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1 |
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) |
مقاله انگلیسی |
2 |
Big data analytics for wireless and wired network design: A survey
تجزیه و تحلیل داده های بزرگ برای طراحی شبکه بی سیم و سیمی: یک مرور-2018 Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile
network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick
and diverse manner in the form of big data. Big data analytics can process large amounts of raw data
and extract useful, smaller-sized information, which can be used by different parties to make reliable
decisions.
In this paper, we conduct a survey on the role that big data analytics can play in the design of data
communication networks. Integrating the latest advances that employ big data analytics with the net
works’ control/traffic layers might be the best way to build robust data communication networks with
refined performance and intelligent features. First, the survey starts with the introduction of the big data
basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle em
ploying big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics.
Third, there is a detailed review of the current academic and industrial efforts toward network design
using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics
in network design. Finally, we highlight several future research directions. To the best of our knowledge,
this is the first survey that addresses the use of big data analytics techniques for the design of a broad
range of networks.
Keywords: Big data analytics , Network design , Self-optimization , Self-configuration , Self-healing network |
مقاله انگلیسی |