The Impact of Distance on Performance and Scalability of Distributed Database Systems in Hybrid Clouds

TL;DR

This study evaluates how distance in hybrid cloud setups affects the performance and scalability of six modern databases, revealing key insights into throughput, scalability, and optimal VM configurations.

Contribution

It provides a comprehensive analysis of database performance in hybrid clouds, focusing on the impact of distance and VM sizing, which was previously underexplored.

Findings

Distance reduces throughput for most databases.

MongoDB has the best throughput performance.

Vertical scalability outperforms horizontal scalability.

Abstract

The increasing need for managing big data has led the emergence of advanced database management systems. There has been increased efforts aimed at evaluating the performance and scalability of NoSQL and Relational databases hosted by either private or public cloud datacenters. However, there has been little work on evaluating the performance and scalability of these databases in hybrid clouds, where the distance between private and public cloud datacenters can be one of the key factors that can affect their performance. Hence, in this paper, we present a detailed evaluation of throughput, scalability, and VMs size vs. VMs number for six modern databases in a hybrid cloud, consisting of a private cloud in Adelaide and Azure based datacenter in Sydney, Mumbai, and Virginia regions. Based on results, as the distance between private and public clouds increases, the throughput performance of most databases reduces. Second, MongoDB obtains the best throughput performance, followed by MySQL C luster, whilst Cassandra exposes the most fluctuation in through performance. Third, vertical scalability improves the throughput of databases more than the horizontal scalability. Forth, exploiting bigger VMs rather than more VMs with less cores can increase throughput performance for Cassandra, Riak, and Redis.