Energy-Efficient Mapping and Scheduling of Task Interaction Graphs for Code Offloading in Mobile Cloud Computing

Abstract

To reduce the energy consumption in mobile devices, intricate applications are divided into several interconnected partitions like Task Interaction Graph (TIG) and are of floaded to cloud resources or nearby surrogates. Dynamic Voltage and Frequency Scaling (DVFS) is an effective technique to reduce the power consumption during mapping and scheduling stages. Most of the existing research works proposed several task scheduling solutions by considering the voltage/frequency scaling at the scheduling stage alone. But, the efficacy of these solutions can be improved by applying the DVFS in both mapping as well as scheduling stages. This research work attempts to apply DVFS in mapping as well as scheduling stages by combining both the task-resource and resource-frequency assignments in a single problem. The idea is to estimate the worst-case global slack time for each task-resource assignment, distributes it over the TIG and slowing down the execution of tasks using dynamic voltage and frequency scaling. This optimal slowdown increases the computation time of TIG without exceeding its worst-case completion time. Further, the proposed work models the code offloading as a Quadratic Assignment Problem (QAP) in Matlab-R2012b and solves it using two-level Genetic Algorithm (GA) of the global optimization toolbox. The effectiveness of the proposed model is assessed by a simulation and the results conclude that there is an average energy savings of 35% in a mobile device.