...Motivation The classic fields reporting packets and bytes in flow records have long been used by network engineers and analysts to gauge both the health and operational characteristics of networks. For example it is well-known that the packet counts in TCP connections are typically balanced due to the end-points needing to send frequent acknowledgments even though the application level data transfer may be asymmetric. One aspect of communication that is interesting not only for traffic engineering but also for security is the producer/consumer relationship. That is what roles do the participants in a communication play; data producer data consumer or both? In typical client-server associations it is tempting to assign the role of producer to the server and consumer to the client but this view is too simplistic. For example a file server frequently oscillates between the two roles depending on whether files are being read or written; or from a security perspective the transformation of a node from being a consumer to being a producer may be the only wire-line evidence that it has been compromised for data exfiltration. In the study below we look at these relationships in more detail and slice them in different ways. In order to get to the semantic relationships we define a new flow metric for the producer/consumer relationship based on the application data bytes exchanged. ...PCR Metric definition The metric is based on the bytes transferred between two end-points not including the bytes required for transport purposes. The Argus system conveniently provides these values in the optional 'sappbytes' and 'dappbytes' fields and we will use these for this study. Our producer/consumer metric is a value from -1.00 to 1.00 defined as pcr = (sappbytes-dappbytes)/(sappbyte+dappbytes) Thus a pcr = 1 defines the src as a pure producer; pcr = -1 defines the dst that way; while pcr = 0 indicates perfectly balanced exchange. This normalized dimensionless ratio helps compare entities which may be operating similarly but at very different volumes. Likewise it can be computed not only for a single flow but also over all the flows of a host or subnet or port - any categorical flow variable. When aggregated this way the metric is a measure of export/import activity. In this presentation we look at uses of the PCR across a range of variables in traffic drawn from a large production network.