A Dynamic Data Fragmentation and Distribution Strategy for Main-Memory Database Cluster

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Abstract:

MMDB cluster system is a memory optimized relation database that implements on cluster computing platform, provides applications with extremely fast response time and very high throughput as required by many applications in a wide range of industries. Here, a new dynamic fragment allocation algorithm (DFAPR) in Partially Replicated allocation scenario is proposed. This algorithm reallocates data with respect to changing data access pattern for each fragment in which data is maintained in current site, migrated or created new replicas on remote sites depend on accessing frequency and average response time. At last, the simulation results show that the DFAPR is suitable for MMDB cluster because it provides a better response time and maximize the locality of processing so it could be developed parallel processing of MMDB in cluster environment.

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Advanced Materials Research (Volumes 490-495)

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1231-1236

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March 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] A.Singh, K.S. Kahlon, "Non-replicated Dynamic Data Allocation in Distributed Database Systems", IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.9: pp.176-180, September 2009.

Google Scholar

[2] Peter M.G. Apers, Carel A.van den Berg, Jan Flokstra, Paul W.P.J Grefen, Martin L.Kersten, Annita N.Wilschut, "PRISMA/DB: A Parallel, Main Memory Relational DBMS", Transaction on knowledge and Data Engineering, Vol. 4, No 6: pp.541-554, 1992.

DOI: 10.1109/69.180605

Google Scholar

[3] Soon M.Chung, "Parallel Main Memory Database System", ACM 1992: pp.273-282.

Google Scholar

[4] J. Griffioen, T.Anderson, Y. Breitbart, R.Vingralek, "DERBY: A Memory Management

Google Scholar

[5] Y.Huang, Y.S. Zhang, X.D. Ji, Z.W. Wang, S. Wang, "A Data Distribution Strategy for Scalable Main-Memory Database". APWeb and WAIM 2009, LNCS 5731, pp.13-24, Springer 2009.

Google Scholar

[6] Y. Zhang et al, "ScaMMDB: Facing Challenge of Mass Data Processing with MMDB", APWeb and WAIM 2009, LNCS 5731, pp.1-12, Springer 2009.

Google Scholar

[7] D.Hao, L.Shengmei, Z.Hengsheng, "A Distributed In-Memory Database Solution for Mass Data Applications", A Distributed In-Memory Database Solution for Mass Data Applications, December 2010 Vol.8 No.4: 45-48.

Google Scholar

[8] T. Ulus and M. Uysal, "A Threshold Based Dynamic Data Allocation Algorithm- A Markove Chain Model Approach", Journal of Applied Science 7(2), pp.165-174, 2007.

DOI: 10.3923/jas.2007.165.174

Google Scholar

[9] T.Ulus and M.Uysal, "Heuristic Approach to Dynamic Data Allocation in Distributed Database Systems", Pakistan Journal of Information and Technology, 2(3): pp.231-239, (2003)

DOI: 10.3923/itj.2003.231.239

Google Scholar

[10] L.S. John, "A Generic Algorithm for Fragment Allocation in Distributed Database System", ACM 1994.

Google Scholar

[11] Nilarun Mukherjee, "Non-Replicated Dynamic Fragment Allocation in Distributed Database Systems", CCSIT 2011, Part I, CCIS 131, p.560–569, Springer 2011.

DOI: 10.1007/978-3-642-17857-3_55

Google Scholar

[12] G.Karakostas, D.N. Serpanos, "Pratical LFU Implementation for web caching", 2000.

Google Scholar

[13] D.G. Cameron, R.C. Schiaffino, J.Ferguson, A.P. Millar, C.Nicholson, K.Stockinger, F.Zini, "OptorSim v2.1 Installation and User Guide". October 4, 2006. http://edg-wp2.web.cern.ch/edg-wp2/optimization/ optorsim.html

DOI: 10.1109/grid.2003.1261698

Google Scholar

[14] W.H. Bell, D.G. Cameron, L.Capozza, A.P. Millar, K.Stockinger, F.Zini, "Simulation of Dynamic Grid Replication Strategies in OptorSim", GRID 2002, LNCS 2536, p.46–57, Springer 2002.

DOI: 10.1007/3-540-36133-2_5

Google Scholar