How to Maximize Efficiency for Today’s Containerized Applications
Containerization is an increasingly popular way to architect modern applications for its portability across locations and platforms, and microservices-based development is a natural complement. Containerized microservices maximize development efficiencies and lend themselves well to DevOps processes. Along with the management tool, Kubernetes is easily orchestrated for deployment, management and scaling. What is needed is the right underlying hardware to maximize this new software architecture.
Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support and tools are widely available.
A master server is the “leader” of a pool of worker servers and the applications they serve are partitioned across various microservices and assigned into pods, a group of one or more containers with shared storage and networking. Pods provide on-demand scalability and resilience against hardware failures and can span multiple servers.
To begin implementing containerized services, consideration should be given to server size and whether to host them in the cloud or on-premises, as Kubernetes managed applications can easily span across both. For many applications, the deciding factor is cost. Latency is also a crucial issue to consider between server pods and their data, leading to hybrid cloud and edge micro-datacenters.
The selection of the server used as a worker node is first a question of size (how many cores, how much memory, can it quickly access enough storage and network bandwidth?). Using a big, multicore processor worker brings shortcomings including power inefficiencies and bottlenecks. Another important consideration is the hidden cost that a power-hungry processor needs to run even the smallest of microservices, which in turn reduces server utilization and increases energy costs.
Utilizing several servers is also an option but is not the most effective delivery route, as there can be resource inefficiencies that become apparent. Additionally, the overhead in loading up support for containers and registering workers with the master server can be burdensome.
What is required today is a cluster of servers that maximizes space and energy consumption, while delivering the throughput and resources for a Kubernetes workload. Ideally, not only will these will be Arm-based to leverage the energy efficiency of this architecture, but also the system design should focus on how to build clusters of machines efficiently, not just how to network multiple independent servers together. Arm processors are designed to perform a smaller number of types of computer instructions, allowing them to operate at a higher speed and enabling them to perform more millions of instructions per second. Arm optimizes pathways, enabling processors to provide high performance at a fraction of the power demand of other computing devices.
Support for Arm architecture has been available in Linux distributions for years, as well as the software and tools required to deploy and run a Kubernetes server cluster. Kubernetes service repositories are filling up with Arm containerized applications and development tools. This makes Kubernetes an easy-to-use option to build containerized packages for both x86 and Arm for any proprietary code.
Kubernetes-managed containerized applications are the future of modern software architecture, and to maximize the benefits of this, highly efficient, high throughput Arm-based servers offer the best supporting environment.