Maximizing Mobiles Energy Saving Through Tasks Optimal Offloading Placement in two-tier Cloud

Abstract

In this paper, we focus on tasks offloading over two tiered mobile cloud computing environment. We consider several users with energy constrained tasks that can be offloaded over cloudlets or on a remote cloud with differentiated system and network resources capacities. We investigate offloading policy that decides which tasks should be offloaded and determine the offloading location on the cloudlets or on the cloud. The objective is to minimize the total energy consumed by the users. We formulate this problem as a Non-Linear Binary Integer Programming. Since the centralized optimal solution is NP-hard, we propose a distributed linear relaxation heuristic based on Lagrangian decomposition approach. To solve the subproblems, we also propose a greedy heuristic that computes the best cloudlet selection and bandwidth allocation following tasks' energy consumption. We compared our proposal against existing approaches under different system parameters (e.g. CPU resources), variable number of users and for six applications, each having specific traffic pattern, resource demands and time constraints. Numerical results show that our proposal outperforms existing approaches. We also analyze the performance of our proposal for each application.