Software-defined networks can enable a variety of concurrent, dynamically instantiated, measurement tasks, that provide fine-grain visibility into network traffic. Recently, there have been many proposals to configure TCAM counters in hardware switches to monitor traffic. However, the TCAM memory at switches is fundamentally limited and the accuracy of the measurement tasks is a function of the resources devoted to them on each switch.
When many flows are multiplexed on a non-saturated link, their volume changes over short timescales tend to cancel each other out, making the average change across flows close to zero. This equilibrium property holds if the flows are nearly independent, and it is violated by traffic changes caused by several, potentially small, correlated flows. Many traffic anomalies (both malicious and benign) fit this description. Based on this observation, we exploit equilibrium to design a computationally simple detection method for correlated anomalous flows.