Budget-Aware Equilibrium Offloading for Mobile Edge Computing

Recently, Mobile Edge Computing (MEC) has emerged as a promising paradigm to provide the customized service to the users. MEC aims at enhancing the user experience by migrating intensive computation to the geographically proximal edge node. The base stations (BSs) in the MEC have limited computation capacity, and the maintaining also incurs extra cost. An incentive allocation strategy is critical to balance the maintaining consumption and task requirement. We introduce a multi-user and multi-BS MEC system, and there is a budget constraint for the edge nodes. We address the problem of finding the allocations of tasks to BSs and the optimal equilibrium price, such that the total utility performance of task is maximized, and the constraints can be satisfied in terms of cost budget. The problem is formalized as an optimization problem, and computation complexity is proved to be NP-Complete. We provide a greedy heuristic based polynomial-time approximate algorithm for offloading. Simulation results show that the offloading scheme is important for the tradeoff of budget and task requirement.