Pod Resource Rescaling Using Utilization Metrics
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Solution Overview
Problem
Existing container management platforms fail to accurately match resource allocations to current demands, leading to underprovisioning or overprovisioning, which results in performance degradation or resource wastage.
Innovation Solution
A method and system for adjusting pod resources by collecting resource utilization metrics, calculating selected and target CPU and memory requests based on current demands, and incrementally scaling resources to match changing workloads, ensuring pods are updated with up-to-date resource allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If container management platforms adjust resources based on historical demand, then resource allocation is automated, but resources are either underprovisioned or overprovisioned
Solution Approach 1:
The system performs preliminary actions by continuously collecting resource utilization metrics before making allocation decisions. It gathers CPU usage, memory usage, and other performance data over time to establish accurate baseline measurements, enabling more precise resource provisioning decisions rather than relying solely on historical averages
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring container performance metrics and using this information to dynamically adjust resource allocations. The platform compares actual resource consumption against allocated resources and automatically refines provisioning decisions based on observed performance patterns and utilization trends
2Device complexity
If platforms allocate resources based on closest historical demand, then resource adjustment is simplified, but performance degradation occurs due to underprovisioning
Solution Approach 1:
The system transitions from static historical-based allocation to dynamic resource management. It continuously adapts resource allocations based on real-time and recent utilization patterns, allowing the platform to respond to changing workload demands while maintaining simple operational processes through automated adjustment mechanisms
3Reliability
If platforms over provision resources to containers, then performance is maintained, but resources are wasted
Solution Approach 1:
The system dynamically changes resource allocation parameters based on actual utilization patterns. By continuously analyzing CPU usage, memory consumption, and other metrics, the platform adjusts allocation parameters to match actual needs, eliminating both underprovisioning and overprovisioning while maintaining optimal container performance
Data Source
AI summary
Certain aspects of the disclosure provide a method for adjusting resources of a pod. Resource utilization metrics and resource configuration of a pod running on a node in a cluster of nodes are received and stored in a metrics data store. A selected CPU request, a target CPU limit, a selected memory request, and a target memory limit is calculated based on the resource utilization metrics and the resource configuration. A recommendation for rescaling CPU and memory for the pod is generated based on the selected CPU request, the target CPU limit, the selected memory request, and the target memory limit. A new pod is created in the cluster based on the recommendation. After the new pod is created, the pod running on the node is deleted.