CELL: Counter Estimation for Per-flow Traffic in Streams and Sliding Windows

Measurement capabilities are fundamental for a variety of network applications. Typically, recent data items are more relevant than old ones, a notion we can capture through a sliding window abstraction. These capabilities require a large number of counters in order to monitor the traffic of all network flows. However, SRAM memories are too small to contain these counters. Previous works suggested replacing counters with small estimators, trading accuracy for reduced space. But these estimators only focus on the counters' size, whereas often flow ids consume more space than their respective counters. In this work, we present the CELL algorithm that combines estimators with efficient flow representation for superior memory reduction. We also extend CELL to the sliding window model, which prioritizes the recent data, by presenting two variants named RAND-CELL and SHIFT-CELL. We formally analyze the error and memory consumption of our algorithms and compare their performance against competing approaches using real-world Internet traces. These measurements exhibit the benefits of our work and show that CELL consumes at least 30% less space than the best-known alternative. The code is available in open source.