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%.
Highlights
In recent years, with the rapid development of mobile computing technologies and pervasive proliferation of mobile devices, mobile traffic data has been growing at an unprecedented rate
While mobile applications are aggressively demanding in computation resources, mobile devices are still constrained by the limited capacities in the batteries, memory, and processers
FairEdge integrates the balls-and-bins theory [6] with fairness index [7] to achieve effective load balancing in mobile cloudlet networks
Summary
With the rapid development of mobile computing technologies and pervasive proliferation of mobile devices, mobile traffic data has been growing at an unprecedented rate. In comparison with remote cloud computing resources, the mobile cloudlets at edge networks can improve the task processing time significantly. It is of great importance to maintain load balancing among all mobile cloudlets at edge networks, so that each cloudlet’s computing resource can be fully exploited, and mobile users can have a quick response on their offloaded tasks. To deal with the above challenges, we propose FairEdge, a Fairness-oriented task offloading scheme for collaborative mobile cloudlets at Edge networks. FairEdge integrates the balls-and-bins theory [6] with fairness index [7] to achieve effective load balancing in mobile cloudlet networks. By leveraging the task load information and fairness index of two targeted neighbors, the proposed FairEdge scheme enables a more reasonable computation offloading decision for each cloudlet.
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