دانلود مقاله انگلیسی رایگان:دو دهه AI4NETS - AI / ML برای شبکه های داده: چالش ها و دستورالعمل های تحقیق - 2020
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  • Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions
    Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Two Decades of AI4NETS - AI/ML for Data Networks: Challenges & Research Directions


    ترجمه فارسی عنوان مقاله:

    دو دهه AI4NETS - AI / ML برای شبکه های داده: چالش ها و دستورالعمل های تحقیق


    منبع:

    IEEE - NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium;2020; ; ;10.1109/NOMS47738.2020.9110266


    نویسنده:

    Pedro Casas


    چکیده انگلیسی:

    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)


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 6
    حجم فایل: 1005 کیلوبایت

    قیمت: رایگان


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