The Sprawling Kubernetes Issue
Using Kubernetes allows a company to abstract machines, storage and networks from their physical implementation, but as time goes by managing issues similar to virtual machines can become issues. Similar to virtual machine sprawl, Kubernetes sprawl is now becoming a reality. Just like sitting is the new smoking, container sprawl is the new VM sprawl.
The overall trend is organizations are running workloads with root access, workloads without memory limits set and workloads impacted by image vulnerabilities. Configuration issues are running amok and unprepared developers are not helping the issue.
Container sprawl is getting worse, not better. Benchmark data shows that enterprise companies are not configuring Kubernetes to best practice guidelines. Issues are arising in reliability, security and cost efficiency.
Let’s walk through some best practices.
Set Your Developers Up For Success
If you have been running Kubernetes for 6 months to a year or longer, you may begin to experience an increase in costs. This can happen due to developers not having true cloud experience or a deep understanding on how to properly optimize for cost efficiency.
The developers may try to over-provision instead of optimize for sudden spikes. Providing your developers with the proper training will allow them to build Kubernetes clusters properly. A great example would be https://acloudguru.com/ where developers can get on-demand training.
Kubernetes Monitoring for Enterprise
Guardrails are necessary to reign in the configuration issues and avoid all the issues that come into play. Kubernetes services can be costly if you do not have a plan. Just like you tune a car, you need to tune your Kubernetes workloads. In order to achieve this, you need to perform real-time monitoring on your Kubernetes deployments.
Kubernetes monitoring is where you will, in real-time, monitor events such as security, health and cost of your containerized apps. Obtaining optimal performance and health is critical to any successful Kubernetes deployment or dev-ops initiative.
Here are some important metrics one should monitor when it comes to Kubernetes.
- API Latency Requests
- Kubernetes Cluster Health
- Running and Deployment of Kubernetes Pods
- Resource Metrics (IE: CPU, Memory, Disk)
- Application Metrics
- Cost Metrics
So how does one collect all of these metrics? These are just some examples. The list can go on and on. As an organization, you have to determine what metrics are essential. For example:
- Cost per Unit
- Cost per feature
- Cloud Cost
- Cost Per App
Kubernetes Optimization for Enterprise
The game plan for optimizing is as follows:
- Monitor your Spending -Using Granulate’s capacity optimization or another monitoring solution to provide insights into your Kubernetes cluster.
- Use Cloud Native Applications – Build your Kubernetes containers using best practices (IE hardening, version control, limits, RBAC).
- Optimize Cost with Autoscaling – Kubernetes provides a set of features to ensure that clusters are sized to handle any type of load. As demand rises, autoscaling expands and contracts to meet that demand of your enterprise.
- Make Cost Optimization part of the definition of done – Developers are so focused on coding, they don’t take into account optimizing cost. It can be the difference between success and failure of a project. Make sure you are taking into account cost optimization and your definition of done when you complete a coding milestone.
- Continuous Optimization – Continuously orchestrate Kubernetes resources to fit the actual usage with Granulate’s real-time Kubernetes & containers optimization solution. This can reduce your Kubernetes costs by up to 45% totally autonomously, so that your developers can focus on new features and applications.
Configuring Kubernetes has many challenges. And these challenges must be mitigated with monitoring and tuning in order to keep security, reliability and cost efficiency. Developer training and keen Kubernetes monitoring from DevOps, along with leveraging automation to continuously optimize workloads, will help avoid misconfigurations. Applying Kubernetes governance and guardrails via monitoring and best practices will allow enterprises to ship code faster and provide the SLA and performance your customers demand.