We consider the problem of service hosting where an application provider can dynamically rent edge computing resources and serve user requests from the edge to deliver a better quality of service. A key novelty of this work is that we allow the service to be hosted partially at the edge which enables a fraction of the user query to be served by the edge. We model the total cost for (partially) hosting a service at the edge as a combination of the latency in serving requests, the bandwidth consumption, and the time-varying cost for renting edge resources. We propose an online policy called $\alpha$-RetroRenting ($\alpha$-RR) which dynamically determines the fraction of the service to be hosted at the edge in any time-slot, based on the history of the request arrivals and the rent cost sequence. As our main result, we derive an upper bound on $\alpha$-RR's competitive ratio with respect to the offline optimal policy that knows the entire request arrival and rent cost sequence in advance. We conduct extensive numerical evaluations to compare the performance of $\alpha$-RR with various benchmarks for synthetic and trace-based request arrival and rent cost processes, and find several parameter regimes where $\alpha$-RR's ability to store the service partially greatly improves cost-efficiency.
翻译:我们考虑了服务托管问题,即应用程序提供商可在其中动态租赁边缘计算资源,并为边缘用户请求提供服务,以提供更好的服务质量。这项工作的一个重要新颖之处是,我们允许在边缘部分托管服务,使用户查询的一小部分能够由边缘服务。我们将(部分)在边缘托管服务的总成本作为在服务请求、带宽消耗和租赁边缘资源的时间分配成本之间的延迟结合,作为在服务请求、带宽消耗和租赁资源的时间分配成本的离线最佳政策的一个模型。我们提出了一个名为$alpha$-retroenting (alpha$-RRR)的在线政策,以动态方式决定服务在任何时间段边缘的端端的服务份额,根据抵达请求的历史和租金顺序。我们的主要结果是,我们根据美元-RR的竞争性比率,在了解整个请求抵达和租赁成本序列的离线最佳政策方面,我们进行了广泛的数字评估,将美元-RR的绩效与各种基于合成和痕量要求效率的费率参数比。