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Scalability of Kubernetes Running Over AWS - A Performance Study while deploying CPU intensive application containers
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Background: Nowadays lot of companies are enjoying the benefits of kubernetes by maintaining their containerized applications over it. AWS is one of the leading cloud computing service providers and many well-known companies are their clients. Many researches have been conducted on kubernetes, docker containers, cloud computing platforms but a confusion exists on how to deploy the applications in Kubernetes. A research gap about the impact created by CPU limits and requests while deploying the Kubernetes application can be found. So, through this thesis I want to analyze the performance of the CPU intensive containerized application. It will help many companies avoid the confusion while deploying their applications over kubernetes.

Objectives: We measure the scalability of kubernetes under CPU intensive containerized application running over AWS and we can study the impact created by changing CPU limits and requests while deploying the application in Kubernetes.

Methods: we choose a blend of literature study and experimentation as methods to conduct the research.

Results and Conclusion: From the experiments it is evident that the application performs better when we allocate more CPU limits and less CPU requests when compared to equal CPU requests and CPU limits in the deployment file. CPU metrics collected from SAR and Kubernetes metrics server are similar. It is better to allocate pods with more CPU limits and CPU requests than with equal CPU requests and CPU limits for better performance.

Keywords: Kubernetes, CPU intensive containerized application, AWS, Stress-ng.

Place, publisher, year, edition, pages
2019. , p. 46
Keywords [en]
Kubernetes, CPU intensive containerized application, AWS, Stress-ng
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-18841OAI: oai:DiVA.org:bth-18841DiVA, id: diva2:1367111
Subject / course
DV2572 Master´s Thesis in Computer Science
Educational program
DVADA Master Qualification Plan in Computer Science
Supervisors
Examiners
Available from: 2019-11-04 Created: 2019-11-01 Last updated: 2019-11-04Bibliographically approved

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Scalability of Kubernetes(1449 kB)39 downloads
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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