Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. In addition, edge cloud servers must make allocation decisions with only limited information available, since the arrival of future client tasks might be impossible to predict, and the states and behavior of neighboring servers might be obscured. We focus on a distributed resource allocation method in which servers operate independently and do not communicate with each other, but interact with clients (tasks) to make allocation decisions. We follow a two-round bidding approach to assign tasks to edge cloud servers, and servers are allowed to preempt previous tasks to allocate more useful ones. We evaluate the performance of our system using realistic simulations and real-world trace data from a high-performance computing cluster. Results show that our heuristic improves system-wide performance by $20-25\%$ over previous work when accounting for the time taken by each approach. In this way, an ideal trade-off between performance and speed is achieved.

翻译：边缘计算已成为一种非常普及的服务，它使移动设备能够借助基于网络的计算资源运行复杂任务。然而，边缘云通常资源受限，这使得资源分配成为一个具有挑战性的问题。此外，由于未来客户任务的到达可能无法预测，且相邻服务器的状态和行为可能不透明，边缘云服务器必须仅依据有限信息做出分配决策。我们聚焦于一种分布式资源分配方法，其中各服务器独立运行且互不通信，但通过与客户端（任务）的交互来做出分配决策。我们采用两轮竞标方法将任务分配给边缘云服务器，并允许服务器抢占先前任务以分配更重要的任务。我们使用真实仿真和高性能计算集群的实际轨迹数据评估系统性能。结果表明，在计入每种方法耗时的情况下，我们的启发式方法相比先前研究工作可将系统整体性能提升20-25%。由此，在性能与速度之间实现了理想平衡。