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Atharva Digamber Katurde, Jitendra Musale, Anil Lohar, Soham Vijay Kolapkar, Bhakti Bharat Shinde, Riya Girish Kshirsagar
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- Student, Assistant Professor, Assistant Professor, Student, Student, Student, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Department of Computer Engineering ABMSP’s Anantrao Pawar College of Engineering and Research Pune, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, India, India, India, India, India, India
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Abstract
n”Data Compression for Backbone Network” involves the application of data compression techniques to improve the efficiency and performance of the core infrastructure of modern digital networks. This approach focuses on reducing the size of transmitted data without compromising its quality, aiming to enhance network throughput, reduce latency, and minimize energy consumption. The study also considers practical implementation challenges and trade-offs to optimize resource utilization in backbone networks. We delve into various compression methods, including lossless and lossy compression algorithms, and evaluate their effectiveness in reducing data size without compromising the quality of transmitted information. We also investigate the potential benefits of data deduplication and data pruning in further minimizing data traffic within backbone networks. We discuss the practical challenges associated with implementing data compression in backbone networks, considering issues such as real-time data processing, security, and scalability. Efficiency is a paramount concern in modern network design, and data compression plays a pivotal role in achieving this goal. We analyze the impact of data compression on network throughput, latency, and energy consumption, providing insights into how these techniques can be leveraged to meet the stringent demands of today’s digital landscape.
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Keywords: Compression, Infrastructure, Optimization, Transmission, Performance, Algorithms
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References
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[1] Jarvis Haupt; Waheed U. Bajwa -“Compressed sensing for networked data.”in:IEEE Signal Processing Magazine Volume: 25, Issue: 2, March 2008.
[2] Reetuparna Das; Asit K. Mishra -“Performance and power optimization through data compression in network-on-chip architectures.” in: 2008 IEEE 14th International Symposium on High Performance Computer Architecture Date of Conference: 16-20 February 2008 Date Added to IEEE Xplore: 24 October 2008.
[3] Ganesh Khade; S. Mini -“Identification of data aggregators in wireless sensor network.” in: 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) Date of Conference: 22-24 March 2017 Date Added to IEEE Xplore: 22 February 2018.
[4] Christopher M. Sadler; Margaret Martonosi -“Data compression algorithms for energy-constrained devices in delay tolerant networks.” SenSys : Proceedings of the 4th international conference on Embedded networked sensor systems 31 October 2006.
[5] Ammar Nasif Zulaiha Ali Othman and Nor Samsiah Sani -“The deep learning solutions on lossless compression methods for alleviating data load on iot nodes in smart cities. Accepted: 2 June 2021 / Published: 20 June 2021.
[6] Stefan Winter and Neeraj Suri -“A security study of data compression in network services” This paper is included in the Proceedings of the 24th USENIX Security Symposium August 12–14, 2015 Washington, D.C.
[7] Andres Perez-Uribe & Héctor F. Satizábal-“Artificial neural networks and data compression statistics for the discrimination of cultured neuronal activity.” 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings, Part I.
[8] Laith Alzubaidi, Jinglan Zhang -“Review of deep learning: concepts, cnn architectures, challenges, applications, and future directions.” Survey Paper in journal of Bigdata Published: 31 March 2021 Volume 8, article number 53, (2021).
[9] Prof. Jitendra C. Musale,-” Data Security System in Cloud by Using Fog Computing and Data Mining”, in International Journal of Advanced Research in Computer Science and Software Engineering Volume 7, Issue 3, March 2017 ISSN: 2277 128X Available online at: www.ijarcsse.com .
[10] Bomash I, Roudi Y, Nirenberg S. A virtual retina for studying population coding. PloS one. 2013 Jan 14;8(1):e53363.
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Journal of Multimedia Technology & Recent Advancements
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | February 16, 2024 | |
| Accepted | April 2, 2024 | |
| Published | April 4, 2024 |
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