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Related Experiment Video

Updated: Dec 15, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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MonkeyKing: Adaptive Parameter Tuning on Big Data Platforms with Deep Reinforcement Learning.

Haizhou Du1,2, Ping Han2, Qiao Xiang3

  • 1College of Electronics and Information Engineering, Tongji University, Shanghai, China.

Big Data
|July 14, 2020
PubMed
Summary
This summary is machine-generated.

Optimizing big data analytics platforms is challenging due to numerous parameter configurations. MonkeyKing, using deep reinforcement learning (DRL), enhances job performance by automatically tuning key parameters, achieving up to 25% improvement.

Keywords:
big data platformsdeep reinforcement learningparameter tuningperformance optimization

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Configuring big data analytics platforms involves hundreds of parameters, impacting performance significantly.
  • Parameter interdependencies complicate automated optimization for diverse applications.

Purpose of the Study:

  • To develop an automated system, MonkeyKing, for optimizing big data platform configurations.
  • To improve job performance by intelligently selecting and tuning critical parameters.

Main Methods:

  • MonkeyKing leverages past experience and real-time data for parameter adjustment.
  • Deep reinforcement learning (DRL), specifically Double DQN and Dueling DQN, is employed for parameter optimization.
  • Key performance-impacting parameters are identified based on job types.

Main Results:

  • The combined Double DQN and Dueling DQN approach demonstrated superior performance.
  • Experiments on Spark showed performance improvements of approximately 25% in optimal scenarios.
  • MonkeyKing effectively recommends and optimizes critical parameters.

Conclusions:

  • Automated parameter optimization using DRL is feasible and effective for big data platforms.
  • MonkeyKing offers a promising solution for enhancing the performance of big data analytics jobs.
  • The proposed DRL-based approach significantly boosts efficiency and performance.