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DIDA: Distributed In-Network Intelligent Data Plane for Machine Learning Applications
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AI
DOI：10.1109/TNSM.2025.3548477.png)
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Abstract
En 中文
Recent advances in network switch designs have enabled machine learning inference directly within the switch at line speed. However, hardware constraints limit switches capabilities of tracking stateful features essential for accurate inference, as the demand for these features grows rapidly with line rates. To address this, we propose DIDA, a distributed in-network machine learning approach. In DIDA, feature extraction occurs at the host, features are transmitted via in-band telemetry, and inference is performed on the switches. In this paper, we evaluate the effectiveness and efficiency of this architecture. We examine its impact on network bandwidth, CPU and memory usage at the host, and its robustness across different feature sets and deep neural network classifications.
Keywords:
Intelligent data plane
computer network management
distributed computing
machine learning
network security
telemetry
Journal
IF:
5.4
Papers:
528
Citations:
9.2K