Robust Distributed Monitoring of Traffic Flows

Abstract

Scalable monitoring of traffic flows faces challenges posed by unrelenting traffic growth, device heterogeneity, and load unevenness. We explore an approach that tackles these challenges by shifting a portion of the monitoring-task execution from an overloaded network element to another element that has spare resources. Moving the entire execution of the task to a lightly loaded element might be infeasible because execution on multiple elements is inherent in the task or requires at least partial participation by the particular overloaded element (e.g., flow-size computation at the ingress element for billing purposes). Distributed execution of a stateful traffic-monitoring task has to be robust against packet reordering or loss, i.e., network noise. This paper designs robust traffic monitoring where the goal is to determine a flow metric for each flow exactly in spite of network noise. We follow the open-loop paradigm that does not add any control packets, communicates flow state in-band by appending few (on the order of 2 or 4) control bits to packets of the monitored flows, and keeps latency low. We consider the task of flow-size computation, analytically derive conditions assuring correct operation of the designed algorithms, and evaluate the algorithms on realistic traffic traces. The algorithms successfully distribute the monitoring-task load without imposing significant computation or storage overhead.