Using Reinforcement Learning to Control Auto-Scaling of Distributed Applications

Abstract

Modern distributed systems can benefit from the availability of large-scale and heterogeneous computing infrastructures. However, the complexity and dynamic nature of these environments also call for self-adaptation abilities, as guaranteeing efficient resource usage and acceptable service levels through static configurations is very difficult. In this talk, we discuss a hierarchical auto-scaling approach for distributed applications, where application-level managers steer the overall process by supervising component-level adaptation managers. Following a bottom-up approach, we first discuss how to exploit model-free and model-based reinforcement learning to compute auto-scaling policies for each component. Then, we show how Bayesian optimization can be used to automatically configure the lower-level auto-scalers based on application-level objectives. As a case study, we consider distributed data stream processing applications, which process high-volume data flows in near real-time and cope with varying and unpredictable workloads.