A Predictive Energy Consumption Scheduling Algorithm for Multiprocessor Heterogeneous System

Abstract

With the increasing number of users and application in the era of the Industrial Internet of Things (I-IoT), computing efficiency and energy consumption become two vital problems. Multi-processor heterogeneous system has been widely used for handling parallel workflow applications to advance the performance of system, in which efficient workflow scheduling and the choice of frequency for each task play key roles. However, most scheduling algorithms just focus on an optimal solution for every single task and may exclude optimal task priority sequence at the task sorting phase. Therefore, this paper proposes a Predictive Energy Consumption Scheduling (PECS) algorithm to match the frequency for each task. Firstly, the alterable time space is introduced to decompose the deadline of the whole workflow application into each task. Secondly, as the basis of allocation, Predictive Energy Consumption Matrix (ECM) is defined to predict the impact of current task allocation on the energy consumption of subsequent tasks. Thirdly, the processor with the lowest predicted energy consumption is matched for each task to form a preliminary scheduling scheme with the constraint of time. Finally, all tasks in the same priority level of a processor are swapped in order, then a scheduling strategy is formed to minimize energy consumption. Experimental results reveal that our proposed algorithm can reduce energy 48.28% and 13.33% averagely compared with other two state of the art algorithms.