Revenue-Maximized Offloading Decision and Fine-Grained Resource Allocation in Edge Network

For providing highly demanding services with powerful computational ability and ultra low-latency communication, mobile edge computing (MEC) has been recognized as a bright rising star among key technologies for the next-generation networking. Generally, jointly optimizing offloading decision and resource allocation in one multi-variable problem is complicated. To decrease computational scale and develop practicable strategy by splitting problems, we divide the workflow of MEC-enabled base station into two stages. First, through formulating a task offloading problem, we propose a low-complexity improved simulated annealing-based heuristic offloading decision (SAHOD) algorithm to maximize network revenue from the perspective of mobile network operator. Then, the optimal fine-grained resource allocation solution is obtained in closed forms via Lagrange duality decomposition method. Furthermore, an effective realtime sub-gradient-based resource allocation (SGRA) algorithm is presented to converge to a specific optimal allocation strategy within the adjustable accuracy. For given users, simulation results show that our SAHOD algorithm can earn about 20.5% more revenue than value-based greedy algorithm. Besides, our SGRA algorithm can converge within 4 iterations and obtain approximately 19.3% more sum rates than static scheduling method.