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

Big data frameworks usually provide a large numberof performance-related parameters. Online auto-tuning these pa-rameters based on deep reinforcement learning (DRL) to achieve abetter performance has shown their advantages over search-basedand machine learning-based approaches. Unfortunately, the timecost during the online tuning phase of conventional DRL-basedmethods is still heavy, especially for Big Data applications. There-fore, in this paper, we propose DeepCAT+, a low-cost and trans-ferrable deep reinforcement learning-based approach to achieveonline configuration auto-tuning for Big Data frameworks. Toreduce the total online tuning cost and increase the adaptability: 1)DeepCAT+utilizes the TD3 algorithm instead of DDPG to alleviatevalue overestimation; 2) DeepCAT+modifies the conventional ex-perience replay to fully utilize the rare but valuable transitions viaa novel reward-driven prioritized experience replay mechanism;3) DeepCAT+designs a Twin-Q Optimizer to estimate the exe-cution time of each action without the costly configuration eval-uation and optimize the sub-optimal ones to achieve a low-costexploration-exploitation tradeoff; 4) Furthermore, DeepCAT+also implements an Online Continual Learner module based onProgressive Neural Networks to transfer knowledge from historicaltuning experiences. Experimental results based on a lab Spark clus-ter with HiBench benchmark applications show that DeepCAT+is able to speed up the best execution time by a factor of 1.49x,1.63xand 1.65xon average respectively over the baselines, whileconsuming up to 50.08%, 53.39% and 70.79% less total tuningtime. In addition, DeepCAT+also has a strong adaptability to thetime-varying environment of Big Data frameworks