A Proactive Cloud Application Auto-Scaler using Reinforcement Learning
2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC)(2022)
摘要
This work explores the use of reinforcement learning to design a proactive cloud resource auto-scaler that is able to predict usage across a distributed microservice application. The focus is on serving time-sensitive workloads, e.g., industrial automation, connected XRNR (eXtended Reality/Virtual Reality), etc., where each job has a deadline and there is some cost associated with missing a deadline. A simple workload model, as well as a microservice application model, is presented. A reinforcement learning agent is trained to identify workloads and predict needed utilization for identified service chains. The results are compared to standard purely reactive techniques.
更多查看译文
关键词
Cloud Computing,Reinforcement Learning,Auto-Scaling,Microservice,Proactive Control
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要