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Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach.

Zaipeng Xie1,2, Cheng Ji2, Lifeng Xu2

  • 1Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, China.

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Summary
This summary is machine-generated.

This study introduces a novel distributed message system for AIoT edge computing, enhancing message ordering and load balancing. The proposed optimization algorithm significantly improves system throughput for high-concurrency applications.

Keywords:
AIoT edge computingartificial intelligence of thingsdistributed message queuereinforcement learning approachsystem throughput performance

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Artificial Intelligence

Background:

  • The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) into AIoT edge computing presents challenges for existing message queue systems.
  • Fluctuating system conditions, including device numbers, message size, and frequency, impact real-time data processing at edge servers.
  • A need exists for systems that decouple message processing and manage workload variations effectively in AIoT environments.

Purpose of the Study:

  • To develop a distributed message system tailored for AIoT edge computing.
  • To address critical challenges in message ordering within dynamic AIoT environments.
  • To enhance the reliability and performance of message handling for edge devices.

Main Methods:

  • Introduction of a novel Partition Selection Algorithm (PSA) for ensuring message order and load balancing.
  • Development of a distributed message system configuration optimization algorithm (DMSCO) based on Deep Deterministic Policy Gradient (DDPG).
  • Experimental evaluation comparing DMSCO with genetic algorithms and random searching.

Main Results:

  • The PSA effectively ensures message order, balances loads across broker clusters, and improves message availability.
  • The DMSCO algorithm demonstrated significant improvements in system throughput compared to traditional methods.
  • The proposed system meets the demanding requirements of high-concurrency AIoT edge computing applications.

Conclusions:

  • The presented distributed message system offers a robust solution for AIoT edge computing message handling.
  • The novel PSA and DMSCO algorithms contribute to enhanced performance, reliability, and scalability.
  • This work provides a foundation for more efficient and effective AIoT edge data processing systems.