Energy-Performance Balancing Task Scheduler for Asymmetric Platforms

Abstract

Sustainability is a growing concern for society and computer science is no exception. Power consumption by computers may be reduced by lowering the frequency of the processor as well as meticulously limiting the hardware resource usage. An optimally energy efficient computation may, however, cause an impractically long execution time. Previous work has successfully provided a framework that minimizes energy consumption using task-based computation. One way to develop the framework concerns efforts to strike a balance between performance and energy efficiency to find the optimal trade-off between increased execution time and reduced energy cost. An option to utilize such a trade-off could incentivize a greater adoption of aforementioned energy reduction techniques. This thesis presents various efforts to modify an existing energy efficient task scheduling framework in order to balance energy efficiency and performance. The framework was further generalized and tested on multiple platforms for the sake of affirming its generic applicability. The Simics hardware simulator was assessed in hopes of enabling the possibility to test the framework on a myriad of virtual platforms. The evaluation shows that the modified framework can successfully determine the task scheduling decisions that yield optimal trade-off between performance and energy efficiency. After some additional modifications, the framework could seamlessly run on other platforms than the one it was designed for. Although the attempts to use the framework within the select virtual environment were somewhat futile, promising directions for future research were discovered.