In this work, we consider a multi-user mobile edge computing system with multiple computing access points (CAPs). Each mobile user has multiple dependent tasks that must be processed in a round-by-round schedule. In every round, a user may process their individual task locally, or choose to offload their task to one of the $M$ CAPs or the remote cloud server, in order to possibly reduce their processing cost. We aim to jointly optimize the offloading decisions of the users and the resource allocation decisions for each CAP over a global objective function, defined as a weighted sum of total energy consumption and the round time. We first present a centralized heuristic solution, termed MCAP, where the original problem is relaxed to a semi-definite program (SDP) to probabilistically generate the offloading decision. Then, recognizing that the users often exhibit selfish behavior to reduce their individual cost, we propose a game-theoretical approach, termed MCAP-NE, which allows us to compute a Nash Equilibrium (NE) through a finite improvement method starting from the previous SDP solution. This approach leads to a solution from which the users have no incentive to deviate, with substantially reduced NE computation time. In simulation, we compare the system cost of the NE solution with those of MCAP, MCAP-NE, a random mapping, and the optimal solution, showing that our NE solution attains near optimal performance under a wide set of parameter settings, as well as demonstrating the advantages of using MCAP to produce the initial point for MCAP-NE.
翻译:在这项工作中,我们考虑一个多用户移动边缘计算系统,它包含多个计算机接入点。每个移动用户都有多重依赖性任务,必须逐轮处理。在每一回合中,用户可以在当地处理他们的任务,或者选择将其任务卸到一个美元CAP或远程云服务器,以便降低其处理成本。我们的目标是共同优化用户的卸载决定和每个CAP资源分配决定,以履行一个全球目标功能,该功能被定义为能源消费总量和周期的加权总和。我们首先提出一个集中的超常性格解决方案,称为 MCAP,其中最初的问题可以放松到半确定性(SDP)程序,从而在概率上产生卸载决定。然后,认识到用户往往表现出自私的行为来降低其个人成本。 我们提议一种游戏理论方法,称为MCAP-NE,通过从以前的SDP解决方案开始的有限改进方法,即称之为 MCAP-CAP 。 这种方法导致最初的利差(SDAP)方案,在模拟中,我们用最优的解决方案来降低MCAS-MCL的汇率,在模拟中,我们用最优的模型来显示最优的解决方案。