In recent years, the rapid adoption of Kubernetes has emerged as a transformative force in the world of cloud computing. Organizations across industries have been drawn to Kubernetes’ promises of scalability, flexibility and streamlined application deployment. However, while Kubernetes offers an array of benefits in terms of application management and development efficiency, its implementation is not without challenges. As more businesses migrate to Kubernetes-driven environments, an unintended consequence has become increasingly apparent: a surge in cloud costs. The very features that make Kubernetes so attractive are also contributing to a complex and dynamic cloud infrastructure, leading to new cost drivers that demand careful attention and optimization strategies.
For example, inaccurate resource requests set on workload resources in Kubernetes can lead to massive over-provisioning of resources, causing significant increases in cloud costs. When resource requirements are overestimated, Kubernetes will scale the underlying infrastructure, leading to waste. This inefficient utilization can create workload scheduling issues, hamper cluster performance and trigger additional scaling events, further amplifying expenses. Mitigating these issues, particularly at scale, has proven to be a tremendous challenge.
Furthermore, right-sizing workload resources in Kubernetes is challenging at scale due to the sheer volume and diversity of applications. Each has varying resource demands, making it complex to accurately determine optimal resource allocations for efficient utilization and cost-effectiveness. As the number of deployments increases, manual monitoring and adjustment become impractical, necessitating automated tools and strategies to achieve effective right-sizing across the entire cluster.
To continuously right-size Kubernetes workload resources at scale, three key elements are crucial. First, resource utilization needs to be continuously tracked across all workloads deployed on a cluster, enabling continuous assessment of resource needs accurately. Next, machine learning capabilities play a vital role in optimizing resource allocations by analyzing historical data and predicting future resource demands for each deployment. Lastly, automation is needed to proactively deploy changes and reduce toil on developers. These technologies ensure that Kubernetes resources are efficiently utilized, leading to cost-effectiveness and optimal workload performance across the entire infrastructure.
StormForge Optimize Live combines automated workload analysis with machine learning and automation to continuously optimize workload resource configurations at enterprise scale.
Optimize Live is deployed as a simple agent, automatically scans your Kubernetes cluster for all workload types and analyzes their usage and settings with machine learning. Right-sizing recommendations are generated as patches and are updated continuously as new recommendations come in.
These recommendations can be implemented quickly and easily by integrating the recommendations into your configuration pipeline, or they can be implemented automatically, putting resource management on your Kubernetes cluster on autopilot.
StormForge (link resides outside ibm.com) users see much-improved ROI in their cloud-native investments while eliminating manual tuning toil—freeing up engineering bandwidth for higher-value initiatives.
Sign up for a 30-day free trial of StormForge Optimize Live (link resides outside ibm.com) to get started.