Estimators also need shared values to grow together

Network management applications require large numbers of counters in order to collect traffic characteristics for each network flow. However, these counters often barely fit into on-chip SRAM memories. Past papers have proposed using counter estimators instead, thus trading off counter precision for a lower number of bits. But these estimators do not achieve optimal estimation error, and cannot always scale to arbitrary counter values. In this paper, we introduce the CEDAR algorithm for decoupling the counter estimators from their estimation values, which are quantized into estimation levels and shared among many estimators. These decoupled and shared estimation values enable us to easily adjust them without needing to go through all the counters. We demonstrate how our CEDAR scheme achieves the min-max relative error, i.e., can guarantee the best possible relative error over the entire counter scale. We also explain how to use dynamic adaptive estimation values in order to support counter up-scaling and adjust the estimation error depending on the current maximal counter. Finally we implement CEDAR on FPGA and explain how it can run at line rate. We further analyze its performance and size requirements.