NetJIT: Bridging the Gap from Traffic Prediction to Preknowledge for Distributed Machine Learning

Today's distributed machine learning (DML) introduces heavy traffic load, making the network one of the primary bottlenecks. To mitigate this bottleneck, existing state-of-the-art network optimization methods, such as traffic or topology engineering, are proposed to adapt to real-time traffic. However, current traffic measurement and prediction methods struggle to collect sufficiently fine-grained and accurate traffic patterns. This limitation impedes the ability of cutting-edge network optimization techniques to react agilely to the ever-changing traffic demands of DML jobs. This paper proposes NetJIT, a novel program-behavior-aware toolkit for foreseeing the traffic pattern of DML. To the best of our knowledge, this is the first work proposing the use of just-in-time (JIT) program analysis for real-time traffic measurement. In DML applications, communication behavior is primarily determined by the previously computed results. NetJIT leverages this characteristic to anticipate communication details by tracing and analyzing the data relations in the computation process. This capability enables the deployment of optimization strategies in advance. We deploy NetJIT in real-world network optimization for traffic preknowledge. Evaluation with the self-built testbed demonstrates that NetJIT can achieve up to about 97% less error of detecting communication events compared with other methods. Simulations with real-world DML workloads further illustrate that NetJIT enables more precise network optimization, leading to approximately 50% better network performance w.r.t the metrics including average iteration time, throughput, and average packet delay.