Energy-Efficient Efficient Resource Allocation for Mobile Mobile-Edge Edge Computation Offloading
Abstract: Mobile-edge edge computation offloading (MECO) off off-loads loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capaci capacities ties of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time time-division division multiple access (TDMA) and orthogonal frequency frequency-division division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based threshold structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete plete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub--optimal resource-allocation allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer integer problem. To solve this challenging problem and characterize its policy structure, a low low-complexity sub-optimal optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. count The