计算资源受限的移动边缘计算服务器收益优化策略

移动边缘计算（MEC）服务器通过向用户提供计算资源获得收益。对MEC服务器而言，如何在计算资源受限的情况下提高自身收益至关重要，为此提出一种通过优化计算任务执行次序提高MEC服务器收益的策略。首先，将MEC服务器收益最大化问题建模为以任务执行次序为优化变量的优化问题；然后提出了一种基于分支定界法的算法求解任务执行次序。仿真结果表明，采用所提算法获得的MEC服务器平均收益分别比大任务优先（LTF）算法、低延迟任务优先（LLTF）算法和先到先服务（FCFS）算法提高了11%、14%和21%。在保证卸载用户服务质量（QoS）同时，所提策略可以显著提高服务器的收益。

Revenue maximization strategy for mobile-edge computing server with limited computing resources

Mobile Edge Computing (MEC) servers receive revenue by leasing computing resources to users. Improving revenue with limited computing resources is critical for MEC servers. Therefore, a strategy of improving MEC server revenue by the optimization of computing task execution order was proposed. Firstly, the revenue maximization problem of servers was modeled to an optimization problem with task execution order as optimization parameter. Then, an algorithm based on the branch and bound approach was proposed to find the optimal task execution order. Simulation results show that the average revenue of MEC server of the proposed algorithm is 11%, 14% and 21% higher than those of Large Task First (LTF) algorithm, Low-Latency Task First (LLTF) algorithm, and First Come First Served (FCFS) algorithm respectively. The proposed strategy can significantly improve the servers’ revenue while guaranteeing offloading users’ Quality of Service (QoS).