Task Interference Detection via Performance Correlation Scoring
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Solution Overview
Problem
In large-scale parallel processing systems, tasks often interfere with each other due to shared resource contention, leading to degraded performance, as one task may require more CPU or memory resources than others, causing increased latency or CPU cycles, which existing systems fail to effectively manage.
Innovation Solution
A method is introduced to identify poorly performing tasks by collecting performance data, determining scores for other tasks based on their correlation with the degraded task, ranking potential causes of interference, and modifying the execution of the highest-ranked interfering task, such as throttling or rescheduling it, to improve the performance of the affected task.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple tasks are executed in parallel on the same computing device, then system productivity increases, but task performance degrades due to shared resource contention
Solution Approach 1:
The patent introduces a performance management system that acts as an intermediary between conflicting tasks. This system monitors performance metrics, identifies interfering tasks through correlation analysis, and implements mitigation strategies such as rescheduling or resource allocation adjustments, thereby resolving the contradiction between maintaining high productivity and ensuring reliable task performance
Solution Approach 2:
The system continuously collects performance data from executing tasks and uses this feedback to identify performance degradation patterns. By analyzing correlations between task performance metrics and identifying interfering tasks, the system dynamically adjusts resource allocation and scheduling decisions to maintain both productivity and task performance reliability
2Speed
If a single task consumes greater share of CPU and memory resources, then its execution speed improves, but other tasks experience increased latency and CPU cycles
Solution Approach 1:
The system dynamically changes resource allocation parameters based on real-time performance monitoring. When performance degradation is detected, the system adjusts CPU and memory allocation parameters for identified interfering tasks, balancing resource distribution to reduce latency while maintaining acceptable execution speeds for all tasks
Solution Approach 2:
The patent implements dynamic resource management where resource allocation is not static but continuously adjusted based on system state. The performance management system monitors execution patterns and dynamically reschedules tasks or adjusts resource shares, transforming the rigid resource allocation into a flexible, adaptive system that responds to changing workload conditions
3Reliability
If performance data is collected and analyzed to identify interfering tasks, then task performance management improves, but system complexity increases
Solution Approach 1:
The performance management system implements self-service mechanisms by automatically collecting performance data, analyzing correlations, identifying interfering tasks, and implementing mitigation strategies without requiring manual intervention. This automation reduces the operational complexity burden on system administrators while maintaining reliable performance management
Solution Approach 2:
The patent introduces a specialized performance management system as an intermediary layer that handles the complexity of performance monitoring and analysis. This intermediary absorbs the computational and analytical complexity, presenting a simplified interface to users while managing the sophisticated correlation analysis and task identification processes in the background
Data Source
AI summary
Among other disclosed subject matter, a computer-implemented method includes receiving an indication that execution of an instance of a first task is degraded relative to a performance threshold associated with the first task. Performance data associated with the execution of the instance of the first task and performance data associated with execution of a plurality of additional tasks executed on the computing device are collected. For each of the plurality of additional tasks, the method includes determining a score for the respective additional task based on the performance data associated with the execution of the instance of the first task and performance data associated with the respective additional task. The method includes identifying one or more additional tasks as a potential cause of degraded performance based on the scores associated with each of the plurality of additional tasks. The method includes modifying an execution of a particular identified task.