DeepCAT: A Cost-Efficient Online Configuration Auto-Tuning Approach for Big Data Frameworks

Abstract

To support different application scenarios, big data frameworks usually provide a large number of performance-related configuration parameters. Online auto-tuning these parameters based on deep reinforcement learning to achieve a better performance has shown their advantages over search-based and machine learning-based approaches. Unfortunately, the time consumption during the online tuning phase of conventional DRL-based methods is still heavy, especially for big data applications. Therefore, in this paper, we propose DeepCAT, a cost-efficient deep reinforcement learning-based approach to achieve online configuration auto-tuning for big data frameworks. To reduce the total online tuning cost: 1) DeepCAT utilizes the TD3 algorithm instead of DDPG to alleviate value overestimation; 2) DeepCAT modifies the conventional experience replay to fully utilize the rare but valuable transitions via a novel reward-driven prioritized experience replay mechanism; 3) DeepCAT designs a Twin-Q Optimizer to estimate the execution time of each action without the costly configuration evaluation and optimize the sub-optimal ones to achieve a low-cost exploration-exploitation trade off. Experimental results based on a local 3-node Spark cluster and HiBench benchmark applications show that DeepCAT is able to speed up the best execution time by a factor of 1.45 × and 1.65 × on average respectively over CDBTune and OtterTune, while consuming up to 50.08% and 53.39% less total tuning time.