Multi-runtime Workload Framework for Cluster Performance Monitoring
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Solution Overview
Problem
System administrators face challenges in accurately assessing the performance of workloads across different runtime engines in orchestrated environments, leading to potential production issues due to misconfiguration, which can result in delays or costly redesigns.
Innovation Solution
A framework that automatically deploys a common workload across multiple runtime engines, collecting performance metrics and providing them in an automated dashboard for comparison, allowing for reliable evaluation of configuration and resource usage across various engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If workloads are deployed across multiple runtime engines without automated performance monitoring, then configuration flexibility is improved, but performance assessment accuracy deteriorates
Solution Approach 1:
The patent implements automated performance monitoring that continuously collects metrics from multiple runtime engines and provides feedback to administrators through dashboards. This feedback mechanism enables accurate performance assessment across different runtime engines while maintaining configuration flexibility, directly resolving the contradiction between adaptability and measurement precision.
Solution Approach 2:
The framework provides a universal performance monitoring system that works across multiple runtime engines (Kubernetes, OpenShift, Mesos, etc.) simultaneously. This multi-functional approach allows accurate performance assessment to be achieved universally across different engines without sacrificing the flexibility to choose different runtime environments.
2Device complexity
If manual performance assessment methods are used across multiple runtime engines, then system complexity is reduced, but time consumption and administrative overhead increase
Solution Approach 1:
The patent implements a self-service automated monitoring system that independently collects performance metrics, processes data, and generates reports without requiring manual administrator intervention. The system automatically discovers runtime engines, collects relevant metrics, and presents findings through web-based dashboards, dramatically reducing both time consumption and administrative overhead while maintaining manageable system complexity through modular architecture.
3Loss of energy
If performance metrics are not automatically collected and compared across runtime engines, then resource usage for monitoring is reduced, but configuration accuracy deteriorates
Solution Approach 1:
The patent implements selective metric collection that gathers only the most relevant performance indicators for each runtime engine type rather than attempting to collect all possible metrics universally. This partial action approach collects sufficient data to achieve accurate configuration assessment while minimizing the resources consumed by the monitoring system itself.
Data Source
AI summary
A command identifying a workload for execution within a cluster architecture is received. Responsive to the command, the workload is deployed to a plurality of different runtime engines on one or more compute nodes within the cluster architecture, wherein the plurality of different runtime engines comprise a container-based runtime engine and a virtual machine (VM)-based runtime engine. Performance metrics are received from each of the plurality of different runtime engines corresponding to execution of the workload.