Photograph of a mosaic depicting Alexander the Great fighting King Darius III of Persia, released into the public domain. When services are subdivided into individual functions, or microservices, that contact each other through the network instead of through the memory and processor they would otherwise be sharing, Kubernetes can scale-out individual microservices and scale them back, as demand increases and falls, just the same as if they were complete applications. This is the process that brought Kubernetes to fame, which IT operators call scaling out and scaling back, respectively. As more clients make requests of a certain type or class of workload, the orchestrator makes more replicas of it available likewise, as requests subside, it reduces the number of replicas. In managing these services, Kubernetes changes the layout of the network as necessary. Kubernetes exposes this entire process to observability and manageability. This is distributed data processing, which used to take place in the confines of a single, monolithic application. It then presents those workloads to clients as services - meaning, a client system can contact them through the network, pass some data through to them, and after a moment or two of waiting, collect a response. In this scheme, Kubernetes enables any number of servers of many kinds at the same time, separated by any amount of distance, to share workloads for a common tenant. Here now is the Fall 2019 edition: Kubernetes is a workload distribution and orchestration mechanism for clustered servers in a data center, ensuring resource availability, accessibility, and balanced execution for multiple services concurrently. The definition of Kubernetes keeps changing because as it keeps growing, Kubernetes changes the world around it. Today's data centers remain the nerve center of the enterprise, and automation is powering new levels of agility and digital transformation. Ukrainian developers share stories from the war zone
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