Auto-Scaling Cloud Resources using LSTM and Reinforcement Learning to Guarantee Service-Level Agreements and Reduce Resource Costs

Abstract

Auto-Scaling cloud resources aim at responding to application demands by automatically scaling the compute resources at runtime to guarantee service-level agreements (SLAs) and reduce resource costs. Existing approaches often resort to predefined sets of rules to add/remove resources depending on the application usage. However, optimal adaptation rules are difficult to devise and generalize. A proactive approach is proposed to perform auto-scaling cloud resources in response to dynamic traffic changes. This paper applies Long Short-Term Memory (LSTM) to predicting the accurate number of requests in the next time and applies Reinforcement Learning (RL) to obtaining the optimal action to scale in or scale out virtual machines. To validate the proposal, experiments under two real-world workload traces are conducted, and the results show that the approach can ensure virtual machines to work steadily and can reduce SLA violations by up to 10%-30% compared with other approaches.