  Matrix/array analysis of networks can provide significant insight into their
behavior and aid in their operation and protection. Prior work has demonstrated
the analytic, performance, and compression capabilities of GraphBLAS
(graphblas.org) hypersparse matrices and D4M (d4m.mit.edu) associative arrays
(a mathematical superset of matrices). Obtaining the benefits of these
capabilities requires integrating them into operational systems, which comes
with its own unique challenges. This paper describes two examples of real-time
operational implementations. First, is an operational GraphBLAS implementation
that constructs anonymized hypersparse matrices on a high-bandwidth network
tap. Second, is an operational D4M implementation that analyzes daily cloud
gateway logs. The architectures of these implementations are presented.
Detailed measurements of the resources and the performance are collected and
analyzed. The implementations are capable of meeting their operational
requirements using modest computational resources (a couple of processing
cores). GraphBLAS is well-suited for low-level analysis of high-bandwidth
connections with relatively structured network data. D4M is well-suited for
higher-level analysis of more unstructured data. This work demonstrates that
these technologies can be implemented in operational settings.
