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Determining Appropriate Cache-size for Cost-effective Cloud Database Queries

by Ruchi Nanda, Swati V. Chande, Krishna S. Sharma
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 6
Year of Publication: 2017
Authors: Ruchi Nanda, Swati V. Chande, Krishna S. Sharma
10.5120/ijca2017912651

Ruchi Nanda, Swati V. Chande, Krishna S. Sharma . Determining Appropriate Cache-size for Cost-effective Cloud Database Queries. International Journal of Computer Applications. 157, 6 ( Jan 2017), 29-34. DOI=10.5120/ijca2017912651

@article{ 10.5120/ijca2017912651,
author = { Ruchi Nanda, Swati V. Chande, Krishna S. Sharma },
title = { Determining Appropriate Cache-size for Cost-effective Cloud Database Queries },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 6 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 29-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number6/26837-2017912651/ },
doi = { 10.5120/ijca2017912651 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:03:14.365709+05:30
%A Ruchi Nanda
%A Swati V. Chande
%A Krishna S. Sharma
%T Determining Appropriate Cache-size for Cost-effective Cloud Database Queries
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 6
%P 29-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Retrieving results from the cache is one of the prominent techniques to improve the query response time and reducing load on the back-end database servers. One of the important factors that influences the performance of cache system the most, is the size of the cache. In cloud-based systems, memory is scalable and hence, size of the cache is not a critical issue. However, when the cache is overpopulated with queries and their results, in that case, the query response time increases. This is due to the fact that time for searching the cache for the desired results increases. In this paper, an appropriate cache size is calculated in terms of the number of queries, for the database-size under consideration. This paper also describes the set-up of Virtual Machine Creation (VMC) cloud, using Cloud Virtual Machine Creation (CVMC) algorithm. This facilitates the deployment of database in cloud-based systems. An appropriate cache-size for cloud-based system is determined through experimentation using Apache HBase.

References
  1. A. N. Packer 2001. Configuring and tuning databases on the Solaris platform. Prentice Hall PTR.
  2. X. Long, and T. Suel 2006. Three-level caching for efficient query processing in large web search engines. World Wide Web, 9(4), 369-395.
  3. R. Nanda, K. S. Sharma, S. Chande 2016. Enhancing the Query Performance of NoSQL Datastores using Caching Framework. International Journal of Computer Science and Information Technologies, Volume 7, Issue 5 (September-October 2016), 2332-2336, 0975-9646.
  4. Red-Hat Inc., libvirtd(8) - Linux man page. Online Available: http://linux.die.net/man/8/libvirtd.
  5. Retrieved on 18 July 2016.
  6. K. Raichura, N. Padhariya, and K. Atkotiya 2014. Cache-Based Query Optimization In Mobile Ad-Hoc Networks,” International Journal of Technology Enhancements and Emerging Engineering Research, vol. 3(2), pp.226-232, 2014.
  7. O. D. Sahin, A. Gupta, D. Agrawal, and A. El Abbadi 2004. A peer-to-peer framework for caching range queries. In Proc. Data Engineering. IEEE, pp. 165-176.
  8. H. Ding, A. Yalamanchi, R. Kothuri, S. Ravada, and P. Scheuermann 2006. QACHE: query caching in location-based services. Progress in Spatial Data Handling. Berlin, Heidelberg: Springer, pp. 99-116.
  9. G. Chockler, G., Laden, and Y. Vigfusson 2010. Data caching as a cloud service. In Proc. of the 4th International Workshop on Large Scale Distributed Systems and Middleware ACM, pp. 18-21.
  10. F. Dong, K. Ma, and B. Yang. 2015. Cache system for frequently updated data in the cloud. WSEAS Transactions on Computers, vol. 14, pp. 163-170.
  11. N. Ilayaraja, F. M. Jane, I. Thomson, C. V. Narayan, R. Nadarajan and M. Safar 2011. Semantic Data Caching Strategies for Location Dependent Data in Mobile Environments. In International Conference on Digital Information and Communication Technology and Its Applications (pp. 151-165). Springer Berlin Heidelberg.
  12. N. Le Scouarnec, C. Neumann and G. Straub 2014. Cache policies for cloud-based systems: To keep or not to keep. In IEEE 7th International Conference on Cloud Computing (pp. 1-8). IEEE.
  13. S. L. Kiani, A. Anjum, N. Antonopoulos, K. Munir and R. McClatchey 2012. Context caches in the Clouds. Journal of Cloud Computing: Advances, Systems and Applications, 1(1), 1.
  14. D. Wessels 2001. Web caching, O'Reilly Media, Inc.
  15. Userguide HappyBase [Online]Available: http://happybase.readthedocs.io/en/latest/user.html
  16. C. N. Ziegler, S. M. McNee, J. A. Konstan and G. Lausen 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th international conference on World Wide Web, ACM, 22-32.
  17. M. Perrin 2015. Time-, Energy-, and Monetary Cost-Aware Cache Design for a Mobile-Cloud Database System. Doctoral dissertation, University of Okalahoma.
  18. B. J. Sandmann 2014. Implementation of a Segmented, Transactional Database Caching System. Journal of Undergraduate Research at Minnesota State University, Mankato, vol. 6(1), pp. 21.
Index Terms

Computer Science
Information Sciences

Keywords

Caching NoSQL datastores NoSQL database HBase Cache-size Cloud-based systems cloud datastores Query Response Time

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