Not All Microseconds are Equal: Fine-Grained Per-Flow Measurements with Reference Latency Interpolation
New applications such as algorithmic trading and high-performance computing require extremely low latency (in microseconds). Network operators today lack sufficient fine-grain measurement tools to detect, localize and repair performance anomalies and delay spikes that cause application SLA violations. A recently proposed solution called LDA provides a scalable way to obtain latency, but only provides aggregate measurements. However, debugging application-specific problems requires per-flow measurements, since different flows may exhibit significantly different characteristics even when they are traversing the same link. To enable fine-grained per-flow measurements in routers, we propose a new scalable architecture called reference latency interpolation (RLI) that is based on our observation that packets potentially belonging to different flows that are closely spaced to each other exhibit similar delay properties. In our evaluation using simulations over real traces, we show that RLI achieves a median relative error of 12% and one to two orders of magnitude higher accuracy than previous per-flow measurement solutions with small overhead.
measurement algorithms active measurement approximation