Achieving Peak Cloud Native Efficiency with Kubernetes Optimization

As more enterprises adopt Kubernetes as their container orchestration platform of choice, the topic of Kubernetes optimization is coming up more and more. With the complexity of Kubernetes resource management, manual approaches to optimization can require days or weeks of tuning, tweaking, and troubleshooting.

This blog will provide an overview of Kubernetes optimization – what is it, why does it matter, and how can you achieve it?

What is Kubernetes Optimization?

To start, let’s be sure and define what we mean by Kubernetes optimization. Merriam-Webster defines ‘optimization’ as:

an act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible

If we apply that to Kubernetes, it means we want to make our cloud native/Kubernetes environment and the applications that run in that environment as perfect, functional, or effective as possible. The term “perfect, functional, or effective” may mean different things to different organizations, but leaving aside an application’s functional requirements, Kubernetes optimization consists of two key components:

• An application’s performance and reliability. In other words, what is the response time of the application and how much downtime does it have?
• The cost of running the application, which is a direct result of compute resources utilized in running the app, for example CPU, memory, and storage.

Simply put then, Kubernetes optimization means that your application meets or exceeds business requirements for performance and reliability (defined by SLAs and SLOs) at the lowest possible resource utilization and cost.

Why Does Kubernetes Optimization Matter?

Kubernetes adoption continues to accelerate, with recent data from Red Hat showing 70% of organizations using the popular container orchestration platform, with almost a third planning to significantly increase their use of containers in the next twelve months. Similarly, in their 2022 Kubernetes Adoption Survey, Portworx found that 87% of organizations expect Kubernetes to play a larger role in their organizations’ infrastructure management in the next two to three years.

The growth in Kubernetes adoption is driven by many factors. The Portworx survey found the top three benefits expected from Kubernetes were:

1. Faster time to deploy new apps (68% ranked in their top 3)
2. Reduced IT/staffing costs (66%)
3. Easier to update apps (63%)

These benefits all bring tremendous business value, but they’re not easy to achieve. In Canonical’s 2022 Kubernetes and cloud native operations report, respondents ranked “How can we optimize resource utilization?” as the second most important question ops people should care about, behind only, “How secure is that thing?” Without optimization, it’s impossible to realize the promised value of Kubernetes.

Benefits of Kubernetes Optimization

It’s clear that Kubernetes optimization is a priority for enterprises, for several reasons:

• Cost savings – With cloud costs making up an increasing portion of the overall cost of revenues, sometimes approaching 75-80%, and 47% of cloud spend wasted, Kubernetes optimization is imperative, and the opportunity for cost savings is substantial.
• User satisfaction – Kubernetes optimization means consistently meeting or exceeding SLAs and SLOs. The result? No more unacceptable response times or frustrated users, and fewer abandoned site visits.
• Efficient resource utilization – While cost savings is important, resource utilization is another benefit that should be considered separately. Especially for organizations running on-premises in private clouds, compute resources can be reallocated for other uses, like additional testing environments.
• Environmental responsibility – Kubernetes efficiency means fewer resources used, with the result being reduced carbon emissions from data centers. And while environmental responsibility is worthy on its own merits, it’s also valued by consumers, with 80% of consumers considering sustainability when making purchase decisions.

How to Achieve Kubernetes Optimization

Given all the benefits, why is Kubernetes optimization still a top unsolved issue for so many organizations? It’s because of the perception that optimization is time-consuming and difficult. As one developer commented, “Who has time to optimize? The name of the game is to slap as many features together as possible as fast as possible and ship it!”

It’s true that time spent doing anything other than developing new and differentiating capabilities is considered by most organizations as time wasted. With the complexity of Kubernetes resource management, manual approaches to optimization can require days or weeks of tuning, tweaking, and troubleshooting. Nothing could be more frustrating for an engineer who just wants to work on cool technology and deliver business value.

Fortunately, new solutions like StormForge have applied machine learning and automation to make Kubernetes optimization virtually effortless. StormForge includes two solutions for a holistic approach to Kubernetes optimization.

Optimize Live Machine Learning-based Optimization

StormForge Optimize Live continuously rightsizes Kubernetes workloads to ensure applications are both cost effective and performant while removing developer toil. As a vertical rightsizing solution, Optimize Live is autonomous, tunable, and works seamlessly with the Kubernetes horizontal pod autoscaler (HPA) at enterprise scale.

Optimize Live addresses both over- and underprovisioned workloads by analyzing usage data with advanced machine learning to recommend optimal resource requests and limits. Recommendations can be deployed automatically on a flexible schedule, accounting for changes in traffic patterns or application resource requirements, ensuring that workloads are always rightsized and freeing developers from the toil and cognitive load of infrastructure sizing.

Organizations see immediate benefits using Optimize Live — with cost savings of 40-60% from reducing wasted resources, and performance improvements across the entire estate from rightsizing underprovisioned workloads.

Get Started with Kubernetes Optimization

With tools like StormForge, Kubernetes optimization can become more than an unattainable vision. In fact, given the value that can be gained with minimal effort, it’s now a must-have. StormForge can help you unlock the benefits of Kubernetes, reducing costs, minimizing resource waste, and ensuring application performance, all while freeing up your software engineers to innovate.

FAQ

Why is Kubernetes application optimization important?

Kubernetes application optimization is important because it allows applications to take advantage of the cloud-native architecture of Kubernetes to realize improved performance, efficiency, scalability, and availability. By optimizing applications for Kubernetes, organizations can reduce costs, improve user experience, and ensure their applications are always available and functioning properly. Kubernetes application optimization also helps to reduce the time and effort needed to deploy and manage applications.

How do you optimize Kubernetes?

There are two ways to optimize a Kubernetes environment:

1. Handle the task manually. This involves a human manually changing settings or parameters, assessing how these “tweaks” have impacted results, and then employing a trial-and-error process until one set of results are acceptable and desirable. Clearly, this trial-and-error approach is time-consuming, error-prone and ultimately ineffective for optimizing a Kubernetes environment.
2. The alternative is automated optimization of resource management and application performance that is software-defined – driven by artificial intelligence and machine learning. Given the huge numbers of variables involved with cloud-based applications, and the complexity of testing, adjusting and re-testing millions of combinations of variables, the automated approach is far superior to any manually-based efforts.

What is container optimization?

Container optimization is the process of finding the set of configuration options that will result in application performance that meets or exceeds SLAs at the lowest possible cost. Configuration settings include CPU and memory requests and limits, replicas, and application-specific settings such as JVM heap size and garbage collection. This can be accomplished by tuning the environment in which the containers run, as well as the applications themselves.

How can I improve my Kubernetes cluster?

Kubernetes clusters include one or more nodes that run containerized applications. Within this set of components, there are several opportunities for improvement in terms of performance and efficiency, including:

• Resource optimization at the container level, including memory and CPU requests and limits, and replicas.
• Resource and configuration settings for the applications inside the container, including worker process/thread counts, garbage collection settings, memory process allocation, and cache settings.
• Resource settings and constraints at the node level, including CPU and memory available for scheduling workloads as well as restrictions and affinities on what type of workloads can be scheduled on the node.
• Also for nodes, specialized hardware such as GPUs can be added to assist with specialized workloads.
• Networking and storage infrastructure can be balanced between performance, cost, and level of complexity.