  Our society has never been more dependent on computer networks. Effective
utilization of networks requires a detailed understanding of the normal
background behaviors of network traffic. Large-scale measurements of networks
are computationally challenging. Building on prior work in interactive
supercomputing and GraphBLAS hypersparse hierarchical traffic matrices, we have
developed an efficient method for computing a wide variety of streaming network
quantities on diverse time scales. Applying these methods to 100,000,000,000
anonymized source-destination pairs collected at a network gateway reveals many
previously unobserved scaling relationships. These observations provide new
insights into normal network background traffic that could be used for anomaly
detection, AI feature engineering, and testing theoretical models of streaming
networks.
