DM2-ECOP

Abstract

Mobile Edge Computing is a promising paradigm that can provide cloud computing capabilities at the edge of the network to support low latency mobile services. The fundamental concept relies on bringing cloud computation closer to users by deploying cloudlets or edge servers, which are small clusters of servers that are mainly located on existing wireless Access Points (APs), set-top boxes, or Base Stations (BSs). In this article, we focus on computation offloading over a heterogeneous cloudlet environment. We consider several users with different energy—and latency-constrained tasks that can be offloaded over cloudlets with differentiated system and network resources capacities. We investigate offloading policies that decide which tasks should be offloaded and select the assigned cloudlet, accordingly with network and system resources. The objective is to minimize an offloading cost function, which we defined as a combination of tasks’ execution time and mobiles’ energy consumption. We formulate this problem as a Mixed-Binary Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation-based heuristic approach that relies on the Lagrangian decomposition method. To solve the subproblems, we also propose a greedy heuristic algorithm that computes the best cloudlet selection and bandwidth allocation following tasks’ offloading costs. Numerical results show that our offloading policy achieves a good solution quickly. We also discuss the performances of our approach for large-scale scenarios and compare it to state-of-the-art approaches from the literature.