Application Influence Factor Calculation for Proactive Resource Allocation
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Solution Overview
Problem
Conventional monitoring systems in computing systems are reactive and fail to proactively detect and address performance issues, as they primarily focus on detecting problems after they occur, rather than predicting or mitigating them in real-time.
Innovation Solution
A computer-implemented method that collects instrumentation data from multiple time segments, calculates performance and robustness values, generates health-waveforms, determines influence-factors between applications using Pearson correlation coefficients, and adjusts resource allocation based on these factors to anticipate and address potential system failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional monitoring systems evaluate real-time data against thresholds to detect errors, then error detection capability is improved, but the system remains reactive and cannot predict performance issues before they occur
Solution Approach 1:
The system performs preliminary actions by continuously collecting instrumentation data and generating health-waveforms that predict future system state. By analyzing trends in performance values and robustness values over multiple time-segments, the system anticipates potential failures before they occur, enabling proactive resource reallocation rather than reactive response after errors are detected
Solution Approach 2:
The system implements feedback by continuously monitoring application performance, calculating influence-factors between applications, and dynamically adjusting resource allocation based on this feedback loop. The health-waveform generation creates a continuous feedback mechanism that shows how current resource allocation affects future system performance, allowing the system to self-optimize
2Reliability
If monitoring systems collect and analyze data from all applications, then comprehensive system monitoring is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the essential information needed for monitoring by generating condensed health-waveforms that represent application performance trends. Instead of storing and analyzing all raw instrumentation data from all applications, the system extracts key performance and robustness values, transforms them into polar coordinates, and creates simplified waveform representations that capture the essential system state with minimal data storage requirements
Solution Approach 2:
The system changes parameters by transforming performance and robustness values into polar coordinates (magnitude and phase angle), which simplifies the data representation. This parameter transformation enables efficient storage and comparison of health-waveforms while maintaining the ability to detect performance issues and calculate influence-factors between applications
3Productivity
If resource allocation is adjusted dynamically based on application influence, then system performance optimization is improved, but the complexity of resource management increases
Solution Approach 1:
The system implements self-service by automatically calculating influence-factors between applications and dynamically reallocating resources without human intervention. The health-waveform analysis and resource allocation adjustments occur autonomously based on the computed influence relationships, allowing the system to self-optimize its performance while reducing the complexity of manual resource management
Solution Approach 2:
The system applies dynamics by making resource allocation flexible and adaptive rather than static. Resource allocation changes dynamically in response to computed influence-factors and health-waveform trends, allowing the system to automatically adjust to changing workload conditions and optimize performance in real-time
Data Source
AI summary
A computer-implemented method for determining influence of applications on system performance includes collecting, by a processor, for several applications that are executing on a computing system, respective instrumentation data during multiple time-segments. The method further includes determining, for each of the applications, a performance value and a robustness value for each of the time-segments based on the respective instrumentation data. Further, using the performance value and robustness value for each time-segment, multiple health-waveforms are generated, where a health-waveform is generated for each respective application. The method further includes determining, by the processor, an influence-factor of a first application on a second application, the first application and the second application are executing on the computing system. The method further includes adjusting, by the processor, allocation of a computer resource by releasing the computer resource from the first application and allocating the computer resource to the second application based on the influence-factor.