Embedded Intelligence for Network Switches and Routers: Architecture, Techniques, and Evaluation
DOI:
https://doi.org/10.63282/3117-5481/WFCMLS26-106Keywords:
AI, ML, Networking, Embedded IntelligenceAbstract
The rapid evolution of network technologies has necessitated the integration of embedded intelligence within network switches and routers to enhance performance, efficiency, and adaptability. This paper explores the architectural frameworks that support embedded intelligence in network devices, focusing on various techniques employed to optimize data processing and routing functionalities. We examine the key components that constitute intelligent network architectures, including machine learning algorithms, real-time data analytics, and adaptive control mechanisms. Furthermore, we evaluate the effectiveness of these techniques through comparative analysis and performance metrics, highlighting their impact on throughput, latency, and energy consumption. By synthesizing current advancements and methodologies, this study aims to provide insights into future directions for designing smarter network infrastructures that can autonomously adapt to dynamic traffic conditions and evolving user demands.
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