GeoScale: Microservice Autoscaling With Cost Budget in Geo-Distributed Edge Clouds

Deploying microservice instances in geo-distributed edge clouds which are located at the network edge and in proximity to end-users can provide on-site processing, thereby improving the quality of service (QoS). To accommodate the time-varying request arrival rate of each edge cloud, the deployment scheme of microservice instances is dynamically adapted, which is called microservice autoscaling. However, existing studies on microservice autoscaling at the edge either only optimize the QoS without considering the cost of deploying microservice instances or simply focus on the cost per individual timeslot, and thus always severely violate the long-term budget constraint. To solve this problem, in this article, we propose GeoScale, a novel method that aims to optimize the average request response time under the long-term cost budget constraint. GeoScale first utilizes the Lyapunov optimization framework to decompose the long-term optimization problem into a series of per-timeslot sub-problems and then applies a signomial geometric programming (SGP)-based algorithm to obtain a near-optimal solution to each NP-hard sub-problem. Through extensive trace-driven experiments, we validate the superiority of GeoScale. The experimental results show that compared with existing strategies and designed baselines, GeoScale can improve QoS by reducing the average request response time up to 87.8% while significantly mitigating the violation of the long-term cost budget constraint.