Resource Allocation Method for Minimizing Total Computation Time in Multi-Task Mobile Edge Computing Systems

Aiming at the the characteristic of dependency relationship existing among multiple tasks, a resource allocation strategy for multi-task mobile edge computing systems is investigated in this paper. Sequential dependency relationship among multiple tasks is taken into account. When the current task completes offloading, the next task can be offloaded without waiting for the current task to finish computing. By using a two-tier offloading strategy, when the edge server in small base station has insufficient computing capacity, the offloading task could be further divided and offloaded to the edge server in macro base station with sufficient computation resources. The resource allocation problem is formulated as an optimization problem. The objective is to minimize the total computation time of the overall system under the constraints of computing capability range of user, maximal computing resource of edge server, and maximal transmitting power of user. To solve the formulated optimization problem, a suboptimal solution is obtained by adopting a quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm has less computation time compared with the standard particle swarm optimization algorithm.