Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Performance Evaluation of Cassandra Scalability on Amazon EC2
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Context In the fields of communication systems and computer science, Infrastructure as a Service consists of building blocks for cloud computing and to provide robust network features. AWS is one such infrastructure as a service which provides several services out of which Elastic Cloud Compute (EC2) is used to deploy virtual machines across several data centers and provides fault tolerant storage for applications across the cloud. Apache Cassandra is one of the many NoSQL databases which provides fault tolerance and elasticity across the servers. It has a ring structure which helps the communication effective between the nodes in a cluster. Cassandra is robust which means that there will not be a down-time when adding new Cassandra nodes to the existing cluster.  Objectives. In this study quantifying the latency in adding Cassandra nodes to the Amazon EC2 instances and assessing the impact of Replication factors (RF) and Consistency Levels (CL) on autoscaling have been put forth. Methods. Primarily a literature review is conducted on how the experiment with the above-mentioned constraints can be carried out. Further an experimentation is conducted to address the latency and the effects of autoscaling. A 3-node Cassandra cluster runs on Amazon EC2 with Ubuntu 14.04 LTS as the operating system. A threshold value is identified for each Cassandra specific configuration and is scaled over to five nodes on AWS utilizing the benchmarking tool, Cassandra stress tool. This procedure is repeated for a 5-node Cassandra cluster and each of the configurations with a mixed workload of equal reads and writes. Results. Latency has been identified in adding Cassandra nodes on Amazon EC2 instances and the impacts of replication factors and consistency levels on autoscaling have been quantified. Conclusions. It is concluded that there is a decrease in latency after autoscaling for all the configurations of Cassandra and changing the replication factors and consistency levels have also resulted in performance change of Cassandra.

Place, publisher, year, edition, pages
2018. , p. 41
Keyword [en]
Cassandra, Amazon EC2, Performance Evaluation, Scalability, Cloud Benchmarking, Data Scalability, Infrastructure as a service, Cassandra-stress
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-16141OAI: oai:DiVA.org:bth-16141DiVA, id: diva2:1202676
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2018-05-09 Created: 2018-04-29 Last updated: 2018-05-09Bibliographically approved

Open Access in DiVA

BTH2018Srinadhuni(858 kB)9 downloads
File information
File name FULLTEXT02.pdfFile size 858 kBChecksum SHA-512
9a2a4689dd851d0328218c6809f3d14dc6680aed171acdadaa14f3c4e595a7b9748f0240421f663f82da89b7129d250154e1575351a06b09965cbe9aecdfef0f
Type fulltextMimetype application/pdf

By organisation
Department of Computer Science and Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 42 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf