Parallel Work Process Capacity Calculation
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Solution Overview
Problem
Determining the optimal number of parallel work processes in computer systems, especially application servers, is challenging as setting it too high leads to slow performance and too low results in underutilized resources, making it difficult to balance processing efficiency and resource allocation.
Innovation Solution
An iterative process is used to calculate the number of parallel work processes by measuring execution times, starting with a single execution unit and incrementally adding more until a predetermined threshold is met, with the option to utilize machine learning for optimizing initial settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of parallel work processes is increased, then processing capacity is improved, but system performance deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of parallel work processes based on measured execution times. The system iteratively tests different numbers of execution units and selects the optimal parameter value that maximizes processing capacity while maintaining performance thresholds, thereby resolving the contradiction between productivity and reliability.
2Reliability
If the number of parallel work processes is decreased, then system performance is maintained, but resource utilization deteriorates
Solution Approach 1:
The patent implements feedback mechanisms by continuously measuring execution times of parallel work processes and using this information to determine the optimal number of execution units. The system monitors performance metrics and adjusts the number of parallel processes accordingly, ensuring both performance maintenance and optimal resource utilization through closed-loop control.
3Loss of time
If machine learning is used to predict optimal initial settings, then the number of iterations is reduced, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict optimal initial settings for the number of parallel work processes before actual execution begins. This pre-computation reduces the number of iterative measurements needed, trading off increased system complexity for significant time savings in determining optimal configuration.
Data Source
AI summary
In an example embodiment, an iterative process is used to calculate the number of parallel work processes to set for a computer system. Specifically, an execution unit is started and the execution time for that execution unit is measured. This execution time for the single execution unit is called “unit time.” Then a fixed number (e.g., 20) of execution units are started, and the execution times of each are measured. If the execution time consumption of each of the execution units is lower than some fixed threshold percentage of the unit time (e.g., 120%), then this means that the maximum parallel processing capacity is higher than the fixed number of execution unit. Then more execution units can be added and the process repeated until the execution units' execution times exceed that fixed threshold percentage.