Modeling and Analysis of RabbitMQ Using UPPAAL

Li, Ran; Yin, Jiaqi; Zhu, Huibiao

doi:10.1109/trustcom50675.2020.00024

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…AIoT systems may also face message floods due to network jitter or failures, necessitating an ideal message queue that can handle volatile environmental factors smoothly. Several message queue systems [7][8][9][10][11][12] have been developed for IoT data processing. However, applying these message queues in the dynamic AIoT computing environment poses challenges.…”

Section: Introductionmentioning

confidence: 99%

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

Xie

et al. 2023

Sensors

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The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.

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Section: Introductionmentioning

confidence: 99%

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

Xie

et al. 2023

Sensors

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Analysing an autonomous tramway positioning system with the Uppaal Statistical Model Checker

et al. 2021

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The substitution of traditional occupancy detecting sensors with an Autonomous Positioning System (APS) is a promising solution to contain costs and improve performance of current tramway signalling systems. APS is an onboard system using satellite positioning and other inertial platforms to autonomously estimate the position of the tram with the needed levels of uncertainty and protection. However, autonomous positioning introduces, even in absence of faults, a quantitative uncertainty with respect to traditional sensors. This paper investigates this issue in the context of an industrial project: a model of the envisaged solution is proposed, and it is analysed using Uppaal Statistical Model Checker. A novel model-driven hazard analysis approach to the exploration of emerging hazards is proposed. The analysis emphasises how the virtualisation of legacy track circuits and on-board satellite positioning equipment may give rise to new hazards, not present in the traditional system.

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Verification of RabbitMQ with Kerberos Using Timed Automata

Yin

Zhu

et al. 2022

Mobile Netw Appl

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Modeling and Analysis of RabbitMQ Using UPPAAL

Cited by 5 publications

References 12 publications

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

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

Analysing an autonomous tramway positioning system with the Uppaal Statistical Model Checker

Verification of RabbitMQ with Kerberos Using Timed Automata

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