Title: Optimising communication and performance in IoT with RabbitMQ: a bulk arrival single server retrial queueing model with multi-phase service

Authors: Vivesini Devaraj; J. Ebenesar Anna Bagyam; T. Poongodi

Addresses: Department of Mathematics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu–641043, India ' Department of Mathematics, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu – 641043, India ' Department of Mathematics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu – 641043, India

Abstract: RabbitMQ is an advanced messaging technology in which the communication layer interfaces with a message broker and a message queue. This system has opted for a specific message queue, enabling asynchronous communication by implementing message-oriented middleware. This approach encourages applications to be loosely connected, potentially enhancing system performance and optimising resource utilisation. In this study, RabbitMQ serves as an intermediary for sending and receiving user messages. Queueing models can be employed to determine the necessary resources for providing high-quality service. For RabbitMQ (IoT) based on the computing system, we propose a bulk arrival single server retrial queueing model with a mandatory multi-phase second service phase for theoretical modelling. We solve steady-state equations and derive various performance metrics. Subsequently, we develop a cost model. To compute the total expected cost of the service rate, we utilise the particle swarm optimisation algorithm. Finally, we perform a numerical analysis on our model to gain insights into its behaviour.

Keywords: cloud computing; bulk arrival; retrial queue; two phase service; RabbitMQ; particle swarm optimisation; PSO.

DOI: 10.1504/IJMMNO.2025.145609

International Journal of Mathematical Modelling and Numerical Optimisation, 2025 Vol.15 No.1, pp.6 - 26

Received: 04 Oct 2023
Accepted: 07 Oct 2023
Published online: 09 Apr 2025 *