SARA: Stably and quickly find optimal cloud configurations for heterogeneous big data workloads

Abstract With the development of cloud platforms, more and more workloads are operated on the cloud. It is important to find the optimal cloud configurations for these workloads. But most of the previous optimizers concentrate on optimizing single workload. They have high overhead when faced with a large number of workloads to be optimized. To address this problem, we propose SARA, a system that uses Deep Reinforcement Learning to find the optimal cloud configuration for workloads. It is especially stable and quick for any amount of heterogeneous big data workloads. Our research results show that SARA finds the optimal or near-optimal configuration for single workload at a low cost, which is about 28% of Brute Force. For collective workloads, the average probability of SARA to find the optimal configuration is 1.8 times higher than the state-of-the-art collective optimizer Micky, and the cost of SARA for small number of workloads is within 12.5%–80% of Micky.