Energy-efficient offloading of real-time tasks using cloud computing

Abstract

With the increasing number of sophisticated power-intensive applications, embedded systems require energy-efficient computing devices. Most real-time applications of embedded systems are subject to timing constraints that should be met for the applications to run properly. Unfortunately, local computational devices have limited computation and storage capacity. Moreover, the real-time applications that perform complex computations consume large amounts of power. Therefore, offloading the power-intensive computational tasks to a more powerful entity is an efficient technique to overcome the limited computational resources of local devices and reduces the overall power consumption. In this paper, we propose two algorithms for making an efficient offloading decision for soft and weakly hard (firm) real-time applications while guaranteeing the schedulability of tasks. We have performed different experiments and investigated the technical and economic feasibility of using offloading to perform various processes that require different computational power. Experimental results show that significant power can be saved by offloading the resources of the power-intensive applications into the cloudlet for weakly hard real-time tasks and and cloud for soft real-time tasks.