We consider the setting where a service is hosted on a third-party edge server deployed close to the users and a cloud server at a greater distance from the users. Due to the proximity of the edge servers to the users, requests can be served at the edge with low latency. However, as the computation resources at the edge are limited, some requests must be routed to the cloud for service and incur high latency. The system's overall performance depends on the rent cost incurred to use the edge server, the latency experienced by the users, and the cost incurred to change the amount of edge computation resources rented over time. The algorithmic challenge is to determine the amount of edge computation power to rent over time. We propose a deterministic online policy and characterize its performance for adversarial and stochastic i.i.d. request arrival processes. We also characterize a fundamental bound on the performance of any deterministic online policy. Further, we compare the performance of our policy with suitably modified versions of existing policies to conclude that our policy is robust to temporal changes in the intensity of request arrivals.
翻译:我们考虑了在靠近用户的第三方边缘服务器和距离用户更远的云层服务器上托管服务的设置。由于边缘服务器与用户的距离很近,请求可以在低潜伏的边缘得到满足。然而,由于边缘的计算资源有限,有些请求必须经过云端获得服务,并具有高潜伏性。该系统的总体性能取决于使用边端服务器的租金成本、用户经历的延缓度,以及改变长期租用的边际计算资源数量的成本。算法上的挑战在于确定边际计算能力与租赁时间的距离。我们提出一个确定性在线政策,并描述其对于对抗性和随机性i.d.要求到达程序的作用。我们还将任何确定性在线政策的执行情况与我们的政策执行情况与经适当修改的现有政策版本加以比较,以得出结论我们的政策是否稳健地适应了请求抵达时间的强度的变化。