Comparison and model of compression techniques for smart cloud log file handling

Compression as data coding technique has seen approximately 70 years of research and practical innovation. Nowadays, powerful compression tools with good trade-offs exist for a range of file formats from plain text to rich multimedia. Yet in the dilemma of cloud providers to reduce log data sizes as much as possible while having to keep as much as possible around for regulatory reasons and compliance processes, many companies are looking for smarter solutions beyond brute compression. In this paper, comprehensive applied research setting around network and system logs is introduced by comparing text compression ratios and performance. The benchmark encompasses 13 tools and 30 tool-configuration-search combinations. The tool and algorithm relationships as well as benchmark results are modelled in a graph. After discussing the results, the paper reasons about limitations of individual approaches and suitable combinations of compression with smart adaptive log file handling. The adaptivity is based on the exploitation of knowledge on format-specific compression characteristics expressed in the graph, for which a proof-of-concept advisor service is provided