By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD’s task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD’s transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption.

中文翻译：

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具有通信和计算动力学的MEC系统中基于在线学习的计算分流

通过将任务从移动设备（MD）卸载到附近的已部署接入点（AP），每个接入点都连接到一台服务器进行任务处理，计算卸载可以在MD的任务执行延迟和移动边缘计算的能耗之间取得平衡（MEC）系统。考虑到MEC系统中的通信和计算动态，我们旨在设计在线计算卸载机制，以在平均能耗约束下将时间平均预期任务执行延迟最小化。首先，利用已知的MD和AP之间的当前信道增益以及MEC服务器上的可用计算能力，我们利用Lyapunov优化框架对MD的发射功率分配和MEC服务器选择做出最佳的一时隙决策。在此基础上，然后，我们考虑一个更现实的情况，即难以捕获MEC服务器上的当前可用计算能力，并且难以将多臂匪盗框架组合在一起以用于基于在线学习的MEC服务器选择算法。最后，通过理论分析和广泛的仿真，我们证明了我们提出的算法的接近最优性和可行性，并提出了我们提出的算法充分探索了通信与计算之间的相互作用，从而丰富了用户体验并降低了能耗。