Decision Tree-Based Approaches for Handling Offloading Decisions and Performing Adaptive Monitoring in MCC Systems

Abstract

Mobile cloud computing (MCC) has emerged as a solution to overcome the resource constraints of mobile devices by using computation offloading to execute mobile application tasks on remote servers, thus enhancing performance and reducing the energy consumption of mobile devices. Nevertheless, the effectiveness of an offloading solution is determined by its ability to infer when offloading will improve performance. In this context, several solutions have been proposed to handle computational offloading operations and the decisions of when and where to offload. The problem is that such decisions depend on periodic monitoring of several metrics and usually involve compute intensive task that, when executed on mobile devices, can contribute to overhead the system. Thus, this paper proposes a novel approach for handling offloading decisions using decision trees and an adaptive monitoring scheme that allows MCC systems to monitor only the metrics that are relevant to the offloading decision. The results show that computation offloading can be beneficial for improving the performance of mobile applications and the energy consumption of mobile devices can be reduced by using the proposed adaptive monitoring scheme.