A cloudlet is a small-scale cloud datacenter deployed at the network edge to support mobile applications in proximity with low latency. While an individual cloudlet operates on moderate power, cloudlet clusters are well-suited candidates for emergency demand response (EDR) scenarios due to substantial electricity consumption and job elasticity: mobile workloads in the edge often exhibit elasticity in their execution. To efficiently carry out edge EDR via cloudlet cluster control, two fundamental problems need to be addressed: how to incentivize the participation of cloudlet clusters and how to schedule and allocate workloads in each cluster to satisfy EDR requirements. We propose a two-stage control scheme, consisting of (i) an auction mechanism to motivate clusters’ voluntary energy reduction and select participants with the minimum social cost and (ii) an online task scheduling algorithm for chosen clusters to dispatch workloads to guarantee target EDR power reduction. Using the primal-dual optimization theory, we prove that our control scheme is truthful, individually rational, runs in polynomial time, and achieves near-optimal performance. Large-scale simulation studies based on real-world data also confirm the efficiency and superiority of our scheme over state-of-the-art algorithms.

cloudlet是部署在网络边缘的小规模云数据中心，用于支持低延迟附近的移动应用程序。尽管单个小云以中等功率运行，但由于大量的电力消耗和工作弹性，小云集群非常适合用于紧急需求响应（EDR）场景：边缘移动工作负载通常在执行过程中表现出弹性。为了通过cloudlet群集控制有效地执行边缘EDR，需要解决两个基本问题：如何激励cloudlet群集的参与以及如何调度和分配每个群集中的工作负载以满足EDR要求。我们提出了一个两阶段的控制方案，包括（i）拍卖机制以激励集群的自愿性节能并选择具有最低社会成本的参与者，以及（ii）在线任务调度算法，用于所选集群分配工作量以确保目标EDR功耗降低。使用原始对偶优化理论，我们证明了我们的控制方案是真实的，个体有理的，在多项式时间内运行，并实现了接近最佳的性能。基于实际数据的大规模仿真研究也证实了我们的方案相对于最新算法的效率和优越性。并达到接近最佳的效果。基于实际数据的大规模仿真研究也证实了我们的方案相对于最新算法的效率和优越性。并达到接近最佳的效果。基于实际数据的大规模仿真研究也证实了我们的方案相对于最新算法的效率和优越性。