Exploiting Dynamic Workload Variation in Low Energy Preemptive Task Scheduling

TL;DR

This paper presents a novel offline voltage scheduling method that exploits workload variation to significantly reduce energy consumption in preemptive systems, achieving up to 60% savings.

Contribution

It introduces a new energy-efficient scheduling approach that leverages workload variation and slack utilization through NLP optimization for offline and online voltage scaling.

Findings

Up to 60% energy reduction compared to static worst-case scheduling.

Schedule effectively exploits workload variation for energy savings.

Improves energy efficiency in preemptive task systems.

Abstract

A novel energy reduction strategy to maximally exploit the dynamic workload variation is proposed for the offline voltage scheduling of preemptive systems. The idea is to construct a fully-preemptive schedule that leads to minimum energy consumption when the tasks take on approximately the average execution cycles yet still guarantees no deadline violation during the worst-case scenario. End-time for each sub-instance of the tasks obtained from the schedule is used for the on-line dynamic voltage scaling (DVS) of the tasks. For the tasks that normally require a small number of cycles but occasionally a large number of cycles to complete, such a schedule provides more opportunities for slack utilization and hence results in larger energy saving. The concept is realized by formulating the problem as a Non-Linear Programming (NLP) optimization problem. Experimental results show that, by using the proposed scheme, the total energy consumption at runtime is reduced by as high as 60% for randomly generated task sets when comparing with the static scheduling approach only using worst case workload.