Adaptive Periodic Task Scheduling for Computing Resource Optimization
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Solution Overview
Problem
Efficiently scheduling recurring tasks in computing systems to balance resource usage and task frequency, as increasing task frequency can divert resources from performing additional operations, leading to inefficiencies.
Innovation Solution
Determining a suitable periodicity for recurring tasks based on configuration data, such as minimum and maximum intervals, and the nature of the operation, to ensure the task executes with sufficient frequency to capture data changes without excessive resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the frequency of executing a recurring task is increased to capture data changes with finer granularity, then the measurement precision of data changes is improved, but the use of computing resources (logic resources and time) increases, leading to system inefficiency
Solution Approach 1:
The patent applies dynamics by making the task execution frequency adaptive rather than fixed. The system dynamically adjusts the periodicity of recurring tasks based on observed data change patterns, configuration parameters, and system state. This allows the system to execute tasks more frequently when data changes are detected and less frequently when data is stable, thereby improving measurement precision when needed while reducing resource consumption during stable periods.
Solution Approach 2:
The patent changes the parameter of execution frequency from a static value to a dynamic parameter that can be adjusted based on system conditions. By modifying the periodicity parameter according to data change detection results, configuration data, and performance metrics, the system achieves finer granularity of data change detection only when necessary, thus resolving the contradiction between measurement precision and resource usage.
2Reliability
If the frequency of executing a recurring task is increased to improve detection of data changes, then the reliability of data change detection is improved, but the productivity of additional operations decreases due to resource diversion
Solution Approach 1:
The patent implements periodic action with variable periods. Instead of executing tasks at fixed intervals, the system uses configurable minimum and maximum intervals to create adaptive periodic execution. The task executes periodically with adjusted frequency based on data change patterns, ensuring reliable detection when changes occur while maintaining productivity during stable periods by reducing execution frequency.
Solution Approach 2:
The patent applies partial action by executing the recurring task only when necessary -即 when data changes are detected or suspected. Rather than continuously executing at high frequency, the system performs partial executions at optimized intervals, achieving sufficient detection reliability for actual data changes while avoiding excessive executions that would harm productivity of additional operations.
Data Source
AI summary
A computing system may run a recurring task, which may use resources, such as logic resources and time, to operate on and/or with a set of data. Accordingly, the frequency at which the recurring task is executed may limit the performance and/or efficiency of the computing system. As such, a scheduler routine may, based on configuration information associated with the recurring task and/or the set of data, schedule the recurring task with a periodicity that may improve the performance and/or efficiency of the computing system.