Self-adaptive threshold-based energy-saving scheduling method in heterogeneous distributed system

Abstract

The invention relates to a self-adaptive threshold-based energy-saving scheduling method in an environment of supporting a heterogeneous distributed system, belonging to the technical field of parallel task scheduling of the heterogeneous distributed system. The method provided by the invention specifically comprises the steps of: reading a parallel task directed acyclic graph (DAG) file; obtaining an initial task scheduling sequence; obtaining an optimal threshold: dynamically obtaining an optimal threshold according to parallel task and system environment; grouping the tasks: controlling task duplication by using the optimal threshold, namely, selectively duplicating optimal precursors of the tasks to balance system performance and energy consumption to obtain approximate optimal grouping; mapping the tasks: scheduling all groups into a processor, wherein the processor is not distributed and has minimum energy consumption; and regulating the voltage of the processor: dynamically regulating the voltage of the processor by using a task idle time to reduce the energy consumption of the processor. According to the invention, requirements on system performance and energy consumption are comprehensively considered. The optical threshold in the method can self-adapt the parallel tasks and the system environment, and is used for controlling the task duplication for balancing the system performance and the energy consumption, so that the energy consumption is reduced as far as possible under the premise that the final scheduling result meets the requirement of the system performance.

Description

technical field [0001] The invention relates to an energy-saving scheduling method for a computer system, in particular to an energy-saving scheduling method based on an adaptive threshold in a heterogeneous distributed system. Background technique [0002] A heterogeneous distributed system (HDS) is usually composed of many computing resources with different processing capabilities, and these computing resources are connected to each other through a high-speed network that can meet various application requirements. Over the past few years, heterogeneous distributed systems have become very popular computing platforms, serving a variety of computing needs for both computation-intensive and communication-intensive tasks. [0003] Heterogeneous distributed systems have been widely used in many fields such as industry and commerce. However, heterogeneous distributed systems consume huge amounts of energy while providing powerful computing capabilities. According to the UN (En...

AI Technical Summary

Problems solved by technology

The deficiencies are as follows: ① Some methods only consider performance and completely ignore energy consumption; ② Some energy-saving scheduling methods based on task replication use thresholds to control task replication, but the given thresholds are set arbitrarily and cannot be based on Adaptive adjustment of parallel tasks and system environment leads to unstable scheduling results; ③Although some methods consider both performance and processor energy consumption, they ignore network communication energy consumption

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