{"id":269130,"date":"2023-09-04T11:49:49","date_gmt":"2023-09-04T16:49:49","guid":{"rendered":"https:\/\/www.webscale.com\/?p=269130"},"modified":"2023-12-29T15:30:54","modified_gmt":"2023-12-29T20:30:54","slug":"the-seven-benefits-of-distributed-multi-cluster-deployment-part-2","status":"publish","type":"post","link":"https:\/\/www.webscale.com\/blog\/the-seven-benefits-of-distributed-multi-cluster-deployment-part-2\/","title":{"rendered":"The Seven Benefits of Distributed Multi-Cluster Deployment (Part 2)"},"content":{"rendered":"

Last week we published Part 1<\/a> of a two-part series on the benefits of distributed multi-cluster deployments for modern application workloads, based on our white paper<\/a> that dives deep into these topics. This is part two. The context for this discussion is the breakthrough Kubernetes Edge Interface<\/a>, which makes it incredibly easy to move workloads to the edge using familiar tools and processes, and then manage that edge deployment with simple policy-based controls.<\/em><\/p>\n

But why move to the edge if you\u2019ve already got a centralized data center or cloud deployment? If you haven\u2019t read the first three benefits in the previous post<\/a>, please take a moment to do so. Here are four more.<\/p>\n

Scalability<\/strong><\/h3>\n

In our previous post, we noted that improvements to performance and latency are a key benefit of moving application workloads to the edge (compared to centralized cloud deployments). A closely related factor: running multiple distributed clusters also improves an organization\u2019s ability to fine tune and scale workloads as needed. This scaling is required when an application can no longer handle additional requests effectively, either due to steadily growing volume or episodic spikes.<\/p>\n

It\u2019s important to note that scaling can happen horizontally (scaling out), by adding more machines to the pool of resources, or vertically (scaling up), by adding more power in the form of CPU, RAM, storage, etc. to an existing machine. There are advantages<\/a> to each, and there\u2019s something to be said for combining both approaches.<\/p>\n

A distributed multi-cluster topology helps facilitate greater flexibility in scaling. Among other things, when run on different clusters\/providers\/regions, it becomes significantly easier to identify and understand which particular workloads require scaling (and whether best served by horizontal or vertical scaling), whether that workload scaling is provider- or region-dependent, and ensure adequate resource availability while minimizing load on backend services and databases.<\/p>\n

Those familiar with Kubernetes will recognize that one of its strengths is its ability to perform effective autoscaling of resources in response to real time changes in demand. Kubernetes doesn\u2019t support just one autoscaler or autoscaling approach, but three:<\/p>\n