In the world of Kubernetes, ensuring cost-effectiveness without compromising performance is paramount. Yet, as we venture into the intricate world of container orchestration, three significant challenges surface — Overprovisioning, Instance Rightsizing, and Effective Workload Management. Let’s delve deeper into these areas of concern.

Overprovisioning: A Slippery Slope to Increased Costs

What is Overprovisioning?

Over-provisioning is the gap between what is truly utilized by the applications and what was reserved in advance. It’s the act of allocating excessive resources—such as CPU and memory—to containers, more than they truly need. This not only squanders valuable resources but also escalates costs and diminishes efficiency.

The Stark Reality

From our analytics, it’s disheartening to see that over 70% of k8s clusters suffer from more than 30% overprovisioning.

Dissecting Overprovisioning

Allocation Gap: 

Visualize a gap between what you’ve allocated and what’s genuinely utilized. This gap, on average, contributes to 25% of the overprovisioning problem. Specifically, it arises due to:

• Mismatched Memory to CPU Request Ratios: In most clouds, apart from GCP, vertical scaling is restrictive, binding you to specific SKUs. This can cause a memory/CPU bottleneck, accounting for 80% of the allocation gap.
• Small Pod Requests: Sometimes, excess gaps arise from pod requests too petite for even a single pod placement. This forms less than 20% of the gap.
• Taint-induced Gaps: Certain scheduling restrictions can prevent specific pods from being placed in nodes, which makes up less than 5% of the gap.

Underutilization Gap:

This can be viewed as the chasm between what’s allocated and what’s actually used. This gap primarily emerges because:

• Engineers prioritize customers SLA over cost-cutting.
• A misalignment exists between teams. Engineers handle resource requests, while devops manage the costs.
• Manual rightsizing is an ongoing process and requires a lot of effort.

Instance Rightsizing: Navigating the Maze of Choices

In Kubernetes, clusters are essentially an amalgamation of instances, be it VMs or cloud instances. Instance rightsizing is the art of striking a perfect balance between resources allocated and real workload demands.

The crux of the problem lies in the overwhelming number of choices offered by cloud providers. With hundreds of instance types available, even seasoned companies with sophisticated monitoring mechanisms find it taxing to make apt selections.

Moreover, the challenge magnifies when you factor in bin packing offerings and affinity configurations, which demand not just selecting node groups but also discerning which workloads coexist within the cluster.

Effective K8s Workload Management: The Search for a Unified Solution

Managing K8s workloads, especially in environments bustling with numerous applications, is a herculean task. Many customers find themselves juggling multiple tools and solutions for pod resource rightsizing, spot utilization, and other crucial configurations.

The market’s glaring void is the absence of a holistic solution. While some cloud providers do offer certain built-in capabilities or complementary tools, they often fall short in meeting all requirements, sealing the optimization gap, offering a unified impact visibility hub, or meeting the stringent standards of enterprise-grade solutions.

Intel Tiber App-Level Optimization’s Holistic Kubernetes Cost Optimization Solution

Intel Tiber App-Level Optimization offers a suite of autonomous and continuous optimization solutions, capable of reducing your Kubernetes costs by up to 45%. Users can expect reductions of up to 61% in allocated cores and 71% in allocated memory. All these advantages can be harnessed without any code alterations. 

With a blend of autonomous runtime optimization combined with Kubernetes resource rightsizing, Intel Tiber App-Level Optimization provides holistic multi-level performance enhancements that ensure your system runs at peak efficiency. Plus, it’s fully customizable, allowing adjustments per cluster or per label, uncovering opportunities for CPU, memory, and cost reductions without ever compromising on resiliency, availability, or stability.

Intel Tiber App-Level Optimization’s Kubernetes Optimization is trusted by giants like ShareChat, ironSource, Claroty, Drift, Salt Security, and Coralogix. It’s versatile and supportive, seamlessly integrating with platforms including AWS, GCP, Azure, Kubernetes, EKS, GKE, AKS, Red Hat, and OpenShift. 

Book a Demo and see the Kubernetes cost savings firsthand. As these reviews from Sharethrough, Claroty, and ironSource testify, Intel Tiber App-Level Optimization is reshaping the Kubernetes optimization landscape.