Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

TL;DR

This study evaluates energy, bandwidth, and storage consumption of popular distributed databases across hybrid cloud and edge environments to inform deployment choices.

Contribution

It provides the first comprehensive experimental analysis of resource consumption for distributed databases in hybrid cloud and edge settings.

Findings

Cassandra and MongoDB show lower energy consumption in hybrid cloud.

Redis performs best in bandwidth efficiency on edge devices.

MySQL exhibits higher storage usage compared to NoSQL options.

Abstract

A benchmark study of modern distributed databases is an important source of information to select the right technology for managing data in the cloud-edge paradigms. To make the right decision, it is required to conduct an extensive experimental study on a variety of hardware infrastructures. While most of the state-of-the-art studies have investigated only response time and scalability of distributed databases, focusing on other various metrics (e.g., energy, bandwidth, and storage consumption) is essential to fully understand the resources consumption of the distributed databases. Also, existing studies have explored the response time and scalability of these databases either in private or public cloud. Hence, there is a paucity of investigation into the evaluation of these databases deployed in a hybrid cloud, which is the seamless integration of public and private cloud. To address these research gaps, in this paper, we investigate energy, bandwidth and storage consumption of the most used and common distributed databases. For this purpose, we have evaluated four open-source databases (Cassandra, Mongo, Redis and MySQL) on the hybrid cloud spanning over local OpenStack and Microsoft Azure, and a variety of edge computing nodes including Raspberry Pi, a cluster of Raspberry Pi, and low and high power servers. Our extensive experimental results reveal several helpful insights for the deployment selection of modern distributed databases in edge-cloud environments.