Service Provisioning Based on Edge-Cloud Collaboration: A Two-Timescale Online Scheduling Algorithm

With the development of 5G network and edge computing technology, service providers can deploy applications on the edge cloud close to the users to improve service quality and efficiency. However, due to the limited number of edge resources and the dynamic change of user requests over time, it remains challenging for service providers to attain optimal resource procurement and job processing decisions at an edge data center. The computing capacity of public cloud resources is generally unlimited, but it is difficult to achieve low-latency performance similar to the edge resources and job transmission also requires high network bandwidth costs. In this article, we consider service providers using the edge-cloud collaborative mode to take advantage of the low-latency characteristics of edge resources and improve their scalability. Service providers purchase resources at the cloud and edge, respectively, to deploy applications, so as to satisfy dynamic service requests with different service quality requirements. In order to optimize the cost of service providers while ensuring the quality of their services, we propose a two-timescale online scheduling algorithm based on the Lyapunov optimization, which makes optimal decisions without knowing future information about users' jobs. By determining the number of edge resources at a large time scale and the number of cloud resources at a small time scale, our algorithm can avoid frequent application deployments at edge while achieving rapid service response in a cost-effective manner. Rigorous theoretical analysis and extensive experiments based on both the synthetic and real-world data verify the effectiveness of our proposed algorithm.