Towards Sustainable Computing: Exploring Energy Consumption Efficiency of Alternative Configurations and Workloads in an Open Source Messaging System

TL;DR

This study investigates energy consumption in open source messaging systems, specifically RabbitMQ, by benchmarking various configurations and workloads to identify energy-efficient architectures and reduce power usage.

Contribution

It introduces an energy-based benchmarking process for RabbitMQ, comparing alternative architectures and configurations to quantify energy savings and provide a publicly available dataset.

Findings

Up to 31% reduction in power consumption with optimal architecture choices

Energy consumption varies significantly with workload and configuration

Benchmark dataset available for architecture comparison and energy modeling

Abstract

Energy consumption in current large scale computing infrastructures is becoming a critical issue, especially with the growing demand for centralized systems such as cloud environments. With the advancement of microservice architectures and the Internet of Things, messaging systems have become an integral and mainstream part of modern computing infrastructures, carrying out significant workload in a majority of applications. In this paper, we describe an experimental process to explore energy-based benchmarking for RabbitMQ, one of the main open source messaging frameworks. The involved system is described, as well as required components, and setup scenarios, involving different workloads and configurations among the tests as well as messaging system use cases. Alternative architectures are investigated and compared from an energy consumption point of view, for different message rates and consumer numbers. Differences in architectural selection have been quantified and can lead to up to 31\% reduction in power consumption. The resulting dataset is made publicly available and can thus prove helpful for architectures' comparison, energy-based cost modeling, and beyond.