A Highly Scalable Peer-to-Peer Grid Computing Architecture for Distributed Data Mining

International Journal of Computer Science and Technology | Vol. 6, No. 1, January 2004 | pp. 1–8

DOI: 10.46882/2004/IJCST/000054

Article Type: Original Research Paper

Title: A Highly Scalable Peer-to-Peer Grid Computing Architecture for Distributed Data Mining

Names of Authors: John A. Benson¹, Ananya Rao²

Authors’ Affiliations: ¹Department of Computer Science, University of Technology, London, UK; ²School of Data Science, Tech-Institute of Bangalore, Karnataka, India

Abstract: Distributed data mining over large-scale wide-area networks requires significant computational infrastructure and robust communication protocols. This paper presents a decentralized Peer-to-Peer (P2P) grid computing architecture designed to execute complex association rule mining without relying on a centralized coordinator. The system introduces a dynamic topology balancing protocol that group nodes based on their available bandwidth and processing power. We implement a non-blocking communication layout using universal text streams to coordinate local frequent itemset exchanges across active nodes. Simulation benchmarks involving 5000 virtual nodes demonstrated that our P2P model achieves a 22.4% reduction in global bandwidth usage compared to traditional master-slave grid frameworks. Furthermore, the computation throughput scaled near-linearly at an efficiency rating of 89.2 ± 1.5%, demonstrating high stability for large-scale data analysis across globally distributed environments.

Keywords: Peer-to-Peer Networks, Grid Computing, Distributed Data Mining, Association Rules, Load Balancing

Manuscript Timeline: Received: October 14, 2003; Revised: November 28, 2003; Accepted: December 20, 2003; Published: January 08, 2004

Citation: Benson, J. A., & Rao, A. (2004). A Highly Scalable Peer-to-Peer Grid Computing Architecture for Distributed Data Mining. International Journal of Computer Science and Technology, 6(1), 1–8.

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