Automatic Resource Scaling for Web Applications in the Cloud

Abstract

Web applications play a major role in various enterprise and cloud services. With the popularity of social networks and with the speed at which information can be disseminate around the globe, online systems need to face ever-growing, unpredictable peak load events.

Auto-scaling technique provides on-demand resources according to workload in cloud computing system. However, most of the existing solutions are subject to some of the following constraints: (1) replying on user provided scaling metrics and threshold values, (2) employing the simple Majority Vote scaling algorithm, which is ineffective for scaling Web applications, and (3) lack of capability for predicting workload changes. In this work, we propose an effective auto-scaling strategy, called Work-load Based scaling algorithm, for Web applications. Our proposed scaling strategy is not subject to the aforementioned constraints, and can respond to fluctuated workload and sudden workload change in a short time without relying on over-provisioning of resources. We also propose a new method for analyzing the trend of workload changes. This trend analysis method provides useful information to the scaling algorithm to avoid unnecessary scaling actions, which in turn shortens the response time of requests. The experiment results show that the hybrid Workload Based and trend analysis method keeps response time within 2 seconds even when facing sudden workload change.