DeepCAT+: A Low-Cost and Transferrable Online Configuration Auto-Tuning Approach for Big Data Frameworks

Abstract

Big data frameworks usually provide a large number of performance-related parameters. Online auto-tuning these parameters based on deep reinforcement learning (DRL) to achieve a better performance has shown their advantages over search-based and machine learning-based approaches. Unfortunately, the time cost 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 low-cost and transferrable deep reinforcement learning-based approach to achieve online configuration auto-tuning for Big Data frameworks. To reduce the total online tuning cost and increase the adaptability: 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 tradeoff; 4) Furthermore, DeepCAT $^+$ also implements an Online Continual Learner module based on Progressive Neural Networks to transfer knowledge from historical tuning experiences. Experimental results based on a lab Spark cluster with HiBench benchmark applications show that DeepCAT $^+$ is able to speed up the best execution time by a factor of 1.49×, 1.63× and 1.65× on average respectively over the baselines, while consuming up to 50.08%, 53.39% and 70.79% less total tuning time. In addition, DeepCAT $^+$ also has a strong adaptability to the time-varying environment of Big Data frameworks.