By executing offloaded tasks from mobile users, edge computing augments mobile user equipments (UEs) with computing/communications resources from edge nodes (ENs), enabling new services (e.g., real-time gaming). However, despite being more resourceful than UEs, allocating ENs' resources to a given favorable set of users (e.g., closer to ENs) may block other UEs from their services. This is often the case for most existing approaches that only aim to maximize the network social welfare or minimize the total energy consumption but do not consider the computing/battery status of each UE. This work develops an energy-based proportional-fair framework to serve all users with multiple tasks while considering both their service requirements and energy/battery levels in a multi-layer edge network. The resulting problem for offloading tasks and allocating resources toward the tasks is a Mixed-Integer Nonlinear Programming, which is NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branch-and-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decisions and multiple subproblems (SPs) for resource allocation. To quickly eliminate inefficient offloading solutions, MP is integrated with powerful Benders cuts exploiting the ENs' resource constraints. We then develop a dynamic branch-and-bound algorithm (DBB) to efficiently solve MP considering the load balance among ENs. SPs can either be solved for their closed-form solutions or be solved in parallel at ENs, thus reducing the complexity. The numerical results show that DBBD returns the optimal solution in maximizing the proportional fairness among UEs. DBBD has higher fairness indexes, i.e., Jain's index and min-max ratio, in comparison with the existing ones that minimize the total consumed energy.
翻译:通过执行移动用户的卸载任务,边缘计算通过边节点(ENs)的计算/通信资源增加移动用户设备(UEs),实现新的服务(例如实时游戏 ) 。然而,尽管比Ues更有智慧,但将ENs的资源分配给给给定的有利用户群(例如更接近ENs)可能会阻碍其他用户的服务。对于多数现有办法来说,这些办法的目的只是最大限度地提高网络的复杂程度社会福利或将能源消耗总量降至最低,但不考虑每个Ues的计算/电池状态。这项工作开发了一个基于能源的按比例公平框架,为所有用户提供多种任务(例如实时游戏游戏),尽管它们比Ues更有智慧,但是将ENES的资源分配到多层边缘网络,由此产生的问题就是卸载任务和将非线性编程编程编程(我们利用宽松的问题解决了内部的节流问题,并提议一个分布式的算法,即以动态版和节流的节流的节流法,从而让IMFS-D(现在的IMFD) 将资源递减到IMFD 。