Advertisement

Proposed Model for Distributed Storage Automation System Using Kubernetes Operators

  • Ashish SharmaEmail author
  • Sarita Yadav
  • Neha Gupta
  • Shafali Dhall
  • Shikha Rastogi
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 612)

Abstract

Cloud distributed system has undergone substantial changes in the past few years, to aim for a more reliable and cost-effective operation. In this paper, we would be focusing on automation of distributed persistent storage system that is a major problem as we are shifting toward containerization. Various systems have been developed that support management of distributed storage. However, these systems fail to manage and are easily scalable in case of failure. In this paper, a smart distributed storage automation system (DSAS) has been proposed that is capable to perform healthy fault detection and fix in a distributed persistent storage system using Kubernetes architecture for distributed system and Ceph Architecture for managing distributed storage. We would be using their existing functionality and extend it using Kubernetes operator. Moreover, provides easy portability, self-reliability, self-scalability, and robustness.

Keywords

Distributed storage Storage orchestration Kubernetes Persistent storage Operators 

References

  1. 1.
    Wu H (2008) Research on the data storage and access model in distributed computing environment. In: Third international conference on convergence and hybrid information technology, Busan, Nov 2008Google Scholar
  2. 2.
    Merkel D (2014) Docker: lightweight linux containers for consistent development and deployment. Int J Linux J 239:2014Google Scholar
  3. 3.
    Sinnott RO, Voorsluys W (2016) A scalable cloud-based system for data-intensive spatial analysis. Int J Softw Tools Technol Transf 18(6):587–605 CrossRefGoogle Scholar
  4. 4.
    Armbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58CrossRefGoogle Scholar
  5. 5.
    Annual Container adoption Survey [Online]. Available https://portworx.com/2017-container-adoption-survey/
  6. 6.
    Burns B, Grant B, Oppenheimer D, Brewer E, Wilkes J (2016) Borg, Omega, and Kubernetes. ACM Queue 14:70–93CrossRefGoogle Scholar
  7. 7.
    Google Cloud Platform, Container engine [Online]. Available https://cloud.google.com/container-engine/. Accessed on 25 Feb 2016
  8. 8.
    Kubernetes [Online]. Available https://kubernetes.io/
  9. 9.
    Kubernetes Architecture [Online]. Available https://x-team.com/blog/introduction-kubernetes-architecture/
  10. 10.
    Weil SA, Brandt SA, Miller EL, Maltzhan C (2006) A scalable, high-performance distributed file system. In: 7th Conference on Operating Systems Design and Implementation (OSDI’06), Nov 2006Google Scholar
  11. 11.
    Weil SA, Brandt SA, Miller EL (2006) CRUSH: controlled, scalable, decentralized placement of replicated data. In: Proceedings of SC’06, Nov 2006Google Scholar
  12. 12.
    Weil SA, Leung AW, Brandt SA (2007) RADOS: a fast, scalable, and reliable storage service for petabyte-scale storage clusters, petascale data storage workshop SC07, Nov 2007Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ashish Sharma
    • 1
    Email author
  • Sarita Yadav
    • 1
  • Neha Gupta
    • 1
  • Shafali Dhall
    • 1
  • Shikha Rastogi
    • 1
  1. 1.Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia

Personalised recommendations