CFseq: A Framework for Constructing Compression-Friendly Field Sequences for Network Logs
Yunwei Dai, Tao Huang, Shuo Wang, Yong Wang · 2025
The rapid growth of network traffic has resulted in a substantial increase in log data, creating significant challenges for storage and processing. Although general-purpose compression algorithms are widely used, they often underperform on network logs due to their inability to exploit inherent structural characteristics. While advanced compression techniques can offer better performance, they typically require extensive system modifications and add deployment complexity. This paper presents CFseq, a lightweight and efficient framework designed to construct compression-friendly field sequences that improve the compressibility of network logs. CFseq is founded on two key observations: first, some fields exhibit high redundancy; second, others contain shared prefixes or suffixes that are well suited to compression algorithms. The framework comprises two modules: the Text Similarity Enhancement module, which ranks fields based on information entropy, and the Brute-Force Search module, which identifies the optimal field order for compression. CFseq operates without modifying existing compression or decompression pipelines, allowing for seamless and low-cost integration. Experimental results show that CFseq improves the compression ratios of general-purpose compressors by up to 32 % and enhances the performance of the state-of-theart advanced compressor Denum by up to 20 %.