HAS: Hybrid auto-scaler for resource scaling in cloud environment

Abstract

Auto-scaling is a crucial mechanism that supports autonomic provisioning and de-provisioning of computing resources in accordance with fluctuating demands in a cloud environment. The success factor of autonomic provisioning depends on efficient resource utilization and response time performance metrics. Existing literature focuses on reactive or predictive auto-scaling mechanism where the computing system is unable to scale proportionally with the Slashdot effect or abrupt traffic bursts while these mechanisms are employed in a discrete fashion. Predictive methods strive to predict the future computational needs and subsequently obtain or release the resources in advance; however it could be directed to under-utilization. Hence, a Hybrid Auto-Scaler (HAS) is proposed to adjust the required resources automatically to the application in demand. HAS forecasts the future behaviour of the system using a time series method and deploys the anticipated resources by computing the required capacity through a queuing model. Further, it uses a reactive approach to scale out the resources in accordance as the provisioned resources are insufficient to deal with the current needs. HAS also balances the load efficiently by employing Continuous Time Markov Model (CTMM). The proposed HAS is validated with several benchmark workloads to achieve significant improvement in CPU utilization and response time.