When thinking about cost management and optimization in Kubernetes ownership, configuration is key. As one of the five key factors for business success, alongside security, compliance, reliability, and scalability, being able to get the most out of your budget allocations is all about avoiding dangerous misconfigurations.
Configuration issues are a top concern for Kubernetes, primarily because they can introduce significant risk to the cloud-native environment while wasting a lot of money. In this way, proper Kubernetes configuration plays a major role in the amount of money organizations spend. Thus, understanding best practices for cost optimization in containers requires a quick study of configuration issues and the importance of budget alignment.
How much does a Kubernetes workload cost, anyway?
1. Spread the costs
The first step is to determine the cost of each individual workload. But it’s not always a simple process, because Kubernetes nodes themselves aren’t simple. The nodes, which are the virtual or physical worker machines in Kubernetes (depending on the cluster), are what ultimately determine your bill amount. But that said, these nodes aren’t a perfect match for the workloads you’re running them on.
Kubernetes nodes are ephemeral and dynamic, capable of being created and destroyed as the cluster scales, or replaced entirely in the event of an upgrade or failure. To complicate matters, Kubernetes does something called “bin packing”, which places workloads into nodes based on what it identifies as the most efficient use of available resources – almost like a game of Tetris. Mapping a specific workload to a specific compute instance is still very difficult. Although efficient clustering of bins in Kubernetes can yield significant cost savings, it is difficult to distribute expenses when a given node’s resources are shared among multiple applications.
2. Good size resources
Robert Brennan is Director of Open Source Software at Fairwinds, the Kubernetes governance and security company. He focuses on developing software that removes complexity from the underlying infrastructure to enable the best experience for engineers. Prior to Fairwinds, he worked as a senior software engineer for Google in AI and natural language processing. He is co-founder of DataFire.io, an open source platform for building APIs and integrations, and LucyBot, developer of a suite of automated API documentation solutions deployed by Fortune companies. 500. He is a graduate of Columbia College and Columbia Engineering, where he focused on machine learning.
Prior to Kubernetes, organizations could rely on cloud cost tools to provide visibility into the underlying cloud infrastructure. Nowadays, Kubernetes provides a new layer of abstraction on top of cloud resource management, which can be a black box for traditional cloud cost monitoring tools. Therefore, organizations need to find a way “under the hood” of Kubernetes to appropriately allocate costs across applications, products, and teams.
When applications are deployed in Kubernetes, users need to know how much memory and CPU should be allocated to their application. This is where initial mistakes are often made, as teams fail to specify these settings or set them too high. Since developers are often tasked with writing code and shipping it quickly, they often omit seemingly optional configuration issues, such as CPU and memory demands and limits. But this leads to big problems and a serious deviation from best practices.
Ignoring this piece of the configuration puzzle leads to reliability issues, including increased latency and even downtime. Even if developers take the time to specify memory and CPU settings, they often compensate by allocating an overly generous amount to the application, to ensure that the application has all the resources it needs. In this way, developers tend to believe that “the more computation the better”. But it’s not just about shipping faster and with less risk. Kubernetes clusters must be configured with the right memory demands and limits to ensure that applications run and scale efficiently. This step avoids wasting money.
Without Kubernetes, cost control and visibility in place, and a strong feedback loop to pass this information to the development team, a developer’s potentially overly generous CPU and memory settings will be honored. And your organization will foot the big bill for cloud computing. Even though Kubernetes does its best to play Tetris as efficiently as possible with your resources by co-locating them in a resource-efficient way, it can’t do much when faced with unclear expectations about memory and CPU or over-allocated resources.
3. Empower teams
Developing a full-service ownership model for Kubernetes is a major best practice and enables development teams to own and run their applications in production. Ops teams can therefore focus on building a great platform for development teams. In Kubernetes, service ownership helps drive efficiency and reliability by providing feedback to engineering teams through things like automation, actionable guidance, alerts, and chain integrations. tools. This change in workflow requires teams to make productive decisions while continuing to follow best practices.
Teams building, deploying, and running their apps have more autonomy and fewer handoffs with other users. Again, the service ownership model helps developers understand more clearly how the software they create impacts both customer and operational overhead. When it comes to improving cost management and collaboration to save money, ownership of Kubernetes services, which includes proper monitoring and configuration, reduces the complexity of containerized workloads and puts the power of best practices in the hands of developers.
Characteristic image provided by the author.