student
Russian Federation
Mozhaisky Military Aerospace Academy (Department of Mathematics and Software, Professor)
Russian Federation
VAK Russia 2.3.1
UDC 004.942
The exponential growth of data volumes in cloud environments makes it critically important to efficiently compress this data to optimize the use of storage resources, network bandwidth and computational power, which directly affects economic and operational efficiency. Purpose: to conduct a comparative analysis of modern lossless compression algorithms to identify optimal scenarios for their application in cloud platforms. Results: the principles of operation and classification of contemporary lossless compression algorithms have been systematized. Based on a comparative analysis of key parameters, such as compression ratio, operation speed, and resource intensity, optimal application scenarios for the Gzip, LZ4, Zstandard and Brotli algorithms in cloud services have been determined. Practical significance: the results obtained can be used for optimizing costs associated with cloud infrastructure and for improving the performance of distributed data processing systems. Discussion: the conducted analysis demonstrates that the choice of algorithm depends on the specific characteristics of the task. The use of hardware acceleration can significantly improve compression performance.
data compression, lossless compression, cloud computing, compression algorithms, Gzip, LZ4, Zstandard, Brotli, cloud system performance, cost optimization, network traffic, hardware acceleration
1. Verzilin D. N., Maximova T. G., Shanygin S. I. Oblachnye tekhnologii v razvitii institutov tsifrovoy transformatsii rossiyskoy ekonomiki: statisticheskoe issledovanie [Cloud Technologies in the Development of Institutions for Digital Transformation of the Russian Economy: Statistical Research], Vestnik Sankt-Peterburgskogo universiteta. Ekonomika [St. Petersburg University Journal of Economic Studies], 2025, vol. 41, iss. 1, pp. 146–178. DOI: 10.21638/ spbu05.2025.107 (In Russian) DOI: https://doi.org/10.21638/spbu05.2025.107; EDN: https://elibrary.ru/VHOHTM
2. Davydenko E. A., Sabirgalieva A. M. Arkhivirovanie i szhatie faylov v Linux: metody, algoritmy i ikh effektivnost [Archiving and Compressing Files in Linux: Methods, Algorithms and Their Effectiveness], Nauchnyy Vestnik Gumanitarno-Sotsialnogo Instituta, 2025, no. 20, 5 p. (In Russian) EDN: https://elibrary.ru/MNHKMT
3. Churkin Ya. M. Szhatie algoritmom Khaffmana [Huffman Coding for Data Compression], Instrumenty i mekhanizmy ustoychivogo innovatsionnogo razvitiya: sbornik statey po itogam Vserossiyskoy nauchno-prakticheskoy konferentsii [Tools and Mechanisms for Sustainable Innovative Development: A Collection of Articles Based on the Proceedings of the All-Russian Scientific and Practical Conference], Samara, Russia, February 06, 2022. Sterlitamak, Agency of International Research, 2022, pp. 20–22. (In Russian) EDN: https://elibrary.ru/YSDNPE
4. Levin I. I., Dudnikov E. A. Strukturnaya modifikatsiya metoda Khaffmana dlya szhatiya plotnykh potokov dannykh bez poter na RVS [Structural Modification of The Huffman Method for Compression of Dense Data Streams Without Loss on a RCS], Izvestiya Yuzhnogo federalnogo universiteta. Tekhnicheskie nauki [Izvestiya Southern Federal University. Engineering Sciences], 2024, no. 5 (241), pp. 48–58. DOI:https://doi.org/10.18522/2311-3103-2024-5-48-58. (In Russian) EDN: https://elibrary.ru/NDMYMD
5. Howard P. G., Vitter J. S. Arithmetic Coding for Data Compression, Proceedings of the IEEE, 1994, Vol. 82, iss. 6, pp. 857–865. DOI:https://doi.org/10.1109/5.286189.
6. Dobychin R. S., Petrov A. R. Sravnitelnyy analiz raboty algoritmov Shennona — Fano i Khaffmana [Comparative Analysis of the Shannon — Fano Algorithm and the Huffman Algorithm for Data Coding], Mezhdistsiplinarnye issledovaniya v oblasti matematicheskogo modelirovaniya i informatiki: materialy 7-y nauchno-prakticheskoy internet-konferentsii [Interdisciplinary Research on Mathematical Modelling and Computer Science: Proceedings of the 7th Scientific and Practical Internet Conference], Togliatti, Russia, March 30–31, 2016. Ulyanovsk, Zebra Publishing House, 2016, pp. 12–14. (In Russian) EDN: https://elibrary.ru/VPXXLB
7. Kreft S., Navarro G. LZ77-Like Compression with Fast Random Access, Proceedings of the 2010 Data Compression Conference, Snowbird, UT, USA, March 24–26, 2010. Institute of Electrical and Electronics Engineers, 2010, pp. 239–248. DOI:https://doi.org/10.1109/DCC.2010.29.
8. Arroyuelo D., et al. LZ78 Compression in Low Main Memory Space. In: Fici G., et al. (eds) String Processing and Information Retrieval (SPIRE 2017): Proceedings of the 24th International Symposium, Palermo, Italy, September 26–29, 2017. Lecture Notes in Computer Science. Vol. 10508. Cham, Springer, 2017, pp. 38–50. DOI:https://doi.org/10.1007/978-3319-67428-5_4.
9. Dheemanth H. N. LZW Data Compression, American Journal of Engineering Research, 2014, vol. 3, iss. 2, pp. 22–26.
10. Faykus M. H., Calhoun J., Smith M. Lossy and Lossless Compression for BioFilm Optical Coherence Tomography (OCT), SC-W 2023: Proceedings of the Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO, USA, November 12–17, 2023. New York, Association for Computing Machinery, 2023, pp. 281–288. DOI:https://doi.org/10.1145/3624062.3625125.
11. Kösters M., et al. PymzML v2.0: Introducing a Highly Compressed and Seekable GZip Format, Bioinformatics, 2018, vol. 34, no. 14, pp. 2513–2514. DOI:https://doi.org/10.1093/bioinformatics/bty046. EDN: https://elibrary.ru/VATXGQ
12. Oswal S., Singh A., Kumari K. Deflate Compression Algorithm, International Journal of Engineering Research and General Science, 2016, vol. 4, iss. 1, pp. 430–436.
13. Zheng L., et al. Design and Optimization of Zstandard Algorithm Based on Concurrent Streaming of Multiple Hash Tables, Proceedings of the Second International Conference on Laser, Optics and Optoelectronic Technology (LOPET 2022), Qingdao, China, May 20–22, 2022. Proceedings of SPIE. Vol. 12343. Bellingham (WA), Society of Photo-Optical Instrumentation Engineers, 2022, pp. 571–576. DOI:https://doi.org/10.1117/12.2649516.
14. Rzun I. G., et al. Yakhimovich I. S. Effektivnoe szhatie log-faylov: analiz proizvoditelnosti LZ4, Zstandard i gibridnogo podkhoda [Efficient Log File Compression: Performance Analysis of LZ4, Zstandard, and a Hybrid Approach], Vestnik Akademii znaniy [Bulletin of the Academy of Knowledge], 2025, no. 4 (69), pp. 435–443. (In Russian) EDN: https://elibrary.ru/EKTQIC
15. Alakuijala J., et al. Brotli: A General-Purpose Data Compressor, ACM Transactions on Information Systems, 2019, vol. 37, iss. 1, art. no. 4, 30 p. DOI:https://doi.org/10.1145/3231935.
16. Gavrikova S. V. Obzor baz dannykh vremennykh ryadov [Time Series Databases Overview], International Journal of Open Information Technologies, 2023, vol. 11, no. 11, pp. 83–102. (In Russian) EDN: https://elibrary.ru/JCGZBP
17. Bisong E. Google BigQuery. In: Bisong E. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners. Berkeley (CA), Apress, 2019, pp. 485–517. DOI:https://doi.org/10.1007/9781-4842-4470-8_38.
