Efficient Support of Big Data Storage Systems on the Cloud

TL;DR

This paper proposes an architecture for running Hadoop on OpenStack using local storage, demonstrating improved performance and cost savings over traditional networked storage in cloud environments.

Contribution

It introduces a novel architecture supporting Hadoop on OpenStack with local storage, highlighting its advantages over networked storage for big data applications.

Findings

Local storage outperforms networked storage in Hadoop benchmarks

Using local storage reduces costs for big data workloads

Supporting local storage enhances Hadoop performance in cloud environments

Abstract

Due to its advantages over traditional data centers, there has been a rapid growth in the usage of cloud infrastructures. These include public clouds (e.g., Amazon EC2), or private clouds, such as clouds deployed using OpenStack. A common factor in many of the well known infrastructures, for example OpenStack and CloudStack, is that networked storage is used for storage of persistent data. However, traditional Big Data systems, including Hadoop, store data in commodity local storage for reasons of high performance and low cost. We present an architecture for supporting Hadoop on Openstack using local storage. Subsequently, we use benchmarks on Openstack and Amazon to show that for supporting Hadoop, local storage has better performance and lower cost. We conclude that cloud systems should support local storage for persistent data (in addition to networked storage) so as to provide efficient support for Hadoop and other Big Data systems