Xie, Z.; Ji, C.; Xu, L.; Xia, M.; Cao, H. Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach. Sensors2023, 23, 5447.
Xie, Z.; Ji, C.; Xu, L.; Xia, M.; Cao, H. Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach. Sensors 2023, 23, 5447.
Xie, Z.; Ji, C.; Xu, L.; Xia, M.; Cao, H. Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach. Sensors2023, 23, 5447.
Xie, Z.; Ji, C.; Xu, L.; Xia, M.; Cao, H. Towards an Optimized Distributed Message Queue System for AIoT Edge Computing: A Reinforcement Learning Approach. Sensors 2023, 23, 5447.
Abstract
The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. The IoT devices in this computing model gather data from various sources and send it to edge servers for real-time processing, which presents a challenge to the existing message queue systems in Artificial Intelligence of Things (AIoT) Edge computing. These systems lack the adaptability to respond to the current state of the system, such as changes in the number of devices, message size, and frequency, and optimize the message transmission mechanism accordingly. Hence, it is critical to devise an approach that can effectively decouple message processing and mitigate workload fluctuations in the AIoT computing environment. To this end, this study introduces a distributed message queue system that is specifically tailored to the AIoT edge environment, utilizing a reinforcement learning approach to optimize message queue performance. Empirical findings reveal that this pioneering method significantly improves system throughput while handling varying message scenarios.
Keywords
Artificial intelligence of thing; Message queue system; Reinforcement learning approach; System throughput
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
