Joint Optimization of Auto-Scaling and Adaptive Service Placement in Edge Computing

In edge computing environment where network connections are often unstable and workload intensity changes frequently, the proper scaling mechanism and service placement strategy based on microservices are needed to ensure the edge services can be provided consistently. However, the common elastic scaling mechanism nowadays is threshold-based responsive scaling and has reaction time in the order of minutes, which is not suitable for delay-sensitive applications in the edge computing environment. Moreover, auto-scaling strategy and service replica placement are considered separately. If the scaled service replicas are misplaced on the edge nodes with limited resources or significant communication latency between upstream and downstream neighbours, the Quality of Service (QoS) cannot be guaranteed even with the auto-scaling mechanism. In this paper, we study the joint optimization of dynamic auto-scaling and adaptive service placement, and define it as a task delay minimization problem while satisfying resource and bandwidth constraints. Firstly, we design a multi-stage auto-scaling model based on workload prediction and performance evaluation of edge nodes to dynamically create an appropriate number of service replicas. Secondly, we propose a Dynamic Adaptive Service Placement (DASP) approach to iteratively place each service replica by using Adaptive Discrete Binary Particle Swarm Optimization (ADBPSO) algorithm. DASP can determine the current optimal placement strategy according to dynamic service replica scaling decision in a short time. The placement results of the current round will guide the optimization of the next cycle iteratively. The experimental evaluation shows that our approach significantly outperforms the existing methods in reducing the average task response time.