Task Caching and Computation Offloading for Muti-User Mobile Edge Computing Network

Mobile edge computing (MEC) can sink the computation capability near the device terminal, reduce the task data backhaul delay and greatly relieve the pressure of the core network compared with cloud computing. With the exponential growth of delay sensitive application services, edge caching gradually rises as a promising technique by pre-storing data at edge server for user devices to be free from task data transmission and reduce edge computing cost. In this paper, an edge computing network model with caching mechanism is established to maximize system utility defined by weighted sum of energy consumption and execution delay. An alternating iteration algorithm based on modified binary particle swarm optimization algorithm (MBPSO) and convex optimization is proposed to solve the joint task caching and offloading optimization problem. Simulation results show that compared with other benchmark schemes, our proposed algorithm can effectively improve system performance.