Per-flow measurement of priority-heterogeneous traffic underpins cloud service-level agreement (SLA) enforcement, anomaly detection, and distributed AI training in high-speed networks, yet remains challenging in the fast L1/L2-cache memory regime where high-priority flows are vastly outnumbered. We propose Charon, a priority-aware sketch that replaces the structural separation used by prior methods with stratified admission sampling: a single, online-adaptive, parameter-free rule decides whether each packet is admitted to the sketch. Across multiple real-world traces, Charon achieves more than 2× higher detection accuracy for high-priority flows than the best baseline and up to four orders of magnitude lower average error than state-of-the-art priority-aware sketches, with the gap widening as memory tightens, at high processing throughput. The implementation on the industry-grade Tofino switch further demonstrates low resource utilization.
Weihe Li, Xicheng Li, Dimitrios P. Pezaros et al.· Conference on Applications,...· 0 citations
In high-speed data streams, identifying long-lived (also referred to as persistent) sparse items is critical, as such patterns may indicate stealthy or low-rate threats yet remain largely underexplored. Although recent studies have begun to examine this problem, existing approaches either suffer from low lookup accuracy due to coarse update strategies or rely on complex data structures with costly update operations, overlooking the practical requirement of deployability. These limitations hinder scalability, particularly as programmable switches and FPGAs are increasingly adopted as data-processing substrates that sustain high-speed processing under strict resource and operational constraints. To address these challenges, we propose Lasso, a lightweight and hardware-conscious approach that achieves high detection accuracy under tight memory budgets while sustaining high processing throughput on industry-grade hardware, including Tofino-1 programmable switches and FPGA platforms. Lasso leverages the observation that long-lived sparse items exhibit a small gap between persistence and frequency, evicting items with large deviations to prioritize promising candidates. In addition, Lasso incorporates fine-grained, temporally aware protection to prevent long-lived items from being prematurely displaced by abundant short-lived items in highly skewed data streams. We further develop a formal analytical model to establish the theoretical soundness of Lasso. Extensive evaluations across CPU, Tofino, and FPGA platforms demonstrate that Lasso delivers high accuracy and throughput while operating within strict resource constraints.
Weihe Li, Jiawei Huang, Zhaoyi Li et al.· Proceedings of the 32nd ACM...· 0 citations
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