FairEdge: A Fairness-Oriented Task Offloading Scheme for Iot Applications in Mobile Cloudlet Networks

Abstract

Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge mobile cloud environment are provided by small-scale cloud infrastructures such as cloudlets. While offloading tasks to in-proximity cloudlets enjoys benefits of lower latency and smaller energy consumption, new issues related to the cloudlets are rising. For instance, unbalanced task distribution and huge load gaps among heterogeneous mobile cloudlets are becoming more challenging, concerning the network dynamics and distributed task offloading. In this paper, we propose ‘FairEdge’, a Fairness-oriented computation offloading scheme to enable balanced task distribution for mobile Edge cloudlet networks. By integrating the balls-and-bins theory with fairness index, our solution promotes effective load balancing with limited information at low computation cost. The evaluation results from extensive simulations and experiments with real-world datasets show that, FairEdge outperforms conventional task offloading methods, and it can achieve a network fairness up to 0.85 and reduce the unbalanced task offload by 50%.