2231–4202  (Print)                
2249–9970  (Online)             
Peer Reviewed and Refereed Journal

JPAST is a Peer Reviewed & Refereed biannual multidisciplinary journal starting from July 2011. Articles are invited for Dec 23 issue.
Volume 1, Issue 2, Pages 9-22, October 2011

Intensify the I/O Performance of OODBS by Collaboration between Clustering and Buffer Replacement

Dheeraj Chooramani1,* and Dr. D.K. Pandey2

1,*Research Scholar, Department of Computer Science, JJTU Rajasthan

2 Director, Dr. Pandey Professional College Ghaziabad


There are different techniques for improving I/O performance of Object oriented database Management Systems (OODBMS). Over 15 years of research into OODBMS design, performance remains as one of the major problems. I/O reduction has proven to be one of the most effective ways enhancing performance. The two main techniques of improving I/O performance of Object Oriented Database Management Systems (OODBMS) are clustering and buffer replacement. Clustering is the placement of objects accessed near to each other in time into the same page. Buffer replacement involves the selection of a page to be evicted, when the buffer is full. The page evicted ideally should be the page needed least in the future. These two techniques both influence the likelihood of a requested object being memory resident. We believe an effective way of reducing disk I/O is to take advantage of the synergy that exists between clustering, and buffer replacement. Hence, we design a framework, whereby clustering algorithms incorporating buffer replacement cache behaviour can be conveniently employed for enhancing the I/O performance of OODBMS. We call this new type of clustering algorithm, Cache Conversant Clustering (C3). In this paper, we present the C3 framework, and a C3 algorithm that we have developed, namely C3-GGP Greedy Graph Partioning. We have tested C3-GGP against three well known clustering algorithms. The results show that C3-GGP out performs them by up to 42% when using popular buffer replacement algorithms such as LRU,FCFS and CLOCK. C3-GGP offers the same performance as the best existing clustering algorithm when the buffer size compared to the database size is very small.

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