Optimizing computation offloading under heterogeneous delay requirements for wireless powered mobile edge computing

This paper studies the optimization design of a wireless powered mobile edge computing (WP-MEC) system with multiple edge servers, in which the heterogeneous delay requirements for tasks are considered. To achieve optimal computing performance of the WP-MEC, a computation rate maximization problem is formulated by jointly optimizing wireless power transmission time, offloading decision and resources allocation. It is a mixed-integer nonlinear programming problem that is NP-hard. In order to solve the problem, we decompose it into two sub-problems, a multiple knapsack sub-problem of user tasks offloading and a one-dimensional optimization sub-problem of wireless power transmission time. The multiple knapsack sub-problem is then solved using an integer encoding differential evolution algorithm. The algorithm can adequately consider various combinations of items among multiple feasible knapsacks, thus making it easier to find the solution with largest overall profit. Moreover, a multiple intervals golden-section search algorithm is designed for the one-dimensional optimization sub-problem of wireless power transmission time. The algorithm evaluates the given wireless power transmission time using the optimal solution to the corresponding multiple knapsacks problem, and can find the optimal wireless power transmission time by jointly optimizing multiple intervals. Next, a new computation offloading scheme, MKCTO, is proposed by integrating the two algorithms. Finally, the performance of MKCTO is verified by extensive numerical experiments and compared with other four benchmark schemes. The results show that MKCTO can achieve satisfactory performance of computation offloading, and outperform the other four schemes.