Comparative analysis evaluates compression algorithms in cloud environments, indicating optimal usage scenarios.
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.
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Yudnikov et al. (2026) studied this question.
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