Elasticity Control for Latency-Intolerant Mobile Edge Applications

Elasticity is a fundamental property required for Mobile Edge Clouds (MECs) to become mature computing platforms hosting software applications. However, MECs must cope with several challenges that do not arise in the context of conventional cloud platforms. These include the potentially highly distributed geographical deployment, heterogeneity, and limited resource capacity of Edge Data Centers (EDCs), and end-user mobility. In this paper, we present an elasticity controller to help MECs overcome these challenges by automatic proactive resource scaling. The controller utilizes information on the physical locations of EDCs and the correlation of workload changes in physically neighboring EDCs to predict request arrival rates at EDCs. These predictions are used as inputs for a queueing theory-driven performance model that estimates the number of resources that should be provisioned to EDCs in order to meet predefined Service Level Objectives (SLOs) while maximizing resource utilization. The controller also incorporates a group-level load balancer that is responsible for redirecting requests among EDCs during runtime so as to minimize the request rejection rate. We evaluate our approach by performing simulations with an emulated MEC deployed over a metropolitan area and a simulated application workload using a real-world user mobility trace. The results show that our proposed pro-active controller exhibits better scaling behavior than a state-of-the-art re-active controller and increases the efficiency of resource provisioning, thereby helping MECs to sustain resource utilization and rejection rates that satisfy predefined SLOs while maintaining system stability.