A cost-driven online auto-scaling algorithm for web applications in cloud environments

Abstract

Today, many web application service providers rely on clouds to deploy applications to serve users. Generally, request arrivals faced by web applications are dynamic and uncertain. When a service provider deploys web applications in clouds, for saving costs, it needs to flexibly rent cloud VM (Virtual Machine) instances based on dynamic request arrivals. However, renting an instance too early may incur more rental fees for the new instance being added incorrectly due to few future requests, and renting an instance too late may incur more penalty fees for SLA (service-level agreement) violations due to too many future requests, which indicates that an arbitrary instance scaling decision will incur more costs. For making optimal instance scaling decisions, future request arrival rate curves are needed, but it is generally very hard to predict them precisely. To solve this problem, in this paper, we propose a cost-driven online auto-scaling algorithm which can make optimized instance rental decisions without requiring future knowledge. We show theoretically that the proposed algorithm can achieve a guaranteed competitive ratio which is less than 2. Eventually, we verify the effectiveness of our online auto-scaling algorithm via extensive experiments using workload data which can simulate real end users.