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Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
دو دهه AI4NETS - AI / ML برای شبکه های داده: چالش ها و دستورالعمل های تحقیق-2020 The popularity of Artificial Intelligence (AI) –
and of Machine Learning (ML) as an approach to AI, has
dramatically increased in the last few years, due to its outstanding
performance in various domains, notably in image,
audio, and natural language processing. In these domains, AI
success-stories are boosting the applied field. When it comes
to AI/ML for data communication Networks (AI4NETS), and
despite the many attempts to turn networks into learning agents,
the successful application of AI/ML in networking is limited.
There is a strong resistance against AI/ML-based solutions, and
a striking gap between the extensive academic research and the
actual deployments of such AI/ML-based systems in operational
environments. The truth is, there are still many unsolved complex
challenges associated to the analysis of networking data through
AI/ML, which hinders its acceptability and adoption in the
practice. In this positioning paper I elaborate on the most
important show-stoppers in AI4NETS, and present a research
agenda to tackle some of these challenges, enabling a natural
adoption of AI/ML for networking. In particular, I focus the
future research in AI4NETS around three major pillars: (i) to
make AI/ML immediately applicable in networking problems
through the concepts of effective learning, turning it into a useful
and reliable way to deal with complex data-driven networking
problems; (ii) to boost the adoption of AI/ML at the large scale
by learning from the Internet-paradigm itself, conceiving novel
distributed and hierarchical learning approaches mimicking the
distributed topological principles and operation of the Internet
itself; and (iii) to exploit the softwarization and distribution of
networks to conceive AI/ML-defined Networks (AIDN), relying on
the distributed generation and re-usage of knowledge through
novel Knowledge Delivery Networks (KDNs). Index Terms: Machine Learning | Artificial Intelligence | Data Communication Networks | Data-driven networking | Knowledge Delivery Networks (KDNs) | AI/ML-defined networking (AIDN) |
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