AMAS: Adaptive Auto-Scaling on the Edge
Saptarshi Mukherjee, Subhajit Sidhanta · 2021
Despite the emergence of edge computing as a key technology paradigm, there is a general lack of auto-scaling techniques specifically designed for edge computing applications. Further, existing auto-scaling solutions tailor-made for the cloud cannot be readily applied to an application running on an edge cluster. In this paper we present AMAS - a novel auto-scaling algorithm, designed specifically for an edge cluster, which allows edge devices to be automatically and seamlessly added or deleted from an edge cluster according to dynamic variations in the workload. By design, AMAS can help users and enterprises minimize the cost of infrastructure while maintaining necessary SLO (i.e., Service Level Objective) deadlines for different edge applications. We demonstrate that AMAS outperforms the state-of-the-art auto-scalers in failure-prone conditions.