Cloud Resource Management System Bottleneck Detection
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Solution Overview
Problem
In a cloud environment with multiple applications of varying performance characteristics, existing methods fail to accurately identify performance bottlenecks, leading to further performance deterioration due to incorrect resource allocation and configuration changes.
Innovation Solution
The method involves detecting performance deterioration in resource-allocated instances, extracting instances with similar performance characteristics, comparing their tendencies and patterns, and estimating bottlenecks based on maximum similarity to enhance accuracy in specifying performance deterioration factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtualization techniques are used to consolidate multiple business systems on shared physical resources, then resource use efficiency is improved, but performance stability deteriorates due to bottlenecks from excessive load
Solution Approach 1:
The system performs preliminary detection of performance deterioration factors before configuration changes are made. By identifying bottlenecks in advance through monitoring resource usage patterns and performance metrics, the system can prevent performance degradation before it occurs, allowing safe consolidation of multiple workloads on shared physical resources
Solution Approach 2:
The system implements continuous feedback loops that monitor performance metrics, detect deterioration factors, and trigger appropriate responses. Performance data is collected, analyzed to identify bottlenecks, and used to dynamically adjust resource allocation or trigger configuration changes, ensuring performance stability while maintaining high resource utilization
2Reliability
If configuration changes are made to eliminate performance bottlenecks, then performance stability is improved, but processing load increases causing further performance deterioration
Solution Approach 1:
The system detects and analyzes performance deterioration factors before implementing configuration changes. By preliminarily identifying the root cause of bottlenecks and simulating or evaluating the impact of potential changes, the system can plan configuration adjustments that minimize additional processing load while effectively resolving performance issues
Solution Approach 2:
The system applies configuration changes selectively and partially rather than comprehensively. Instead of making broad configuration adjustments that would generate excessive processing load, the system targets specific bottlenecks with precise, minimal changes that address performance issues while maintaining system stability and minimizing disruption
3Reliability
If resource allocation is increased to stabilize performance, then performance stability is improved, but resource use efficiency deteriorates
Solution Approach 1:
The system applies resource allocation adjustments locally and selectively to specific resources or workloads that exhibit performance bottlenecks, rather than uniformly increasing resources across the entire system. By identifying specific deterioration factors and targeting only the affected areas, the system maintains high overall resource utilization while stabilizing performance where needed
Solution Approach 2:
The system implements dynamic resource allocation that automatically adjusts resource distribution based on real-time performance conditions. Resources are allocated flexibly according to actual workload demands and performance requirements, ensuring performance stability while maximizing resource use efficiency through adaptive rather than static allocation
Data Source
AI summary
In a cloud environment where multiple applications having different performance characteristics are operated in a mixture, in order to specify the cause of performance deterioration and to solve the confliction of requests related to a shared resource, the present invention detects the performance deterioration of resources allocated to multiple instances constituting a logical server configuration, and extracts an instance sharing resources existing performance deterioration, and compares a performance tendency of the extracted instance and a performance pattern showing a characteristics extracted from a performance information of the resource in association with a bottleneck factor, to thereby estimate the bottleneck factor by calculating mutual similarities and determining a performance pattern where the calculated similarity becomes maximum.