Container Underprovisioning With PSI-Based Workload Redistribution
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Solution Overview
Problem
Existing cloud computing resource allocation methods over-provision resources based on high-percentile utilization metrics, leading to significant idle resource wastage during normal operation.
Innovation Solution
Implement a method that allocates resources at an under-provisioned level, dynamically adjusting based on historical usage patterns and real-time performance metrics, using Pressure Stall Information (PSI) to monitor and manage workload distribution, evicting workloads from busy nodes, and leveraging autoscaling to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are allocated based on high-percentile utilization metrics (80th or 90th percentile), then resource availability during peak demand is improved, but resource efficiency during normal operation deteriorates due to substantial idle resources
Solution Approach 1:
The system dynamically adjusts resource allocation by transitioning from static high-percentile-based provisioning to real-time monitoring and adaptive workload redistribution. When nodes are determined to be busy based on monitored metrics, the system dynamically reassigns workloads to underutilized nodes, ensuring resource availability is maintained during peak demand while optimizing utilization during normal operation.
Solution Approach 2:
The system implements continuous monitoring of node utilization metrics and uses this feedback to make real-time resource allocation decisions. By monitoring performance metrics and determining when nodes are busy, the system creates a closed-loop control mechanism that adjusts resource distribution based on actual demand, preventing both over-provisioning and under-provisioning.
2Reliability
If conservative resource allocation strategies are used to handle sporadic peak demand, then service level agreement compliance is improved, but operational costs increase due to reserved capacity
Solution Approach 1:
The system merges the resource pools of multiple nodes and manages them as a unified flexible capacity. Instead of reserving capacity on individual nodes, the system consolidates available resources across the cluster and dynamically allocates them based on real-time demand, reducing total reserved capacity while maintaining service level agreements.
Solution Approach 2:
The system changes the allocation parameter from fixed high-percentile thresholds to dynamic real-time utilization metrics. By monitoring actual node busy status and adjusting workload distribution accordingly, the system reduces reserved capacity while ensuring service level agreement compliance through adaptive resource management.
3Loss of energy
If resources are allocated at under-provisioned levels below peak demand, then resource efficiency is improved, but resource contention increases during high demand periods
Solution Approach 1:
The system continuously monitors node performance metrics to detect resource contention conditions. When nodes are determined to be busy based on monitored thresholds, the system receives feedback that triggers dynamic workload reassignment to underutilized nodes, preventing resource contention while maintaining under-provisioned allocation levels for efficiency.
Solution Approach 2:
The system transitions from static under-provisioned allocation to dynamic resource management. By continuously monitoring node status and dynamically redistributing workloads based on real-time utilization, the system maintains high resource efficiency during normal operation while preventing resource contention during demand spikes through adaptive reallocation.
Data Source
AI summary
A system or a method for optimizing resource allocation in a cloud computing environment. The system determines an amount of resource to be allocated for a container workload at an under-provisioned level and schedules the workload onto a node among a plurality of nodes that has the determined amount of resource. Performance metrics of the nodes are continuously monitored to determine whether each of the plurality of nodes is busy. Responsive to determining that the node is busy, the system cordons the node by updating the node's metadata to prevent scheduling new workloads while allowing existing workloads to continue running. In some embodiments, container workloads may be evicted from the cordoned node and migrated to another node with sufficient resources.