Optimized Design for Multiuser Cache-enabled Mobile Edge Computing

The energy-efficient caching strategy and computation offloading design of mobile edge computing (MEC) faces the double randomness challenges, which require to adapt to the time-varying wireless channel state and the dynamic task arrivals. This paper investigates a cache-enabled mobile edge computing system with dynamic tasks arriving at multiple wireless devices. By minimizing the system weighted sum energy over multiple time slots, we optimize the AP's task caching decision and MEC execution, the wireless devices’ local computing and tasks offloading under the caching capacity and computation causality, and the computation deadline constraints. The branch-and-bound (BnB) method is first presented to obtain the globally optimal solution to define the lower bound for practical schemes. Then, a relaxation-based scheme is proposed to efficiently achieve a near-optimal solution. Numerical results show that the proposed relaxation-based scheme achieves a closer performance to the optimal BnB scheme when compared to the benchmark schemes.