Open Access   Article Go Back

Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper

Kiranjit Kaur1 , Vijay Laxmi2

Section:Review Paper, Product Type: Journal Paper
Volume-6 , Issue-9 , Page no. 881-884, Sep-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i9.881884

Online published on Sep 30, 2018

Copyright © Kiranjit Kaur, Vijay Laxmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Kiranjit Kaur, Vijay Laxmi, “Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.9, pp.881-884, 2018.

MLA Style Citation: Kiranjit Kaur, Vijay Laxmi "Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper." International Journal of Computer Sciences and Engineering 6.9 (2018): 881-884.

APA Style Citation: Kiranjit Kaur, Vijay Laxmi, (2018). Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper. International Journal of Computer Sciences and Engineering, 6(9), 881-884.

BibTex Style Citation:
@article{Kaur_2018,
author = {Kiranjit Kaur, Vijay Laxmi},
title = {Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {9 2018},
volume = {6},
Issue = {9},
month = {9},
year = {2018},
issn = {2347-2693},
pages = {881-884},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2959},
doi = {https://doi.org/10.26438/ijcse/v6i9.881884}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i9.881884}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2959
TI - Parametric Analysis of Cloud Data Partitioning Techniques: Review Paper
T2 - International Journal of Computer Sciences and Engineering
AU - Kiranjit Kaur, Vijay Laxmi
PY - 2018
DA - 2018/09/30
PB - IJCSE, Indore, INDIA
SP - 881-884
IS - 9
VL - 6
SN - 2347-2693
ER -

VIEWS PDF XML
415 322 downloads 191 downloads
  
  
           

Abstract

Technology makes life easier but at the same time generating bundles of data which is difficult to manage in traditional data stores. To manage this huge data, new data stores called NoSQL came into existence, they resolve the problem of data management by using partitioning. This paper discusses different partitioning techniques named horizontal, Vertical and Workload Driven Partitioning. Focus of this paper is to compare these partitioning techniques on the bases of important parameters named communication cost, complexity of search, quality and scalability. It provides the result on the basis of analysis which helps to choose the relevant partitioning technique.

Key-Words / Index Term

Horizontal partitioning, Vertical partitioning, Workload Driven partitioning, Communication cost, Complexity of search, Quality, Scalibility

References

[1]. S. Ahirrao, R. Ingle, “Scalable transactions in cloud data stores”, Journal of Cloud Computing: a Springer Open journal, 2015.
[2]. K. Grolinger et al, “Data Management in cloud environments: NoSQL and NewSQL data stores”, Journal of Cloud Computing: a Springer Open journal, 2013.
[3]. K. Jens et al, “On the performance of Query Rewriting in Vertically Distributed Cloud Databases”, Springer: Innovative Approaches and Solutions in Advanced Intelligent Systems, Vol. 648, pp. 59-73, 2016.
[4]. D. Agarwal et al, “Database Scalability, Elasticity and Autonomy in the Clouds”, Springer: Database Systems for Advanced Applications, Vol 6587, pp 2-15, 2012.
[5]. A. Lakshman, P. Malik, “Cassandra: A decentralized structured storage system”, ACM SIGOPS Operating System Review, Vol. 44, Issue 2, pp. 35-40, 2010.
[6]. G. Decandia et al, “Dynamo: Amazon’s highly available key value store”, in the proceedings of the 21st ACM Symposium on Operating System Principles, ACM, New York, pp 205-220, 2007.
[7]. S. Das et al, “Elastrans: An elastic transactional data store in the cloud”, in the proceedings of the 1st USENIX workshop on hot topics on cloud computing, USENIX Association, Berkeley, CA, pp 1-5, 2013.
[8]. W. Vogels, “Data access patterns in the amazon.com technology platform”, in the proceedings of the 33rd International conference on Very Large Data Bases, VLDB Endowment, 2007.
[9]. K. Kaur, V. Laxmi, “Partitioning techniques in Cloud Data Storage: Review paper”, International journal of advanced research in computer science, Vol. 8, No. 5, May-June 2017.
[10]. Vanderlei et al, “A cooperative classification mechanism for search and reterival software components”, in the proceedings of the 2017 ACM symposium on applied computing, pp 866-871, 2007.