Multi-Cluster Load Balancing in Kubernetes: Strategies and Considerations”

Introduction

In today’s tech landscape, multi-cluster Kubernetes deployments have become the new normal. With this shift comes a plethora of use cases that necessitate effective multi-cluster Load Balancing. From scaling services horizontally across clusters in an east-west fashion to load balancing external traffic for high availability and disaster recovery scenarios in a north-south orientation — there’s no one-size-fits-all solution.

In this blog post, we’ll explore the world of multi-cluster load balancing, helping you choose the right approach to meet your specific use case. By selecting the right method, you can avoid unnecessary cross-cluster/region/cloud data charges, ensure compatibility with heterogeneous clusters, and steer clear of vendor lock-in.

Key Networking Constructs: Regardless of the multi-cluster load balancing strategy you adopt, several fundamental networking constructs should remain intact within Kubernetes:

1. Pods-to-pods communication without the need for proxies or translations using IP addresses.
2. The service abstraction, which groups pods under a common access policy, creating a virtual IP for transparently proxying client requests to the pods.
3. Exposing services to the external world using Ingress, Gateway, or services of type LoadBalancer.

Different Multi-Cluster Load Balancing Strategies

Let’s delve into the various multi-cluster load balancing strategies:

1. GSLB (DNS-based Global Server Load Balancing):

• Geographic Distribution: GSLB shines in geographically distributed architectures, directing traffic to the nearest cluster or region based on the client’s location, minimizing latency.
• External Client Traffic: Ideal for applications with significant external traffic, GSLB efficiently balances traffic from external sources.
• Failover and Disaster Recovery: GSLB plays a pivotal role in disaster recovery, redirecting traffic to backup clusters or regions in case of failures.

• Reference: K8gb

2. Service Mesh-based Multi-Cluster Load Balancing

• Service Mesh Efficiency: Service Mesh excels in efficiently load balancing services across multiple clusters, offering precise control over traffic routing, load balancing, and service discovery.
• East-West Traffic Handling: It’s perfect for east-west traffic, managing communication within and across clusters.
• Advanced Traffic Management: Service Mesh offers advanced traffic management capabilities like traffic splitting, circuit breaking, retries, and fault tolerance, enabling fine-grained control over traffic patterns.

• References: Linkerd, Istio

3. CNI-based Multi-Cluster Load Balancing

• Pod-to-Pod Communication: CNI-based load balancing is designed for pod-to-pod communication across multiple clusters.
• Service Discovery: It also provides service discovery and service-to-service load balancing capabilities.
• Requirements: Ensure each Kubernetes worker node has a unique IP address and IP connectivity between all worker nodes. Assign unique PodCIDR ranges to all clusters.
• Implementation: Achieve this through VPN tunneling for cross-region clusters or direct physical network connections for clusters within the same region/DC.

• References : cilium, submariner

How to select the appropriate multi-cluster load balancing strategy?

Consider asking the following questions to evaluate your needs. For examples, Let say If you are already using a service mesh for mTLS/rate limiting, It is not necessary to use service mesh for your multi-cluster load balancing. Keep in mind that it may not be the ideal choice as it can introduce additional overhead in service discovery & cross regional data transfer(based on the service mesh topology).

You should ask this question and understand why you are using a particular approach to multi cluster load balancing

1. Why do you require multi-cluster load balancing? Is it solely for achieving application high availability and disaster recovery?
2. Is your application stateless? If not, how do you synchronize state across the application running in different regions or clusters? Are you using a shared database layer for this purpose?
3. Does your application need service-to-service communications across clusters? If so, why is this required? Keep in mind that enabling service-to-service communication across clusters may introduce additional hops and gateway requirements with public IPs. In some cases, provider network VPN peering might be necessary, especially if your clusters are spread across different regions or providers.
4. Are you primarily dealing with HTTP-based applications, or do you also support UDP/TCP-based services? Service mesh-based service-to-service communication across clusters may work well for HTTP-based applications but may have limitations with other protocols.
5. Does your use case necessitate pod-to-pod service connectivity across clusters, especially if your clusters are spread across different regions or cloud providers? Keep in mind that this might require overlay networking using VPN, which can incur additional costs and introduce latency. Do the benefits of pod-to-pod communication across clusters justify these trade-offs?

By addressing these questions, you can better determine the right multi-cluster load balancing strategy that aligns with your specific application requirements and constraints.

Conclusion: In summary, choosing the right multi-cluster load balancing strategy depends on your specific requirements:

• For load balancing external traffic across clusters spanning multiple regions, DNS-based GSLB is a straightforward solution that seamlessly integrates with existing clusters.
• When scaling services across clusters and facilitating east-west communication, Service Mesh-based multi-cluster load balancing is the go-to approach.
• If your needs revolve around direct pod-to-pod communication across clusters, opt for the CNI-based approach.

In subsequent blog posts, we will dive deeper into each multi-cluster load balancing method, providing detailed examples to help you make informed decisions. Stay tuned!

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