Data Compression in Backbone Network

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Digital Communication and Analog Signals
Received Date: 10/21/2024
Acceptance Date: 11/06/2024
Published On: 2024-11-20
First Page: 1
Last Page: 6

Journal Menu

By: Pratik Rathod, Gitanjali Gore, Vaishnavi Nevkar, Suvarna Patil, and Pranita Diddi

1-3 Student, Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, Maharashtra,India
4-Associate Professor, Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, Maharashtra, India
5-Student, Department of Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune,Maharashtra, India

Abstract

In the realm of modern computing and extensive data transmission, the efficient management of network resources is paramount. This paper introduces a novel approach, a data compression system meticulously designed backbone network. The proposed system aims to streamline data compression, significantly curbing network b and width demands, and thereby enhancing network performance. Tailored to the unique requisites and limitations of backbone network environments, the solution optimally balances compression ratios and computational costs. Employing state-of-the-art compression algorithms and methodologies, the system achieves substantial reductions in data volume while ensuring prompt data transmission. Through this venture, we present a pioneering stride toward fostering sustainable, high-speed network infrastructures, poised to meet the burgeoning demands of the digital era.

KeywordsCompression; Data compression; Lossless Compression; Backbone Network; LZ77; Huffman Coding; GZIP.

Loading

Citation:

How to cite this article: Pratik Rathod, Gitanjali Gore, Vaishnavi Nevkar, Suvarna Patil, and Pranita Diddi, Data Compression in Backbone Network. International Journal of Digital Communication and Analog Signals. 2024; 10(02): 1-6p.

How to cite this URL: Pratik Rathod, Gitanjali Gore, Vaishnavi Nevkar, Suvarna Patil, and Pranita Diddi, Data Compression in Backbone Network. International Journal of Digital Communication and Analog Signals. 2024; 10(02): 1-6p. Available from:https://journalspub.com/publication/ijdcas/article=12148

Refrences:

  • Ziv J, Lempel A. A universal algorithm for sequential data compression. IEEE Transactions on information theory. 1977 May;23(3):337-43. DOI: 10.1109/TIT.1977.1055714
  • Alakuijala J, Szabadka Z. RFC 7932: Brotli Compressed Data Format. DOI: 10.17487/RFC7932
  • Fitriya LA, Purboyo TW, Prasasti AL. A review of data compression techniques. International Journal of Applied Engineering Research. 2017;12(19):8956-63.
  • Ren H. A data compression technique based on reversed leading bits coding and Huffman coding. In2015 10th International Conference on Communications and Networking in China (ChinaCom) 2015 Aug 15 (pp. 437-441). IEEE.
  • Djusdek DF, Studiawan H, Ahmad T. Adaptive image compression using adaptive Huffman and LZW. In2016 International Conference on Information & Communication Technology and Systems (ICTS) 2016 Oct 12 (pp. 101-106). IEEE.
  • Kawabata T. Enumerative implementation of Lempel-Ziv 77 algorithm. In2008 IEEE International Symposium on Information Theory 2008 Jul 6 (pp. 990-994). IEEE.
  • Murgan AT, Radescu R. A comparison of algorithms for lossless data compression using the lempel-ziv-welch type methods. InProceedings of 1994 Workshop on Information Theory and Statistics 1994 Oct 27 (p. 105). IEEE.
  • Sayood K, Introduction to Data Compression (4th ed.), Morgan Kaufmann Publishers, San Francisco, CA, USA, second edition, 2012.
  • Pu IM, Fundamental Data Compression, Butterworth Heine- mann, Newton, MA, USA, 2005.
  • Kattan A. Universal intelligent data compression systems: A review. In2010 2nd Computer Science and Electronic Engineering Conference (CEEC) 2010 Sep 8 (pp. 1-10). IEEE.
  • Sadchenko A, Kushnirenko O, Plachinda O. Fast lossy compression algorithm for medical images. In2016 international conference on electronics and information technology (EIT) 2016 May 23 (pp. 1-4).
  • Ergude B, Weisheng L, Dongrui F, Xiaoyu M. A study and implementation of the Huffman algorithm based on condensed Huffman table. In2008 International Conference on Computer Science and Software Engineering 2008 Dec 12 (Vol. 6, pp. 42-45).
  • Moffat A. Huffman coding. ACM Computing Surveys (CSUR). 2019 Aug 30;52(4):1-35.
  • Portell J, Villafranca AG, García-Berro E. A resilient and quick data compression method of prediction errors for space missions. InSatellite Data Compression, Communication, and Processing V 2009 Aug 31 (Vol. 7455, pp. 38-48). SPIE.
  • Bhadane DS, Kanawade SY. Comparative study of RLE & K-RLE compression and decompression in WSN. In2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS) 2016 Jan 22 (Vol. 1, pp. 1-5). IEEE.
  • Brisaboa NR, Cerdeira-Pena A, de Bernardo G, Navarro G. Improved compressed string dictionaries. InProceedings of the 28th ACM International Conference on Information and Knowledge Management 2019 Nov 3 (pp. 29-38).
  • Lloyd T, Barton K, Tiotto E, Amaral JN. Run-length base-delta encoding for high-speed compression. InWorkshop Proceedings of the 47th International Conference on Parallel Processing 2018 Aug 13 (pp. 1-9).
  • Pekhimenko G, Seshadri V, Mutlu O, Gibbons PB, Kozuch MA, Mowry TC. Base-delta-immediate compression: Practical data compression for on-chip caches. InProceedings of the 21st international conference on Parallel architectures and compilation techniques 2012 Sep 19 (pp. 377-388).
  • Paudyal R, Shakya S. An approach towards backbone network congestion minimization in software defined network. In2017 International Conference on Computing, Communication and Automation (ICCCA) 2017 May 5 (pp. 412-416). IEEE..
  • Deorowicz S, Grabowski S. Data compression for sequencing data. Algorithms for Molecular Biology. 2013 Jan;8:1-3.
  • Bentley JL, Sleator DD, Tarjan RE, Wei VK. A locally adaptive data compression scheme. Communications of the ACM. 1986 Apr 1;29(4):320-30.
  • Ren H. A data compression technique based on reversed leading bits coding and Huffman coding. In2015 10th International Conference on Communications and Networking in China (ChinaCom) 2015 Aug 15 (pp. 437-441). IEEE.
  • Murgan AT, Radescu R. A comparison of algorithms for lossless data compression using the lempel-ziv-welch type methods. InProceedings of 1994 Workshop on Information Theory and Statistics 1994 Oct 27 (p. 105). IEEE.
  • Sharma K, Gupta K. Lossless data compression techniques and their performance. In2017 International Conference on Computing, Communication and Automation (ICCCA) 2017 May 5 (pp. 256-261). IEEE.
  • Rauschert P, Klimets Y, Velten J, Kummert A. Very fast gzip compression by means of content addressable memories. In2004 IEEE Region 10 Conference TENCON 2004. 2004 Nov 24 (Vol. 500, pp. 391-394). IEEE.
  • Taylor PR. Lossless compression of wave function information using matrix factorization: A “gzip” for quantum chemistry. The Journal of Chemical Physics. 2013 Aug 21;139(7).
  • Gupta A, Nigam S. A review on different types of lossless data compression techniques. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2021 Jan;7(1):50-6.
  • Ng WK, Choi S, Ravishankar C. Lossless and lossy data compression. Evolutionary algorithms in engineering applications. 1997:173-88.
  • Shah AS, Sethi MA. The improvised GZIP, a technique for real time lossless data compression. EAI Endorsed Transactions on Context-aware Systems and Applications. 2019 Jun 26;6(17):e5-.
  • Crochemore M, Ilie L, Smyth WF. A simple algorithm for computing the Lempel Ziv factorization. InData Compression Conference (DCC 2008) 2008 Mar 25 (pp. 482-488). IEEE.
  • Gasieniec L, Karpinski M, Plandowski W, Rytter W. Efficient algorithms for Lempel-Ziv encoding. InAlgorithm Theory—SWAT’96: 5th Scandinavian Workshop on Algorithm Theory Reykjavík, Iceland, July 3–5, 1996 Proceedings 5 1996 (pp. 392-403). Springer Berlin Heidelberg.
  • Wyner AD, Ziv J. The sliding-window Lempel-Ziv algorithm is asymptotically optimal. Proceedings of the IEEE. 1994 Jun;82(6):872-7.