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Multi-Cluster Observability Part 1: Building A Foundation
In the world of modern Kubernetes, things have come a long way from the days of a single cluster handling one app. Now, it's common to see setups that span multiple clusters across different clouds.
Initially, managing those clusters was a complicated operation with many moving parts. Using tools such as SUSE Rancher, RedHat OpenShift or AWS EKS, made managing multiple clusters somewhat easier. And with a Kubernetes controller like Argo CD or a container management engine like Fleet, deploying to these diverse clusters can be fully automated through pipelines. This takes away much of the complexity we faced in the early days of juggling multiple clusters.
Despite these advances, a question emerges: how does an organization effectively observe and monitor these distributed clusters?
In this three-part blog series, we’ll dive into details around the 5 common variations of a multi-cluster setup, how to build a strong multi-cluster observability strategy, and we'll provide you with a step-by-step guide to turn multi-cluster observability into a successful operational reality.
Key considerations for running apps in a multi-cluster setup
If you’re thinking about running applications across multiple clusters, you’ve probably come to appreciate that it’s not entirely a technical decision; it's a strategic move that can propel your enterprise toward greater resilience, scalability and flexibility.
Exploring a multi-cluster setup involves thinking through aspects like availability, disaster recovery, load balancing, compliance, costs and many other factors that make it an enticing choice for modern enterprises. The increasing adoption of this approach underscores its transformative potential for organizations worldwide and in practically every industry.
Let's look at how the following considerations converge to reshape and enhance the landscape of your application deployment strategies.
High Availability and Disaster Recovery: By spreading resources across multiple clusters, possibly in different geographic locations, you can ensure high availability. If one cluster goes down due to a hardware failure, network issue or a natural disaster, the others can continue to operate, minimizing downtime.
Load Balancing and Scalability: In a multi-cluster setup, effective load balancing is well within reach as the workload is distributed across clusters, optimizing resource usage. This not only enhances scalability by accommodating increased demand but also allows for the seamless addition of clusters without overburdening existing infrastructure. Furthermore, deploying clusters in multiple geographic locations can significantly reduce latency, guaranteeing there's always a copy close to your customer.
Avoiding Vendor Lock-in: Steering clear of vendor lock-in is a smart move. By utilizing multiple cloud providers or a combination of cloud and on-premises clusters, you can tap into the best features and pricing from different providers. This setup also makes it easier to switch if a provider changes their service terms or pricing.
Compliance and Data Sovereignty: Regulations around data storage and processing differ across countries and regions. Multi-cluster setups provide the freedom to store and process data in various locations, ensuring compliance with geo-specific legal requirements. This flexibility is critical, especially for global operations.
Specialized Workloads: Tailoring different clusters to specific types of workloads is a game-changer. For instance, you might have one cluster fine-tuned for high-performance computing, another geared towards large-scale data processing and yet another designed for running lightweight microservices. This specialization can lead to better performance and efficiency.
Communication Costs: Communication between clusters can have a significant impact on costs. Consider keeping applications isolated from each other, especially if close monitoring of costs supports your business objectives.
Understanding the 5 foundational multi-cluster variations
The world of multi-cluster environments is diverse, with each variation—from single cloud multi-clusters to hybrid and federated models—bringing unique advantages and challenges.
In practice, there are five different base models, each having its own nuances. A typical production setup often combines some of these models, especially in a large enterprise where you might find various flavors. Whether your goal is to maximize availability, avoid vendor lock-in or optimize for specific workloads, a thorough understanding of these variations is crucial for a successful multi-cluster strategy.
The following list will help you select the right architecture for your organization's needs.
Single Cloud, Multiple Clusters: In this setup, you create multiple clusters within a single cloud provider, such as AWS, Azure or GCP. Each cluster operates independently but shares the infrastructure of the same cloud provider. This arrangement promotes load balancing and high availability so that if one cluster encounters issues, others remain unaffected.
Multi-Cloud, Single Cluster per Cloud: Here, you establish a single cluster within each of multiple cloud providers. This is a strategy to avoid vendor lock-in, capitalize on unique features offered by each provider and enhance overall resilience. If one cloud provider has an outage, the other can continue to operate.
Hybrid Cloud: This entails a mix of on-premises, private cloud and public cloud clusters. For instance, you could have some clusters housed in your own data center and others in a public cloud. This approach proves beneficial for companies with legacy systems on-premises, allowing them to harness the scalability offered by the cloud.
Federated Clusters: This setup is more intricate, interconnecting multiple clusters to allow resource and data sharing. Think of it as a network where clusters communicate and easily offload tasks to one another. It's great for large-scale operations that demand extensive inter-cluster communication.
Edge Clusters: Deployed at or near the source of data generation, like in the case of IoT devices, these clusters are tailored for low latency and real-time processing. Their strategic placement reduces the necessity to send all data to a central cloud, conserving bandwidth and enhancing response time.
Optimizing focus and efficiency in multi-cluster management
As organizations increasingly adopt a multi-cluster approach to Kubernetes-based app deployment, the growing demand for management tasks diverts focus from more productive work. What's needed is a centralized approach to viewing, managing and consolidating diverse clusters. This not only promotes resource optimization, it lays the groundwork for quick issue detection and resolution without sacrificing time. To learn more, take StackState for a test run. Or try us out by exploring our playground!
UP NEXT: We explore the very different facets of the most common multi-cluster observability strategies in Part 2 of our blog series.