Proactive Memory Scaling of Virtualized Applications

Abstract

Enterprise applications in virtualized environments are often subject to time-varying workloads with multiple seasonal patterns and trends. In order to ensure quality of service for such applications while avoiding over-provisioning, resources need to be dynamically adapted to accommodate the current workload demands. Many memory-intensive applications are not suitable for the traditional horizontal scaling approach often used for runtime performance management, as it relies on complex and expensive state replication. On the other hand, vertical scaling of memory often requires a restart of the application. In this paper, we propose a proactive approach to memory scaling for virtualized applications. It uses statistical forecasting to predict the future workload and reconfigure the memory size of the virtual machine of an application automatically. To this end, we propose an extended forecasting technique that leverages meta-knowledge, such as calendar information, to improve the forecast accuracy. In addition, we develop an application controller to adjust settings associated with application memory management during memory reconfiguration. Our evaluation using real-world traces shows that the forecast accuracy quantified with the MASE error metric can be improved by 11 - 59%. Furthermore, we demonstrate that the proactive approach can reduce the impact of reconfiguration on application availability by over 80% and significantly improve performance relative to a reactive controller.