In mobile edge computing (MEC) systems, users offload computationally intensive tasks to edge servers at base stations. However, with unequal demand across the network, there might be excess demand at some locations and underutilized resources at other locations. To address such load-unbalanced problem in MEC systems, in this paper we propose virtual machines (VMs) sharing across base stations. Specifically, we consider the joint VM placement and pricing problem across base stations to match demand and supply and maximize revenue at the network level. To make this problem tractable, we decompose it into master and slave problems. For the placement master problem, we propose a Markov approximation algorithm MAP on the design of a continuous time Markov chain. As for the pricing slave problem, we propose OPA - an optimal VM pricing auction, where all users are truthful. Furthermore, given users' potential untruthful behaviors, we propose an incentive compatible auction iCAT along with a partitioning mechanism PUFF, for which we prove incentive compatibility and revenue guarantees. Finally, we combine MAP and OPA or PUFF to solve the original problem, and analyze the optimality gap. Simulation results show that collaborative base stations increases revenue by up to 50%.
翻译:在移动边缘计算系统(MEC)中,用户将计算密集的任务卸下,在基站上将服务器排挤。然而,由于整个网络需求不均,某些地点的需求可能过多,其他地点的资源可能利用不足。为解决MEC系统中的这种负载不平衡问题,我们在本文件中提出虚拟机器(VMs)在基站之间共享。具体地说,我们考虑各基站的 VM 联合定位和定价问题,以匹配供需并最大限度地增加网络一级的收入。为了使这一问题易于处理,我们将其分解为主控和奴隶问题。关于职位安排问题,我们建议对持续时间马尔科夫链的设计采用Markov 近似算法。关于给奴隶定价问题,我们建议OPA - 最佳VM定价拍卖,所有用户都讲实话。此外,鉴于用户潜在的不真实行为,我们建议采用奖励兼容性拍卖iCAT,同时使用分配机制PUFF,我们证明这是激励兼容性和收入的保证。最后,我们将MAPA和POFF或PUFF MAPAM组合在一起, 来解决原始问题,并分析最佳收入差距。我们通过50 % 基本结果显示合作税将提高收入差距。