Sanchi S. Achalkhamb,
Krishna T. Madrewar,
- Student Department of Electronics and telecommunication Engineering, Deogiri Institute of Engineering and Management studies College, Chhatrapati Sambhajinagar Maharashtra India
- Assistant Professor Department of Electronics and telecommunication Engineering, Deogiri Institute of Engineering and Management studies College, Chhatrapati Sambhajinagar, Maharashtra India
Abstract
The 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.
Keywords: Big data analysis, cluster computing, network topology, computing systems, highperformance computing, high bandwidth
[This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]
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References
- Buyya R, Vecchiola C, Selvi ST. Mastering Cloud Computing: Foundations and Applications Programming. Cambridge, MA: Morgan Kaufmann; 2013.
- Mayer-Schönberger V, Cukier K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston, MA: Houghton Mifflin Harcourt; 2013.
- Medhi D, Ramasamy K. Network Routing: Algorithms, Protocols, and Architectures. Cambridge, MA: Morgan Kaufmann; 2017.
- Ridge D, Becker D, Merkey P, Sterling T. Beowulf: harnessing the power of parallelism in a pile-of-PCs. In: 1997 IEEE Aerospace Conference, Snowmass, CO, USA, February 13, 1997. Volume 2, pp. 79–91.
- Lin J, Dyer C. Data-intensive text processing with MapReduce. In: Hirst G, series editor. Synthesis Lectures on Human Language Technologies #7. Kentfield, CA: Morgan & Claypool Publishers;
- Sa-Ngasoongsong A, Kunthong J, Sarangan V, Cai X, Bukkapatnam ST. A low-cost, portable, high-throughput wireless sensor system for phonocardiography applications. Sensors. 2012; 12 (8): 10851–10870.
- Miller TC, Stirlen C, Nemeth E. satool – a system administrator’s cockpit, an implementation. In: Seventh System Administration Conference: LISA 1993, Monterey, CA, USA, November 5, 1993. pp. 119–130.
- MarketWide Research. Cluster computing market analysis -– industry size, share, research report, insights, covid-19 impact, statistics, trends, growth and forecast 2023-2030. [Online]. 2023. MarkWide Research. 2023. Available at https://markwideresearch.com/cluster-computing-market/
- Saturn Cloud. Hadoop how to unit test filesystem. [Online]. 2023. Available at https://saturncloud.io/blog/hadoop-how-to-unit-test-filesystem/
- NAKIVO Team. High availability vs fault tolerance vs disaster recovery. [Online]. NAKIVO Team. 2018. Available at https://www.nakivo.com/blog/disaster-recovery-vs-high-availability-vs-fault-tolerance/
- Mosley D. Network topology definitions – designing infrastructure Windows Server 2003. [Online]. 2023. Windows Server Brain. Available at https://www.serverbrain.org/designing-infrastructure-2003/network-topology-definitions.html
| Volume | 01 |
| Issue | 01 |
| Received | June 21, 2023 |
| Accepted | June 30, 2023 |
| Published | August 24, 2023 |