without impacting users.³⁴
• Implementation Complexity: Implementing and managing a multi-cluster service mesh across different cloud providers is a complex undertaking.⁵⁸ It requires deep expertise in Kubernetes, networking, security, and the chosen service mesh itself. It introduces additional control plane components and sidecar proxies that consume resources and require ongoing maintenance and upgrades.
• Managed Service Mesh Options:
  • Anthos Service Mesh (ASM): Google’s managed Istio offering, tightly integrated with GKE and the broader Anthos platform.⁵⁷ ASM can simplify Istio deployment and management and supports multi-cluster configurations, potentially including EKS clusters connected via Anthos Multi-Cloud or GKE attached clusters.⁷⁷ This might be a viable option if adopting the Anthos ecosystem is acceptable.
  • AWS App Mesh: AWS’s managed service mesh, also using Envoy proxies.⁵⁷ It is primarily designed for workloads within the AWS ecosystem (EKS, ECS, EC2).⁷¹ While it supports multi-account and potentially multi-cluster setups within AWS ⁷¹, its native capabilities for integrating seamlessly with external GCP/GKE clusters are less emphasized compared to Istio or Linkerd’s multi-cluster features.

Complexity vs. Control Trade-off: A key consideration when evaluating traffic management layers is the trade-off between control granularity and operational complexity.

• DNS/GSLB methods operate at a higher level (L3/L4 or DNS resolution).¹ They are generally simpler to configure for basic weighted distribution between distinct endpoints (like a GCP LB vs. an AWS LB) but offer limited control based on application-level request attributes.⁴⁴
• API Gateways introduce an L7 proxy, enabling routing based on paths, headers, and methods, offering more control than DNS but less than a full service mesh.⁴³ Their complexity lies in managing the gateway itself and ensuring connectivity.
• Service Meshes provide the most fine-grained L7 control, enabling sophisticated routing, resilience patterns (retries, circuit breaking), and security policies directly within the inter-service communication path.⁵⁷ However, this comes at the cost of significant added complexity in deployment, configuration, and ongoing management, especially in a multi-cluster, multi-cloud scenario.⁵⁸

For the specific requirement of a temporary period of concurrent operation with gradual, percentage-based traffic shifting between GCP and AWS, the operational overhead of setting up and maintaining a multi-cloud service mesh might outweigh the benefits of its granular control. The simpler DNS WRR approach (Section 2) or potentially a dedicated GSLB (Section 3) might be sufficient and more practical for achieving the migration goals. If, however, the long-term architecture involves persistent multi-cloud operation with complex inter-service communication patterns, or if very specific L7 routing rules are needed during the transition, then a service mesh could be considered, accepting the associated complexity.

Table: Comparison of Service Mesh Options (Istio vs. Linkerd)

Feature	Istio	Linkerd
Architecture	Complex Control Plane (Istiod), Envoy Proxy (C++)	Simpler Control Plane, linkerd2-proxy (Rust)
Feature Set	Very Comprehensive (Traffic, Security, Observability)	Focused on Core Mesh Features (Simpler)
Complexity	High (Installation, Configuration, Operation) 58	Lower (Designed for Simplicity) 60
Resource Consumption	Higher (Especially Envoy data plane) 73	Lower (Ultralight Rust proxy) 61
Multi-Cluster	Robust Support (Multiple Topologies) 64	Robust Support (Hierarchical, Flat, Federated) 68
Protocol Support	Broad (HTTP/1/2, gRPC, TCP, WebSockets) 61	Broad (HTTP/1/2, gRPC, TCP, WebSockets) 58
Security Focus	Strong (mTLS, AuthZ Policies), Envoy (C++) 58	Strong (mTLS by default), Rust Proxy (Memory Safety) 63
Community/Ecosystem	Large, Backed by Google, IBM, etc. 57	Growing, CNCF Graduated Project 63

⁵⁷

5. Strategies for Maintaining Data Consistency

Context: Migrating applications, particularly those that maintain state (stateful applications), across cloud environments while ensuring zero downtime presents significant challenges related to data consistency.²⁰ During the phase where the application runs concurrently in both GCP and AWS, mechanisms must be in place to ensure that data modifications are reflected accurately and promptly in both locations, or that traffic is directed to the instance with the authoritative state.

Addressing Stateful Application Challenges: The first step is to identify all stateful components within the applications being migrated. This includes relational databases (like PostgreSQL, MySQL), NoSQL databases (like MongoDB), persistent disk volumes attached to GKE pods, stateful caching layers, or any other component that stores data that needs to persist across requests or service restarts. Simply splitting traffic using DNS or load balancers is insufficient for these applications; the underlying state must be consistently accessible or synchronized between the instances running in GCP and AWS.²⁰ Failure to manage state consistency can lead to data loss, incorrect application behavior, and a poor user experience.

Database Replication and Synchronization Techniques:

• Concept: The most common approach for database consistency during migration is to establish replication from the current primary database (likely hosted in GCP initially) to a replica instance in the target AWS environment. This ensures that data changes made in the primary are copied to the replica.²⁰
• Cross-Cloud Managed Databases (Cloud SQL/RDS):
  • Service Comparison: Google Cloud SQL and AWS Relational Database Service (RDS) are the respective managed database offerings.⁸⁴ Both provide managed instances for popular engines like PostgreSQL and MySQL, automating tasks like patching, backups, and failover.⁸⁴ RDS typically supports a wider range of database engines (including Oracle, SQL Server, MariaDB, and Amazon Aurora) compared to Cloud SQL’s focus on MySQL, PostgreSQL, and SQL Server.⁸⁴ Both offer high availability options (Cloud SQL HA configuration, RDS Multi-AZ) and support read replicas for scaling read traffic.⁸⁴ Performance benchmarks and pricing models differ and should be evaluated based on specific needs.⁸⁶
  • PostgreSQL Logical Replication: For migrating PostgreSQL databases, logical replication is a suitable technique as it replicates data changes based on their logical structure (rows/tables) rather than physical block changes, allowing replication between different major versions or platforms.⁸⁸ Setting up logical replication from Cloud SQL for PostgreSQL (as the source/publisher) to RDS for PostgreSQL or PostgreSQL running on EC2/EKS (as the target/subscriber) involves several steps:
    1. Enable logical decoding on the source Cloud SQL instance by setting the cloudsql.logical_decoding flag to on.⁸⁹ This configures the wal_level appropriately.
    2. Ensure network connectivity between the GCP source and AWS target. This typically requires configuring the Cloud SQL instance with a public IP and adding the AWS replica’s IP address to the authorized networks list, or establishing a secure VPN/Interconnect tunnel.⁸⁸
    3. Create a dedicated PostgreSQL user/role with the REPLICATION attribute on the source Cloud SQL instance. This user will be used by the subscriber to connect.⁸⁸ If using the pglogical extension, the cloudsqlsuperuser role might be needed.⁸⁹
    4. Create a PUBLICATION on the source database, specifying which tables (or all tables) should be replicated.⁸⁸
    5. Ensure the target database in AWS has the same table schemas as the source tables being replicated.⁸⁸
    6. Create a SUBSCRIPTION on the target AWS database, providing the connection details (source IP/hostname, port, dbname, replication user/password) and the name of the publication to subscribe to.⁸⁸
    7. Monitor replication status using views like pg_stat_subscription on the subscriber ⁸⁸ and pg_replication_slots on the publisher.⁹¹ AWS Database Migration Service (DMS) can also be used to manage and monitor this replication process.⁹⁰
  • MySQL Replication: For MySQL migrations, standard MySQL replication mechanisms can be configured between a source (e.g., Cloud SQL for MySQL or self-managed in GCP) and a target replica (e.g., RDS for MySQL or MySQL on EC2/EKS).⁹² AWS DMS is also a common tool for migrating MySQL databases to AWS.⁹⁵ Configuration typically involves:
    1. Setting up a replication user with appropriate privileges (REPLICATION SLAVE, SELECT, SHOW VIEW) on the source database.⁹²
    2. Ensuring binary logging (and ideally GTID mode) is enabled on the source.
    3. Establishing network connectivity (IP allowlisting in GCP/AWS or VPN/Interconnect).⁹²
    4. Obtaining the binary log file name and position (or GTID set) from the source at a consistent point in time (often after an initial data dump/restore).
    5. Configuring the replica instance (in AWS) to connect to the source using the replication user credentials and the correct binary log coordinates (e.g., via CHANGE MASTER TO command or DMS task configuration).⁹²
  • MongoDB Replication: If MongoDB is used, consistency is typically managed via MongoDB’s native replica set functionality. To span GKE and EKS, the MongoDB instances in both clusters need to be configured as members of the same replica set.⁹⁶ This requires:
    1. Network reachability between pods across the GKE and EKS clusters (likely via VPN/Interconnect).
    2. Configuring MongoDB instances with hostnames or IP addresses that are resolvable and reachable from the other cluster members.⁹⁸ Kubernetes services of type LoadBalancer or ExternalName, or headless services combined with DNS configuration, might be used to expose pods across clusters.⁹⁶
    3. Initializing the replica set and adding members from both clusters using rs.add() commands.⁹⁷
    4. The MongoDB Enterprise Kubernetes Operator provides features specifically for managing multi-cluster deployments, simplifying configuration.⁹⁶
• Bidirectional Replication: For scenarios requiring active-active setups or complex rollback strategies, bidirectional replication might be considered.⁹⁹ This allows both the GCP and AWS databases to accept writes and synchronize changes with each other. This is significantly more complex than unidirectional replication and often requires specialized tools or careful application-level conflict resolution logic. It can simplify fallback, as the original source remains up-to-date.⁹⁹
• Network Connectivity: Regardless of the replication method, secure and reliable low-latency network connectivity between the GCP source and AWS target environments is paramount.⁸⁸ Options include Cloud VPN tunnels between GCP and AWS VPCs or potentially higher-bandwidth dedicated connections like Cloud Interconnect paired with AWS Direct Connect.¹⁰⁰ Firewall rules must permit the necessary replication traffic between the database instances.
• Data Encryption: Ensuring data security during replication is crucial. Data should be encrypted both in transit (using SSL/TLS for the database replication connections) and at rest within the databases in both GCP and AWS.⁸³ Both Cloud SQL and RDS provide managed options for enabling SSL/TLS connections and at-rest encryption.

Event-Driven Architectures and Change Data Capture (CDC):

• Concept: An alternative to direct database replication is to adopt an event-driven approach using Change Data Capture (CDC).¹⁰⁵ CDC tools monitor the source database’s transaction logs (e.g., Write-Ahead Logs (WAL) in PostgreSQL, binlog in MySQL) and capture row-level changes (inserts, updates, deletes) as discrete events.¹⁰⁵ These change events are then published to an event streaming platform (like Apache Kafka), and downstream consumers (in this case, a service running in AWS) subscribe to these events and apply the corresponding changes to the target database or system.¹⁰⁷ This pattern promotes loose coupling between the source and target systems.¹⁰⁶
• CDC Tools (Debezium): Debezium is a popular open-source distributed platform specifically designed for CDC.¹⁰⁸ It provides connectors for various databases (including PostgreSQL, MySQL, MongoDB, SQL Server, Oracle) that read the native transaction logs.¹⁰⁸ Debezium typically runs on top of Kafka Connect, publishing change events as structured messages (often JSON or Avro) to Kafka topics.¹⁰⁸
• Event Streaming (Kafka MirrorMaker): If the change events are published to a Kafka cluster in GCP (either self-managed or using Google Cloud Managed Service for Apache Kafka), Kafka’s MirrorMaker 2.0 tool (integrated with Kafka Connect) can be used to replicate these event topics to a Kafka cluster in AWS (e.g., Amazon MSK or self-managed Kafka on EC2/EKS).¹¹⁴ This ensures the change events are available for consumers within the AWS environment. Setting up MirrorMaker involves configuring connection details for source and target clusters, security credentials (SASL/SSL), and specifying which topics and consumer groups to replicate.¹¹⁵
• Eventual Consistency: A key characteristic of event-driven architectures and CDC-based synchronization is eventual consistency.¹⁰⁶ There will inherently be a small delay (latency) between a change occurring in the source database, being captured by CDC, published to Kafka, replicated by MirrorMaker (if used), consumed in AWS, and finally applied to the target database. This latency needs to be acceptable for the application’s consistency requirements.
• Use Cases: CDC and event-driven patterns are well-suited for real-time data synchronization, replicating data to data warehouses or analytics platforms, cache invalidation, triggering downstream workflows based on data changes, and generally decoupling microservices.¹⁰⁵

Dual Write Strategy:

• Concept: This strategy involves modifying the application logic itself so that whenever it performs a write operation, it writes the data simultaneously to both the source database (in GCP) and the target database (in AWS).⁹⁹
• Implementation: This requires significant application code changes and careful handling of potential partial failures (e.g., write succeeds in GCP but fails in AWS). Transaction management across two different databases in different clouds is complex.
• Pros/Cons: If implemented correctly, it can ensure near-real-time consistency in both databases during the transition. However, it is generally the most complex and intrusive approach, increasing application complexity and testing effort substantially.⁹⁹ Fallback is relatively simple: stop writing to the target database.⁹⁹ It might be considered if other replication methods are unsuitable or if a phased, application-level cutover (e.g., migrating specific features or user segments first) is planned.⁹⁹

Choosing the Right Data Synchronization Method: The optimal approach depends heavily on the specific context:

• Database Technology: Native logical replication is often the most straightforward and performant option for homogeneous migrations (e.g., PostgreSQL-to-PostgreSQL, MySQL-to-MySQL) and typically offers stronger consistency guarantees.⁸⁸
• Consistency Requirements: If strict, immediate consistency is required, native replication or potentially dual write (with its complexities) might be necessary. If eventual consistency is acceptable (allowing for minor replication lag), CDC offers more architectural flexibility.¹⁰⁶
• Application Architecture: If the application already utilizes event-driven patterns or if the goal is to decouple systems further, CDC aligns well.¹⁰⁵ If the application is a traditional monolithic or N-tier application directly interacting with a database, native replication might be simpler to integrate.
• Team Expertise: Implementing CDC and Kafka requires skills in those specific technologies, in addition to database and cloud expertise.¹⁰⁸ Native replication primarily requires database administration skills. Dual write requires significant application development effort.⁹⁹
• Migration Duration: For a temporary concurrent operation phase focused solely on migration, the simplicity of native logical replication is often appealing. For longer-term multi-cloud data synchronization or integration with other systems, the flexibility of CDC might be more advantageous.

Given the goal of a temporary concurrent phase for migration validation, native logical replication (for PostgreSQL or MySQL) is generally the recommended starting point due to its relative simplicity and stronger consistency compared to CDC, assuming the source and target database engines are compatible. If MongoDB is used, configuring a cross-cluster replica set is the standard approach. CDC should be considered if native replication is unsuitable or if event-driven integration is a broader architectural goal. Dual write should generally be avoided unless absolutely necessary due to its high implementation complexity.

Table: Comparison of Data Synchronization Methods

Method	Description	Pros	Cons	Consistency Model	Complexity	Use Case Suitability (This Migration)
Native Logical Replication (PostgreSQL/MySQL)	Uses built-in DB features to stream logical changes (rows/transactions) from source to replica.88	Simpler setup for homogeneous DBs, strong/near real-time consistency, less infrastructure overhead.	Limited to specific DB engines/versions, can impact source DB performance, requires network path.	Strong/Near Real-time	Moderate	High (if DBs compatible)
CDC + Event Streaming (Debezium/Kafka)	Captures DB log changes as events, streams via Kafka, consumer applies changes to target.105	Decouples source/target, flexible (heterogeneous targets), scalable event stream, supports various DBs.	More complex infrastructure (CDC tool, Kafka, Consumer), eventual consistency, potential latency.108	Eventual	High	Moderate (if events needed / native unsuitable)
Dual Write (Application Level)	Application code modified to write simultaneously to both source and target databases.99	Near real-time consistency if implemented correctly, rollback can be simpler (stop target writes).	Highly complex application changes, difficult error handling/transaction management, intrusive.99	Strong (if correct)	Very High	Low (generally avoid unless necessary)

⁸⁸

6. Comprehensive Multi-Cloud Monitoring and Observability

Context: Operating services concurrently across both GCP and AWS during the migration necessitates a unified monitoring and observability strategy. Relying on separate, siloed monitoring tools for each cloud environment hinders the ability to effectively compare performance, troubleshoot issues spanning both clouds, and make confident decisions about traffic shifting and final cutover.¹¹⁸

Establishing Unified Visibility (Logs, Metrics, Traces):

• Challenge: The primary challenge is overcoming the default separation where GCP resources (GKE, CloudRun) report to Google Cloud Operations Suite (Cloud Monitoring/Logging) and AWS resources (EKS, ELB, EC2) report to Amazon CloudWatch.¹¹⁸ This creates operational friction and makes holistic analysis difficult.¹²⁰
• Goal: The objective is to aggregate key telemetry data – logs, metrics, and traces – from both the GCP and AWS environments into a single, consolidated platform or “pane of glass”.³⁶ This unified view is crucial for comparing behavior, correlating events, and understanding the end-to-end performance during the migration.
• Observability Pillars: A comprehensive strategy must address the three pillars of observability ¹²³:
  • Metrics: Quantitative measurements collected over time, representing the state and performance of systems (e.g., CPU utilization, memory usage, request latency, error counts, queue lengths).⁶⁵ Essential for trend analysis, alerting, and performance baselining.
  • Logs: Timestamped, unstructured or structured text records detailing events that occurred within applications or infrastructure (e.g., application errors, access logs, system events, audit trails).¹²¹ Critical for debugging, root cause analysis, and security auditing.
  • Traces: Records showing the path and timing of a request as it propagates through various services in a distributed system.⁶⁵ Invaluable for understanding dependencies, identifying bottlenecks, and analyzing latency in microservice architectures.

Tooling Options and Integration:

• Cloud-Native Tools:
  • Google Cloud Operations Suite (Cloud Monitoring/Logging): Provides deep integration and visibility for GCP services like GKE and CloudRun.¹²¹ It can ingest data from external sources, including AWS, typically via agents (like the Ops Agent) installed on AWS resources (EC2 instances/EKS nodes) or potentially through other mechanisms like Pub/Sub forwarding or API integrations.¹²⁵ Google Cloud Monitoring also supports multi-project metrics scopes, allowing a central project to view metrics from other projects, potentially including those dedicated to AWS monitoring data.¹²⁹ Grafana dashboards can also be imported into Cloud Monitoring.¹³¹
  • Amazon CloudWatch: Offers comprehensive monitoring for the AWS ecosystem, including EKS, EC2, ELB, RDS, etc..¹²² It features powerful analysis tools like CloudWatch Logs Insights and Metrics Insights.¹²² Similar to GCP’s suite, it can ingest external data via the CloudWatch Agent installed on GCP resources (GCE VMs/GKE nodes) or other methods.
  • Using Both (Separately): While technically possible, operating with two distinct monitoring platforms creates the data silos and operational inefficiency that a unified approach aims to solve.¹¹⁸ Comparing performance or tracing requests across clouds becomes manual and cumbersome.
• Third-Party Observability Platforms: These platforms are often explicitly designed for heterogeneous environments and provide connectors or agents for both GCP and AWS, facilitating unified monitoring.
  • Grafana: A widely used open-source platform for visualization and analytics.¹²⁶ It excels at connecting to diverse data sources, including Google Cloud Monitoring, Amazon CloudWatch, Prometheus, Jaeger, Loki, and many others.¹³² It can serve as the unified dashboard layer, pulling data from both cloud-native monitoring systems or dedicated time-series databases/log aggregators. Grafana Cloud offers a managed SaaS version.¹³² Implementation requires configuring data source connections and potentially managing the underlying data storage and query engines if using the open-source version.
  • Datadog: A popular SaaS observability platform providing broad integrations across clouds, including deep support for AWS (EKS) and GCP (GKE).¹¹⁹ It uses a unified agent to collect metrics, logs, and traces from hosts, containers, and applications, offering features like infrastructure monitoring, APM, log management, security monitoring, and real-time dashboards in a single interface.¹¹⁹
  • New Relic: Another comprehensive SaaS observability platform with strong integrations for AWS (including an EKS add-on) and GCP (GKE integration).¹³⁹ It provides unified monitoring across infrastructure, applications (APM), logs, and user experience, aiming for full-stack visibility.¹³⁹ Agent-based collection is standard.
  • Dynatrace: An enterprise-focused SaaS platform emphasizing AI-powered observability and automation.¹²⁴ It offers integrations for AWS (EKS via CloudWatch metrics or its OneAgent) and GCP (GKE via Operator/OneAgent or direct GCP integration).¹⁴² Dynatrace’s OneAgent provides automatic discovery and full-stack monitoring, including infrastructure, applications, logs, and user experience data.¹²⁴ It supports monitoring multiple GCP projects.¹³⁰
  • Splunk: Traditionally strong in log aggregation and security information and event management (SIEM), Splunk has expanded its capabilities into broader observability.¹⁴⁶ It can ingest logs and metrics from both GCP and AWS environments, often used for security monitoring and operational intelligence.¹⁴⁶
• Integration Methods: The common pattern for third-party tools involves deploying lightweight agents (e.g., Datadog Agent, Dynatrace OneAgent, New Relic agent, OpenTelemetry Collector configured for a specific backend, Splunk Universal Forwarder) onto the compute instances (GKE nodes, EKS nodes, potentially CloudRun sidecars if feasible).¹¹⁹ These agents collect telemetry locally and forward it to the central platform. Additionally, many platforms offer API-based integrations to pull metrics directly from cloud provider APIs (e.g., querying CloudWatch metrics API or Cloud Monitoring API).¹¹⁹

Monitoring Kubernetes (GKE/EKS) and Service Mesh Telemetry:

• Kubernetes Monitoring: Effective monitoring requires visibility into the Kubernetes orchestration layer itself. Key metrics include cluster-level health, node resource utilization (CPU, memory, disk I/O, network), pod status (running, pending, failed), container restarts, resource requests vs. limits, and deployment health.¹³⁸ Both cloud-native tools (Cloud Monitoring for GKE, CloudWatch for EKS) and third-party platforms provide specialized Kubernetes dashboards and integrations that collect data from the Kubernetes API server and node agents (like kubelet).¹¹⁹
• Service Mesh Observability (if used): If an application-layer service mesh (Istio or Linkerd) is employed (as discussed in Section 4), it becomes a rich source of observability data.⁵⁷ Meshes automatically generate detailed metrics about service-to-service communication (request volume, latency distributions, success/error rates), distributed traces showing request paths across services, and access logs.⁶⁵
  • Istio: Typically integrates with Prometheus for metrics scraping, Jaeger for tracing, Grafana for visualization, and Kiali for mesh topology visualization and configuration.⁶⁵ These components can often be configured to send data to a centralized observability platform.
  • Linkerd: Includes its own control plane components that expose Prometheus metrics and often bundles Grafana dashboards.⁶⁸ It also provides CLI tools (linkerd tap) for real-time request inspection.
• Application Performance Monitoring (APM): To gain insights beyond the infrastructure and mesh layers, instrumenting the application code itself is crucial. APM tools (often part of broader observability platforms like Datadog, Dynatrace, New Relic, or using open standards like OpenTelemetry) use agents or libraries linked into the application to automatically capture detailed transaction traces, database query performance, external API calls, application-level errors, and custom business metrics.¹¹⁹ This provides context that infrastructure metrics alone cannot.

Standardization with OpenTelemetry: Relying solely on vendor-specific agents (e.g., CloudWatch Agent, Datadog Agent, OneAgent) for collecting telemetry can lead to vendor lock-in.¹⁴⁶ If the organization decides to switch monitoring platforms later, significant re-instrumentation effort might be required. OpenTelemetry (OTel) offers a vendor-neutral, open-source framework (comprising APIs, SDKs, and a Collector component) for instrumenting applications and collecting metrics, logs, and traces.¹⁴⁶ Data collected via OTel can be exported to various backend platforms, including cloud-native services (GCP Cloud Monitoring, AWS CloudWatch via ADOT) and third-party tools (Datadog, Dynatrace, New Relic, Splunk, Grafana backends). Adopting OpenTelemetry for application instrumentation and potentially infrastructure data collection (using the OTel Collector) provides greater flexibility and reduces dependency on any single monitoring vendor’s proprietary technology. This involves instrumenting code with OTel SDKs and configuring the OTel Collector to scrape/receive telemetry and export it to the chosen analysis platform(s).

Table: Comparison of Multi-Cloud Monitoring Tools

Tool/Platform	Primary Model	GCP Integration (GKE/Run)	AWS Integration (EKS)	K8s Depth	Service Mesh Int.	APM	Logging	Tracing	Ease of Use	Pricing Model	Key Strengths
Cloud Mon/Log + CloudWatch	Cloud Native	Excellent (GCP Native)	Excellent (AWS Native)	Good	Limited Native	Basic/Ext	Native	Native	Moderate	Usage-Based	Deep integration within respective clouds
Cloud Monitoring (w/ AWS ingest)	Cloud Native	Excellent	Agent/API Based	Good	Limited Native	Basic/Ext	Native	Native	Moderate	Usage-Based	Unified view in GCP console
CloudWatch (w/ GCP ingest)	Cloud Native	Agent/API Based	Excellent	Good	Limited Native	Basic/Ext	Native	Native	Moderate	Usage-Based	Unified view in AWS console, Logs/Metrics Insights
Grafana (Cloud/OSS) + Backends	SaaS / Self-Hosted	Data Source Connector	Data Source Connector	Varies	Via Prometheus/Jaeger	Ext	Via Loki	Via Tempo/Jaeger	Moderate-High	Usage (Cloud)/Free (OSS)	Flexible visualization, connects to many sources 132
Datadog	SaaS	Agent/API Integration	Agent/API Integration	Excellent	Good	Yes	Yes	Yes	High	Usage/Host/Tiered 153	Broad integrations, unified platform 119
New Relic	SaaS	Agent/API Integration	Agent/API Integration	Excellent	Good	Yes	Yes	Yes	High	Usage/Host/Tiered 139	Strong APM, full-stack observability 139
Dynatrace	SaaS	Agent/API Integration	Agent/API Integration	Excellent	Good	Yes	Yes	Yes	High	Host Units/Usage	AI-powered analysis, automation 124

⁶⁵

7. Phased Migration Plan

Context: A structured, phased migration plan is paramount to minimize risk, ensure service continuity, and achieve the zero-downtime requirement.²¹ This plan breaks the complex migration into manageable stages, each with specific objectives, actionable steps, validation criteria, and clearly defined rollback procedures.²⁰

Phase 1: AWS EKS Foundation and Connectivity

• Goal: Establish the target AWS EKS environment and secure, reliable network connectivity between the GCP source environment and the AWS target environment.
• Steps:
  1. Provision AWS Networking: Create the necessary AWS Virtual Private Cloud (VPC), subnets across multiple Availability Zones (for high availability), security groups, and Network ACLs required for the EKS cluster and associated load balancers.¹⁰¹
  2. Provision EKS Cluster: Deploy the AWS EKS cluster(s) in the designated target AWS region(s). Select appropriate EC2 instance types for the worker nodes (considering On-Demand, Spot, and Reserved/Savings Plan options based on workload characteristics and cost strategy – see Section 10) and configure EKS control plane settings.⁸⁰ Use Infrastructure as Code (IaC) tools like Terraform or AWS CloudFormation for repeatable and manageable deployments.³⁶
  3. Establish Cross-Cloud Connectivity: Implement a secure and reliable network link between the GCP VPC hosting the current services and the newly created AWS VPC. Options include:
     • Cloud VPN (HA): Set up High Availability VPN tunnels between GCP Cloud VPN and AWS Site-to-Site VPN gateways. This involves configuring gateways, tunnels, BGP routing (dynamic routing recommended for HA), and firewall rules on both sides.¹⁰⁰
     • Interconnect/Direct Connect: For higher bandwidth, lower latency, and potentially more stable connectivity (often at higher cost), consider using Google Cloud Interconnect (Partner or Dedicated) linked to AWS Direct Connect.¹⁰² This requires physical or partner-mediated connections. Ensure firewall rules (GCP Firewall Rules, AWS Security Groups/NACLs) are configured to permit essential traffic between the environments (e.g., for database replication, health checks if applicable, potential inter-service communication).
  4. Configure AWS IAM: Set up necessary AWS Identity and Access Management (IAM) roles and policies. This includes roles for EKS worker nodes (Node IAM Role), roles for Kubernetes service accounts (using IAM Roles for Service Accounts – IRSA, or EKS Pod Identity) to grant specific AWS permissions to applications running in EKS pods, and roles for administrative access.¹⁴⁷ Adhere to the principle of least privilege.
  5. Configure GCP Workload Identity Federation (Optional): If workloads running in AWS EKS need to securely access GCP APIs (e.g., Cloud Storage, BigQuery) without embedding GCP service account keys, configure GCP Workload Identity Federation. This allows trusted AWS IAM roles to impersonate GCP service accounts.¹⁶⁴
• Validation:
  • Perform network connectivity tests (e.g., ping, traceroute, nc or telnet on required ports) between instances/nodes in GCP and AWS VPCs via the established VPN/Interconnect link.
  • Verify the EKS cluster status is active and worker nodes are registered and healthy.
  • Test IAM permissions by deploying test pods/applications in EKS that attempt to access necessary AWS resources (e.g., S3, RDS) or GCP resources (if Federation is used).
• Rollback: As no production services are running on AWS yet, rollback is straightforward. Decommission the created EKS cluster, VPC, and associated resources in AWS. Remove the VPN/Interconnect configurations on both GCP and AWS. Revert any IAM changes made specifically for this phase.

Phase 2: Application Deployment and Data Synchronization

• Goal: Deploy the target applications onto the EKS cluster and establish the necessary data synchronization mechanisms for stateful services.
• Steps:
  1. Container Image Management: Ensure application container images are built and pushed to a container registry accessible by the EKS cluster (e.g., Amazon Elastic Container Registry – ECR).
  2. Deploy Applications to EKS: Deploy application workloads (Kubernetes Deployments, StatefulSets, DaemonSets, Services, etc.) onto the EKS cluster using their Kubernetes manifests.³⁴ Leverage Helm charts or Kustomize for managing complex deployments. Use IaC for deploying Kubernetes resources consistently.³⁶
  3. Configure AWS Load Balancing: Set up AWS Application Load Balancers (ALBs) or Network Load Balancers (NLBs) and Kubernetes Ingress resources (e.g., AWS Load Balancer Controller) to expose services running on EKS externally or internally as needed.⁸ Record the public DNS name or IP address of the primary load balancer that will receive external traffic – this will be used in the DNS WRR policy.
  4. Initiate Data Synchronization: For all identified stateful applications, configure and start the chosen data synchronization method (detailed in Section 5) between the current primary data store (likely in GCP) and the corresponding data store in AWS (e.g., configure Cloud SQL to RDS logical replication, deploy Debezium/Kafka Connect for CDC, or set up MongoDB cross-cluster replication).⁸⁴
  5. Initial Data Seeding: Allow sufficient time for the initial synchronization or seeding of data from the GCP source to the AWS target to complete. Monitor replication lag closely.
• Validation:
  • Verify that all application pods are running successfully in EKS (kubectl get pods).
  • Confirm that AWS load balancers are correctly routing traffic to the backend pods within the EKS cluster (test using internal cluster access or the LB’s direct DNS name).
  • Monitor the data replication process. Check replication lag metrics and perform data spot checks or checksums to ensure consistency between the source (GCP) and target (AWS) data stores.⁸⁸
  • Conduct preliminary functional tests against the application endpoints exposed by the AWS load balancer (without shifting production DNS yet) to catch basic deployment or configuration issues.
• Rollback: If issues arise (e.g., deployment failures, data sync errors): Stop and remove the data synchronization configuration on both ends. Delete the application deployments, services, and load balancers from the EKS cluster. Decommission any newly created database replicas or data stores in AWS. Rollback remains relatively low-risk as production traffic is unaffected.

Phase 3: Traffic Splitting Implementation and Monitoring Activation

• Goal: Configure the DNS-based traffic splitting mechanism (WRR) and activate the unified monitoring system across both GCP and AWS environments.
• Steps:
  1. Configure DNS WRR Policy: In Google Cloud DNS, modify the existing A/CNAME record for the production service hostname or create a new one using a Weighted Round Robin (WRR) policy.
     • Add the IP address(es) of the primary GCP endpoint(s) (CloudRun/GKE LB) with an initial weight representing 100% (e.g., weight 1000).
     • Add the external IP address(es) of the AWS EKS load balancer (obtained in Phase 2) with an initial weight of 0.0.¹
  2. Configure DNS Health Checks: Create and associate a Google Cloud DNS health check specifically for the external AWS ELB IP address(es) added to the WRR policy, as detailed in Section 2.¹ Ensure the corresponding AWS security groups allow traffic from GCP health checkers on the specified port.¹
  3. Lower DNS TTLs: At least 24 hours (or the duration of the previous TTL) before planning to shift any traffic, lower the TTL for the DNS record(s) being managed by the WRR policy to a short value (e.g., 60-300 seconds).¹⁷ Verify the change propagates using tools like dig.
  4. Activate Unified Monitoring: Deploy agents, configure integrations, and ensure the chosen unified observability platform (from Section 6) is actively collecting and displaying metrics, logs, and traces from both the GCP (CloudRun/GKE) and AWS (EKS) environments.¹¹⁹
  5. Establish Baselines and Alerts: Configure key dashboards comparing performance metrics (latency, error rates, saturation) side-by-side for the service running in GCP vs. AWS. Set up critical alerts for the AWS environment (e.g., high error rates, high latency, unhealthy pods, high replication lag).
• Validation:
  • Verify the WRR record set configuration in Google Cloud DNS is accurate, with both GCP and AWS endpoints listed (AWS weight initially 0).
  • Confirm the external health check associated with the AWS endpoint is consistently reporting a healthy status in the GCP console.
  • Use dig or online DNS propagation checkers to confirm the low TTL is active for the relevant hostname.
  • Validate that telemetry (metrics, logs, traces) from both GCP and AWS deployments is visible and correctly tagged/identified within the unified monitoring platform. Test a basic alert condition for the AWS environment.
• Rollback: Revert the DNS record to its original state (non-WRR or WRR with only GCP endpoints). Allow the low TTL to expire, then increase the TTL back to its previous value. Disable and remove monitoring agents/configurations from the AWS environment if necessary.

Phase 4: Incremental Traffic Shift and Stability Validation

• Goal: Gradually introduce live production traffic to the AWS EKS environment while closely monitoring its stability, performance, and impact on data consistency. This is the core phase for de-risking the migration.¹⁵⁸
• Steps:
  1. Initial Traffic Shift: Modify the Google Cloud DNS WRR policy weights to direct a small percentage of traffic (e.g., 5%, 10%, or even 1%) to the AWS EKS endpoint.¹ For example, set GCP weight to 950 and AWS weight to 50.
  2. Intensive Monitoring: Immediately and continuously monitor the unified observability platform.¹¹⁹ Pay close attention to:
     • Application Metrics: Compare latency (average, p95, p99), error rates (HTTP 5xx, 4xx), and throughput for requests served by AWS vs. GCP.
     • Resource Metrics: Monitor CPU, memory, disk, and network utilization on EKS nodes and pods. Check load balancer metrics (healthy hosts, connection counts).
     • Logs: Analyze application and system logs from the EKS environment for any new or increased errors.
     • Data Consistency: Monitor database replication lag and perform checks to ensure data integrity is maintained with writes potentially happening via both paths (depending on application logic and read/write splitting, if any).
  3. Issue Resolution: If any significant performance degradation, increase in errors, or data consistency issues are observed in the AWS environment, immediately investigate and resolve them. Roll back traffic (Step 5) if necessary.
  4. Incremental Increases: If the AWS environment proves stable at the current traffic percentage for a predefined observation period (e.g., several hours, a day), incrementally increase the weight shifted to AWS (e.g., to 25%, then 50%, 75%, 90%).¹⁵⁸ Repeat the intensive monitoring and validation process at each step. The duration of each step depends on traffic volume, application criticality, and risk tolerance.
  5. Testing: As more traffic flows to AWS, perform more comprehensive functional testing, integration testing, and potentially targeted User Acceptance Testing (UAT) against the AWS deployment to ensure all features work as expected under load.
• Validation: At each increment, confirm that the AWS environment meets or exceeds the performance and stability benchmarks set by the GCP environment. Error rates must remain within acceptable thresholds. Data consistency must be maintained. No critical bugs or user-impacting issues should be attributable to the AWS deployment.
• Rollback (Phased): The primary rollback mechanism during this phase is to quickly adjust the DNS WRR weights back to 0% for AWS (or to the last known stable percentage).²⁰ Due to the low TTLs, this should redirect traffic away from the problematic AWS environment rapidly. Once traffic is shifted back, the team can diagnose and fix the issue in AWS without impacting production users. For data corruption issues, more complex database rollback procedures might be needed, potentially involving restoring the AWS database from a recent backup or snapshot and re-establishing replication.⁹⁹

Phase 5: Final DNS Cutover to AWS EKS

• Goal: Transition 100% of production traffic to the AWS EKS environment and begin the process of decommissioning the legacy GCP environment.
• Steps:
  1. Final Validation: Conduct a final review of the AWS environment’s health and performance while handling a significant majority (e.g., 90% or more) of the production load. Ensure all monitoring metrics are stable and within acceptable ranges. Confirm data replication is caught up and consistent.
  2. Schedule Cutover: Announce and schedule a brief cutover window. While the DNS change itself should be quick due to low TTLs, scheduling provides a clear point for the final action.
  3. Execute 100% Traffic Shift: Modify the Google Cloud DNS WRR policy weights one last time: set the weight for the AWS EKS endpoint(s) to maximum (e.g., 1000) and the weight for the GCP endpoint(s) to 0.0.¹
  4. Monitor Post-Cutover: Monitor the system intensely immediately following the DNS change. Verify using monitoring tools and potentially external DNS checkers that all traffic is now being served by the AWS EKS environment. Watch closely for any emergent issues under full load.
  5. Hot Standby Period: Keep the GCP application environment (CloudRun/GKE services, databases configured as replicas if applicable) running and ready as a hot standby for a predetermined period (e.g., 24-72 hours). This provides a rapid rollback path if unforeseen critical issues arise on AWS post-cutover.
  6. Begin GCP Decommissioning: After the standby period expires and confidence in the AWS environment is high:
     • Stop and delete the application deployments and services in CloudRun and GKE.
     • Stop the data replication/synchronization process from GCP to AWS. If AWS is now the primary data source, reconfigure replication if necessary (e.g., AWS primary to GCP replica for fallback).
     • Decommission associated GCP resources like load balancers, unused persistent disks, etc.
  7. Increase DNS TTLs: Once the GCP environment is decommissioned and the AWS environment is stable, increase the DNS TTLs for the migrated service hostnames back to standard, higher values (e.g., 1 hour or 24 hours).¹⁷
• Validation: Confirm monitoring shows 100% traffic routed to AWS. Verify application stability and performance under full load over the standby period. Successfully decommission GCP application resources. Confirm TTLs have been increased.
• Rollback (Post-Cutover / During Standby): If a critical failure occurs on AWS during the hot standby period, the rollback involves quickly reverting the DNS WRR weights back to 100% GCP / 0% AWS.²⁰ This requires the GCP environment and data synchronization (or a very recent snapshot/replica state) to be fully intact and ready to take over traffic immediately. This becomes significantly harder or impossible once the GCP resources are decommissioned.

Rollback Strategy Considerations:

• Automation: Use scripts (gcloud dns record-sets update) or IaC tools (Terraform apply) to automate the DNS weight changes for both forward progression and rollback scenarios to ensure speed and consistency.
• Data Rollback Planning: The database rollback strategy is critical and depends heavily on the chosen synchronization method and the nature of the failure. Options might include: promoting the original GCP primary (if still receiving replicated writes from AWS via bidirectional sync or fall-forward), restoring the GCP primary from a backup taken just before cutover and replaying logs if possible, or failing over to a dedicated fall-forward replica.⁹⁹ These procedures must be documented and tested beforehand.
• Decision Criteria: Establish clear, objective criteria (e.g., error rate threshold exceeded for X minutes, critical functionality failure confirmed by Y users, data corruption detected) that trigger a rollback decision at each phase.
• Communication Plan: Maintain a clear communication plan for the migration team and stakeholders, especially during traffic shifts and in the event of a rollback.

8. Final Zero-Downtime DNS Cutover Process

Context: This section provides a focused checklist and execution steps specifically for the final action within Phase 5: adjusting the DNS configuration to direct 100% of user traffic to the now-validated AWS EKS environment, while ensuring no interruption for end-users.

Pre-Cutover Verification Checklist: Before executing the final DNS change, rigorously verify the following conditions are met:

• AWS Stability Under Load: The AWS EKS environment has been stable and performing correctly while handling a significant percentage (e.g., >90%) of live production traffic for a sustained period (e.g., 24+ hours, depending on risk assessment).
• Performance Metrics: Key performance indicators (KPIs) such as request latency (average, p95, p99), error rates (HTTP 5xx, application-specific errors), and throughput in the AWS environment are within acceptable, predefined limits and comparable or better than the legacy GCP environment.
• Data Consistency: Data synchronization mechanisms have been continuously monitored, replication lag is minimal, and data integrity checks confirm consistency between stateful stores in GCP (if still active source) and AWS.
• Monitoring & Alerting: The unified observability platform provides full visibility into the AWS environment, and critical alerts are configured and functioning correctly. No outstanding critical alerts related to the AWS deployment.
• Rollback Plan Readiness: The documented rollback procedure (reverting DNS weights, potentially database actions) is understood by the team, and the necessary components (e.g., GCP hot standby) are confirmed to be ready.
• Low DNS TTL Confirmed: Verify using external tools (dig, online checkers) that the current TTL for the service hostname’s DNS record is still set to the low value (e.g., 60-300 seconds) used during the traffic shifting phase.
• GCP Hot Standby: The legacy GCP environment (CloudRun/GKE services) is still running and capable of taking traffic immediately if a rollback is required.

Execution Steps (DNS WRR Adjustment): The final cutover is achieved by adjusting the weights in the existing Google Cloud DNS WRR policy.

1. Access DNS Configuration: Log in to the Google Cloud Console and navigate to Cloud DNS, or prepare the necessary gcloud command-line interface commands.
2. Identify Record Set: Locate the managed zone and the specific WRR resource record set (e.g., the A record for service.yourdomain.com) that manages traffic for the service being migrated.
3. Modify WRR Weights: Edit the routing policy configuration for the record set:
   • Find the entry corresponding to the AWS EKS Load Balancer IP address(es). Update its weight to the maximum value or the value representing 100% of the traffic (e.g., set weight to 1000.0 or .1 if using fractional representation and it’s the only active endpoint).⁴
   • Find the entry(ies) corresponding to the legacy GCP endpoint IP address(es) (CloudRun/GKE LB). Update its/their weight(s) to 0.0.²
4. Save Changes: Apply and save the updated DNS record set configuration.

Post-Cutover Monitoring and Validation:

1. Immediate Monitoring: As soon as the DNS change is saved, begin intensive monitoring using the unified observability platform.
   • Track traffic distribution metrics to confirm that requests previously hitting GCP endpoints rapidly decrease to zero, and requests hitting AWS endpoints increase to handle 100% of the traffic.
   • Use external DNS propagation checking tools (e.g., whatsmydns.net) to observe the change propagating across global resolvers (this should happen quickly due to the low TTL).
2. Health and Performance Checks: Continue to closely monitor application health dashboards, error rates, latency metrics, and resource utilization on the AWS EKS environment to ensure it handles the full production load gracefully.
3. Maintain Hot Standby: Keep the GCP environment operational as per the agreed hot standby period (e.g., 24-72 hours).
4. Proceed with Decommissioning: After the standby period passes without issues, proceed with the planned decommissioning of GCP resources and the subsequent increase of DNS TTLs as detailed in Phase 5 of the migration plan (Section 7).

9. Comparative Analysis of Traffic Management Approaches

Context: The migration strategy involves choosing how to direct user traffic between the GCP and AWS environments. Three primary technical approaches were discussed: DNS-level management using Google Cloud DNS, dedicated Global Server Load Balancing (GSLB) services, and Application-Layer control via API Gateways or Service Meshes. This section provides a comparative analysis of these approaches specifically tailored to the requirements of this gradual, zero-downtime migration from GCP to AWS EKS, using GCP DNS as the starting point.

Evaluation Criteria: The approaches are evaluated based on the following criteria:

• Complexity: The effort required for initial setup, configuration, and ongoing management during the migration phase.
• Cost: Associated costs, including service fees, infrastructure resource consumption, and potential data transfer charges.
• Control Granularity: The level of precision offered in directing traffic (e.g., simple percentages vs. routing based on request attributes like headers or user identity).
• Performance Impact: Potential effects on end-user latency, considering factors like additional network hops or reliance on specific network infrastructure.
• Zero-Downtime Suitability: How effectively the approach supports gradual, percentage-based traffic shifting and seamless final cutover with minimal user impact.

DNS-Level (Google Cloud DNS WRR + Health Checks):

• Pros:
  • Simplicity: Leverages the existing authoritative DNS provider (Google Cloud DNS), requiring configuration changes within a familiar platform rather than introducing new systems.¹ Setup involves creating/modifying record sets and health checks.
  • Direct Traffic Path: Clients resolve the DNS name to either a GCP or AWS IP and connect directly, avoiding intermediate proxies (unless introduced at the GCP/AWS LB level).⁵
  • Effective Percentage Splitting: WRR is designed specifically for distributing traffic based on defined proportions.¹
  • Low Inherent Cost: Utilizes existing Cloud DNS service; costs are primarily driven by query volume and potentially health checks, which are generally modest compared to dedicated GSLB/mesh solutions.
• Cons:
  • Basic Health Checks: DNS health checks monitor endpoint reachability (IP/port) but lack deep application-level insight compared to GSLB/App Layer checks.¹ Resilience relies heavily on the health checks within the target environment (AWS ELB/EKS).
  • DNS Propagation Dependency: Relies on DNS TTLs for changes to take effect globally. While low TTLs mitigate this, some residual caching or resolver behavior issues can occur.¹⁷
  • Limited Granularity: Traffic splitting is based solely on weights assigned to IP addresses; routing based on L7 attributes (path, header, user) is not possible at the DNS layer.⁴⁴
• Suitability: High. This approach directly addresses the core requirement of gradual, percentage-based traffic shifting using the mandated GCP DNS infrastructure. Its relative simplicity makes it well-suited for the defined migration process, especially if the concurrent operation phase is temporary.

Global Server Load Balancing (GSLB – e.g., Cloudflare, F5):

• Pros:
  • Advanced Health Checking: Often provides more sophisticated health checks, potentially including application-level validation.⁵
  • Faster Failover: May detect failures and reroute traffic more quickly than DNS-based methods.²⁸
  • Richer Routing Policies: Supports advanced routing logic like latency-based or finer-grained geolocation routing.¹³
  • Integrated Features: Often bundled with CDN, WAF, DDoS protection features.²⁹
• Cons:
  • Increased Complexity: Introduces a new platform/vendor to manage, requiring configuration and integration effort.⁴²
  • Higher Cost: Typically involves subscription fees or usage-based costs significantly higher than basic DNS.²⁹
  • Potential Performance Impact: If the GSLB acts as a proxy (like Cloudflare), it adds an extra hop for data traffic, potentially impacting latency.⁵
  • DNS Changes Required: Requires changing the domain’s NS records to point to the GSLB provider or delegating subdomains, moving away from direct GCP DNS authority.
• Suitability: Moderate. While capable, the added complexity and cost may not be justified solely for this migration’s gradual shift requirement if Cloud DNS WRR suffices. More relevant for long-term multi-cloud strategies needing advanced routing or faster failover.

Application-Layer (API Gateway / Service Mesh):

• Pros:
  • Highest Granularity: Enables traffic splitting based on fine-grained L7 attributes like request path, headers, cookies, user identity, etc..³³ Allows for canary releases targeting specific user groups or features.
  • Integrated L7 Features: Gateways handle API management tasks (auth, rate limiting); meshes handle inter-service concerns (mTLS, retries, circuit breaking).⁴³
• Cons:
  • Highest Complexity: Requires deploying, configuring, and managing the gateway or service mesh infrastructure across both GCP and AWS environments.⁴⁵ Multi-cluster mesh setup is particularly complex.⁶⁶
  • Operational Overhead: Significant ongoing effort for maintenance, upgrades, and troubleshooting of the mesh/gateway components.
  • Performance Overhead: Introduces additional proxy hops (sidecars for mesh, gateway itself) which adds latency to requests.⁴⁴
  • Resource Cost: Consumes additional compute and memory resources for the gateway/mesh components.
• Suitability: Low to Moderate. The complexity is likely excessive for the primary goal of percentage-based traffic shifting during a temporary migration phase. It becomes more relevant if the migration requires intricate L7 routing decisions or if a service mesh is already planned as part of the target EKS architecture for other reasons.

Layered Approach Recommendation: The most pragmatic approach for this migration involves layering traffic management and resilience:

1. Primary Traffic Splitting: Use Google Cloud DNS WRR as the primary mechanism to gradually shift traffic percentages between the GCP and AWS endpoints. Manage TTLs carefully.
2. Endpoint Health (DNS Layer): Implement Google Cloud DNS Health Checks targeting the AWS Load Balancer IP(s) as a first line of defense against major endpoint failures or network reachability issues.¹
3. Endpoint Health (Load Balancer Layer): Configure robust AWS ELB Health Checks targeting the individual EKS pods/instances. This provides finer-grained health detection within the AWS environment.⁸
4. Endpoint Health (Application Layer): Implement proper Kubernetes liveness and readiness probes within the EKS pods to ensure the application instances themselves are healthy and ready to serve traffic.

This layered strategy leverages the simplicity and existing infrastructure of Google Cloud DNS for the core migration task while incorporating necessary health checks at multiple levels for resilience, without introducing the significant complexity of a full GSLB or multi-cloud service mesh solely for the migration period.

Table: Comparison of Traffic Management Approaches for Migration

Approach	Complexity (Setup/Mgmt)	Cost	Control Granularity	Performance Impact	Zero-Downtime Suitability (Gradual Shift)
Google Cloud DNS (WRR + Health Checks)	Low	Low	Basic (IP Weights)	Minimal (Direct Path)	High
Dedicated GSLB (e.g., Cloudflare)	Moderate-High	Moderate-High	High (Geo, Latency, etc.)	Potential (Proxy Hop)	High
Application Layer (API GW/Service Mesh)	Very High	High	Very High (L7 Attributes)	Moderate (Proxy Hops)	High (but complex)

¹

10. Operational Considerations and Best Practices

Context: Successfully executing this multi-cloud migration and managing the temporary hybrid state requires careful consideration of operational aspects beyond the core technical implementation. This includes managing complexity, ensuring consistent security, optimizing costs, and verifying the team possesses the necessary skills.

Managing Multi-Cloud Complexity:

• Challenges: Operating across GCP and AWS simultaneously introduces significant complexity. Each platform has distinct APIs, management consoles, resource models, networking constructs, and operational paradigms.¹²⁰ Maintaining configuration consistency, ensuring interoperability, and avoiding operational silos requires deliberate effort.¹²⁰
• Strategies:
  • Infrastructure as Code (IaC): Employ tools like Terraform to define and manage infrastructure resources (VPCs, subnets, EKS clusters, GKE node pools, load balancers, DNS records, firewall rules) in both GCP and AWS.³⁶ This promotes consistency, repeatability, and version control for infrastructure changes across clouds.
  • Centralized Management Tools: While potentially overkill for a purely temporary migration phase, evaluate if multi-cloud management platforms (offered by AWS ¹⁷⁰, GCP via Anthos ⁷⁹, or third parties ¹¹⁸) could simplify tasks like unified inventory, compliance checking, or automation if the hybrid state persists longer than anticipated. AWS Systems Manager and AWS Config, for example, offer some capabilities to manage and audit resources beyond AWS.¹⁷⁰
  • Unified Monitoring: As emphasized in Section 6, implementing a single pane of glass for monitoring logs, metrics, and traces across both environments is non-negotiable for effective operation during the transition.³⁶
  • Standardized Naming Conventions: Establish and enforce consistent naming conventions for resources (VMs, clusters, load balancers, databases, namespaces, etc.) across both cloud environments to improve clarity, simplify automation, and aid troubleshooting.³⁶

Security Posture:

• Identity and Access Management (IAM): Managing identities and permissions securely across two clouds is critical.
  • Federated Identity: The recommended approach is to use a central Identity Provider (IdP) such as Okta, Azure AD (Entra ID), Google Workspace, or Active Directory. Federate this IdP with both AWS IAM Identity Center (formerly AWS SSO) and Google Cloud IAM using standard protocols like SAML 2.0 or OpenID Connect.¹⁴⁷ This enables centralized user lifecycle management and provides Single Sign-On (SSO) for administrators and potentially applications accessing resources in both clouds.
  • Cross-Cloud Permissions: Define granular roles and permissions in both AWS IAM and GCP IAM based on the principle of least privilege.¹⁴⁷ For EKS workloads needing to interact with AWS APIs, use IAM Roles for Service Accounts (IRSA) or EKS Pod Identities.¹⁶¹ For AWS workloads needing to access GCP resources without exporting GCP service account keys, configure GCP Workload Identity Federation, mapping specific AWS IAM roles to GCP service accounts.¹⁶⁴
  • Dedicated Service Accounts/Roles: Avoid using default or overly broad service accounts/roles. Create dedicated identities with specific, minimal permissions for each application component or automation task in both clouds.¹⁶⁴
• Network Security:
  • Secure Connectivity: Ensure the VPN or Interconnect link established between GCP and AWS is properly secured, typically using IPsec encryption for VPNs.¹⁰²
  • Firewall Rules: Meticulously configure GCP Firewall Rules and AWS Security Groups/Network ACLs. Allow only the specific protocols, ports, and source/destination IP ranges required for essential communication, such as database replication traffic, health checks from GCP to AWS, and any necessary application-level communication between services running in different clouds.⁷ Deny all other traffic by default.
• Policy Enforcement: For consistent security configurations within the Kubernetes clusters (GKE and EKS), consider policy-as-code tools. Open Policy Agent (OPA) with Gatekeeper is a common standard.¹⁷³ Cloud providers also offer integrated solutions (e.g., Azure Policy for Kubernetes, potentially leveraging GCP Config Controller). These tools can enforce policies like requiring specific labels, restricting container image sources, mandating resource limits, or preventing the creation of insecure configurations.¹⁷³ If a service mesh like Istio is used, its AuthorizationPolicy resources can enforce fine-grained L7 access control between services.⁵⁸
• Secrets Management: Utilize managed secrets management services like GCP Secret Manager and AWS Secrets Manager to securely store and inject sensitive information (API keys, database passwords, certificates) into applications running in GKE and EKS. Avoid storing secrets directly in code or configuration files. Tools like HashiCorp Vault can also provide a cloud-agnostic solution.

Cost Optimization Strategies:

• Egress Costs: Data transfer out of a cloud provider (egress) is a significant cost factor in multi-cloud scenarios.¹⁷⁵ Be particularly mindful of:
  • GCP to AWS Traffic: Data transferred for database replication, potential cross-cloud API calls, or monitoring data sent from GCP to an AWS-hosted platform will incur GCP egress charges. GCP egress pricing varies by destination region and volume, with transfers to other continents generally being more expensive.¹⁰
  • Inter-AZ/Region Traffic: Even within AWS, data transfer between Availability Zones or Regions incurs costs.¹⁷⁷ Design EKS deployments and replication strategies to minimize unnecessary cross-AZ traffic where possible.
  • Estimation: Estimate the volume of data expected to traverse the GCP-AWS link for replication and other needs to budget for egress costs. Use cloud provider pricing calculators.¹⁰
• Compute Savings (AWS EKS): Since EKS worker nodes run on EC2 instances, optimizing EC2 costs is crucial.
  • Right-Sizing Instances: Analyze the actual CPU and memory requirements of the workloads migrated to EKS using monitoring data (Section 6). Select EC2 instance types and sizes that match these requirements closely to avoid paying for unused capacity.¹⁷⁸ Tools like Kubecost or the open-source Goldilocks can help identify optimal resource requests/limits for pods, which informs node sizing.¹⁶⁰
  • Effective Autoscaling: Implement both the Kubernetes Horizontal Pod Autoscaler (HPA) to scale the number of application pods based on metrics (CPU, memory, custom metrics) and the Cluster Autoscaler (or Karpenter) to automatically adjust the number of EKS worker nodes in the node group(s) based on pod scheduling demands.¹⁶⁰ This ensures resources scale up to meet demand and scale down to save costs during idle periods.
  • Leveraging Spot Instances: For workloads on EKS that can tolerate interruptions (e.g., stateless applications, batch jobs, some parts of web applications), use EC2 Spot Instances for worker nodes.¹⁶⁰ Spot Instances offer discounts of up to 90% compared to On-Demand prices but can be reclaimed by AWS with short notice.¹⁶⁰ Best practice often involves using mixed-instance node groups combining Spot with On-Demand or Reserved Instances/Savings Plans, allowing the Cluster Autoscaler/Karpenter to utilize Spot when available and fall back to other types if Spot capacity is interrupted.¹⁷⁸
  • Reserved Instances (RIs) / Savings Plans (SPs): For baseline, predictable workloads running continuously on EKS nodes, commit to AWS Reserved Instances or Savings Plans.¹⁶⁰ RIs offer discounts for committing to a specific instance type in a specific AZ for 1 or 3 years. Savings Plans (Compute or EC2 Instance) offer similar or better discounts (up to 72%) with more flexibility – they apply to usage across instance families, regions, or compute services (like Fargate) based on an hourly spend commitment for 1 or 3 years.¹⁶⁰ Analyze workload stability and predictability to determine the appropriate commitment level.
  • AWS Graviton Instances: Evaluate using AWS Graviton (ARM-based) processors for EKS worker nodes.¹⁶⁰ For many workloads, Graviton instances offer significantly better price-performance compared to equivalent x86 instances, leading to cost savings. Ensure application compatibility with the ARM architecture.
• Cost Monitoring and Allocation: Utilize cloud provider billing tools (AWS Cost Explorer, GCP Billing reports) and potentially specialized third-party multi-cloud cost management platforms (e.g., CloudZero, ProsperOps, Kubecost, CAST AI, Vantage, CloudBolt, Cloudability, Tanzu CloudHealth).¹¹⁸ Implement a consistent resource tagging strategy across both clouds to allocate costs accurately to teams, projects, or environments.¹⁷¹ Regularly review spending patterns and optimization recommendations provided by these tools.

Table: Comparison of AWS Compute Cost Optimization Strategies for EKS Nodes

Strategy	Description	Best For	Savings Potential	Flexibility	Risk/Considerations
On-Demand Instances	Pay-as-you-go per second/hour pricing.	Unpredictable, short-term workloads; testing/dev.	None (Baseline)	High (Start/stop anytime)	Highest cost for sustained usage.
Spot Instances	Bid on spare EC2 capacity; significant discounts but instances can be interrupted.160	Fault-tolerant, stateless, flexible workloads (batch, CI/CD, some web apps).	Up to 90% vs On-Demand.178	Moderate (Can be interrupted)	High (Requires interruption handling; not for critical stateful apps).
Reserved Instances (RI)	Commit to specific instance type/region/OS for 1 or 3 years.160	Stable, predictable, long-running workloads (baseline capacity).	Up to 72% vs On-Demand.178	Low (Standard RI) to Moderate (Convertible RI)	Lock-in risk if workload changes; requires upfront planning.
Savings Plans (SP)	Commit to hourly $ spend on compute for 1 or 3 years; applies across instance families/regions.178	Predictable baseline usage with need for flexibility across instance types/regions.	Up to 72% vs On-Demand.178	High (Applies broadly)	Requires accurate usage forecasting for commitment.

¹⁶⁰

Required Team Skills and Expertise: This migration spans two major cloud providers and involves complex technologies. The success of the project heavily depends on the team possessing the right skills.¹²⁰ A skills gap represents a significant risk.¹⁸¹

• Multi-Cloud Proficiency: Deep, hands-on experience with both GCP and AWS is essential. This includes understanding their respective networking (VPC, VPN, DNS), compute (GKE, CloudRun, EKS, EC2), storage, database (Cloud SQL, RDS), and IAM services.¹²⁰
• Kubernetes Expertise: Advanced knowledge of Kubernetes architecture, operations, deployment strategies (YAML, Helm, Kustomize), networking (Services, Ingress, CNI), storage (PersistentVolumes), security (RBAC, NetworkPolicy), and troubleshooting is required for both GKE and EKS environments.¹⁵⁹ Understanding the specific nuances and managed features of GKE and EKS is crucial.
• Networking: Strong skills in cloud networking, including VPC design, subnetting, routing, firewall configuration (GCP Firewall, AWS Security Groups/NACLs), load balancing (GCP LB, AWS ELB), and setting up/troubleshooting cross-cloud connectivity (VPN/Interconnect).¹⁰⁰ DNS expertise, particularly with Google Cloud DNS WRR and health checks, is vital.
• Data Management: Proficiency in administering the specific database technologies being used (PostgreSQL, MySQL, MongoDB). Experience setting up and managing cross-database or cross-cloud replication (logical replication, native replication, replica sets) is critical for stateful applications.⁸⁸ Familiarity with CDC tools (Debezium) and event streaming (Kafka) may be needed depending on the chosen strategy.
• Observability & Monitoring: Experience configuring, managing, and interpreting data from observability platforms (whether cloud-native or third-party). Skills in setting up agents, creating dashboards, defining effective alerts, and analyzing metrics, logs, and traces for troubleshooting.¹¹⁹
• Security: Solid understanding of cloud security principles, IAM configuration (including federation), network security best practices, secrets management, and potentially policy-as-code tools (OPA) in both GCP and AWS contexts.¹⁴⁷
• IaC & Automation: Proficiency in using Infrastructure as Code tools (Terraform preferred for multi-cloud) to automate the provisioning and management of resources in both clouds and potentially Kubernetes configurations.³⁶ Experience with CI/CD pipelines for deploying applications to Kubernetes.

Given the complexity and breadth of skills required, conducting a thorough skills assessment of the migration team early in the planning phase is highly recommended. Identifying and addressing any gaps through training, hiring, or engaging external consultants or partners with proven multi-cloud migration expertise can significantly mitigate project risks.¹⁵⁴

11. Conclusion and Recommendations

Summary: This report has outlined a technical strategy for migrating services from Google Cloud (CloudRun/GKE) to AWS EKS while adhering to the critical requirements of a gradual transition, concurrent operation, and zero downtime for end-users. The recommended core approach leverages the existing Google Cloud DNS infrastructure, utilizing Weighted Round Robin (WRR) policies combined with external health checks for incremental traffic shifting. This DNS-level control is layered with robust health checking within the AWS environment (ELB, EKS probes). For stateful applications, native database logical replication (or equivalent mechanisms like MongoDB replica sets) is generally preferred for maintaining data consistency during the transition, chosen based on the specific database technology. A unified multi-cloud observability platform is deemed essential for monitoring performance and stability across both environments throughout the migration phases. The strategy emphasizes a detailed, phased migration plan with clear validation checkpoints and rollback procedures at each stage.

Key Recommendations:

1. Prioritize Planning and Testing: Thoroughly assess application dependencies, particularly for stateful components. Meticulously plan each migration phase, defining clear goals, steps, validation criteria, and rollback triggers. Rigorously test configurations, data synchronization, and rollback procedures in pre-production environments before applying them to production.
2. Use DNS WRR for Initial Traffic Splitting: Leverage Google Cloud DNS WRR policies with external health checks as the primary mechanism for gradual traffic distribution between GCP and AWS. This approach utilizes existing infrastructure and offers sufficient control for percentage-based shifts with manageable complexity.¹
3. Implement Layered Health Checks: Configure health checks at multiple levels: Google Cloud DNS checks targeting the AWS ELB ¹, AWS ELB health checks targeting EKS pods ⁸, and Kubernetes liveness/readiness probes within the pods.
4. Validate Data Synchronization Method: Select the most appropriate data consistency strategy (likely native logical replication) based on database type, consistency needs, and team expertise. Thoroughly test the chosen method to ensure minimal lag and data integrity before shifting any production traffic involving stateful services.⁸⁸
5. Adopt Unified Observability: Implement a single observability platform (e.g., Datadog, Dynatrace, New Relic, or a well-configured Grafana stack) capable of ingesting and correlating metrics, logs, and traces from both GCP (GKE/CloudRun) and AWS (EKS) environments.¹¹⁸ This is critical for monitoring during the concurrent phase.
6. Manage DNS TTLs Proactively: Lower DNS TTLs for service hostnames significantly (e.g., 60-300 seconds) well in advance (>= previous TTL duration) of any traffic shifting.¹⁷ Increase TTLs back to standard values only after the migration is complete and stable.
7. Define and Test Rollback Procedures: Document clear, actionable rollback plans for each migration phase, including DNS reversions and database state management.²⁰ Test these procedures where feasible.
8. Address Operational Aspects: Implement robust security measures, including IAM federation and least-privilege access across clouds.¹⁶³ Plan for and monitor cross-cloud data egress costs.¹⁰ Optimize EKS compute costs using right-sizing, autoscaling, and appropriate purchasing options (Spot, RIs/SPs).¹⁶⁰ Critically assess team skills across GCP, AWS, Kubernetes, networking, data management, and observability, addressing gaps proactively.¹²⁰
9. Avoid Unnecessary Complexity: For the migration itself, resist introducing complex GSLB solutions or multi-cloud service meshes unless their advanced features are strictly necessary for the transition or are part of the confirmed long-term architecture. Start with the simplest effective approach (DNS WRR) and add complexity only if required.

Next Steps: The immediate actions recommended are:

Initiate Phase 1 of the migration plan: provisioning the foundational AWS EKS environment and establishing secure cross-cloud network connectivity.

Conduct a detailed inventory and dependency mapping of all applications, identifying stateful components.

Select the specific data synchronization method(s) for stateful applications and begin testing the setup and performance in a non-production environment.

Perform a skills assessment of the team responsible for the migration and plan for any necessary training or external support.