Problem
Research questions and friction points this paper is trying to address.
rule-based compression
network traffic
header compression
structured data
automated rule learning
Learning Compression Rules for Network Traffic
🤖 AI Summary
This work addresses the high redundancy in header fields of structured network traffic by proposing RECAP, an automated rule compression method that operates without human intervention. RECAP innovatively integrates unsupervised structure discovery with constrained optimization: it first recursively partitions packets using a normalized entropy ratio criterion to identify redundant patterns, then selects the subset of rules yielding maximal compression gain under a hard constraint on rule count via dynamic programming, and finally applies short-code replacements in accordance with the IETF SCHC standard. Experiments on four real-world datasets from IoT and 5G core networks demonstrate that RECAP significantly outperforms expert-designed compression rule sets, achieving substantially higher compression efficiency with only a small number of learned rules.
📝 Abstract
We study the problem of learning compact rule-based compressors for structured network traffic. Each packet is a record of header fields that are highly redundant within a flow, and a compressor is a small set of rules matching such records and replacing predictable fields with short codes. We cast rule learning as a two-stage problem: (i) an unsupervised structure-discovery stage that recursively partitions training packets using a normalized entropy-ratio criterion robust to small samples, and (ii) a constrained selection stage that uses dynamic programming to pick the rule subset maximizing expected compression gain under a hard budget on the number of installable rules. We instantiate the framework on Static Context Header Compression (SCHC), the IETF standard for rule-based header compression in constrained networks, and evaluate it on four real-world Internet-of-Things and 5G core-network datasets. Our method, Robust Entropy Clustering for Adaptive comPression (RECAP), surpasses expert-engineered rule sets with a small number of learned rules and removes the need for manual rule design.
Innovation
Methods, ideas, or system contributions that make the work stand out.
rule-based compression
unsupervised structure discovery
entropy-ratio clustering
dynamic programming
SCHC
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