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نتیجه جستجو - Data Communication Networks

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
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
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