Several network management applications require high fidelity estimates of flow-level metrics. Given the inadequacy of current packet sampling based solutions, many proposals for application-specific monitoring algorithms have emerged. While these provide better accuracy, they increase router complexity and require router vendors to commit to hardware primitives without knowing how useful they will be to future monitoring applications. We argue that such complexity is unnecessary and build a case for a "RISC" approach for flow monitoring, in which generic collection primitives on routers provide data from which other traffic metrics can be computed using separate, offline devices. We demonstrate one such RISC approach by combining two well-known primitives: flow sampling and sample and hold. We show that allocating a router's memory resources to these generic primitives can provide similar or better accuracy on metrics of interest than dividing the resources among several metric-specific algorithms. Moreover, this approach better insulates router implementations from changing monitoring needs.

32 pages

*Carnegie Mellon University and University of North Carolina-Chapel Hill

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