Network Traffic Characteristics of Machine Learning Frameworks Under the Microscope

High computational demands of complex deep learning models led to workload distribution across multiple machines. Many frameworks for distributed machine learning (DML) have been developed and are employed in practice for orchestrating workload distribution. In this paper, we analyze and compare network behaviors of three widely used state-of-the-art DML frameworks. The study reveals that traffic can largely vary across the frameworks. While some frameworks exhibit well predictable patterns, others are less structured. We further explore whether and how it is possible to relate the network traffic to the DML jobs' attributes, and present a multiple linear regression model accordingly. Our results can inform the networking community about traffic characteristics and contribute toward the generation of realistic DML traffic for simulation studies.