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Sanchi S. Achalkhamb, Krishna T. Madrewar
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- Student, Assistant Professor, Department of Electronics and telecommunication Engineering, Deogiri Institute of Engineering and Management studies College, Chhatrapati Sambhajinagar, Department of Electronics and telecommunication Engineering, Deogiri Institute of Engineering and Management studies College, Chhatrapati Sambhajinagar,, Maharashtra, Maharashtra, India, India
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Abstract
nThe rapid expansion of big data has posed substantial difficulties for conventional computing systems. As a result, cluster computing has grown to be a potent method for effective large data processing. Cluster computing involves multiple interconnected nodes functioning as a unified system, pooling together their processing, storage, and memory resources. These nodes are typically connected through high-speed networks such as Ethernet or InfiniBand, facilitating efficient data sharing and communication among them. Big data has made cluster computing frameworks like Apache Hadoop and Apache Spark very popular. These frameworks provide accessible tools and libraries that make creating and running parallel computing tasks on a cluster easier. Additionally, they have fault tolerance methods to ensure system resilience in the event of node failures, protecting data integrity and allowing computation to continue without interruption. By leveraging interconnected computers working in unison, cluster computing enables parallel processing, leading to faster and more scalable data analysis. This paper examines the effects of cluster computing on both big data analysis and network topology.
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Keywords: big data analysis, cluster computing, network topology, computing systems, high- performance computing, high bandwidth
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References
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International Journal of Algorithms Design and Analysis Review
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| Volume | 01 |
| Issue | 01 |
| Received | June 21, 2023 |
| Accepted | June 30, 2023 |
| Published | July 29, 2023 |
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