Net-in-AI: A Computing-Power Networking Framework with Adaptability, Flexibility, and Profitability for Ubiquitous AI

Xiaofei Wang, Xiaoxu Ren, Chao Qiu*, Yifan Cao, Tarik Taleb, Victor C.M. Leung

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Along with the unprecedented development of artificial intelligence (AI), a considerable number of intelligent applications are universally recognized to significantly facilitate the evolution of anthropogenic activities. The abundant AI computing power is one of the main pillars to fuel the booming of ubiquitous AI applications. As the computing power proliferates to a multitude of network edges, even end devices, the networking function bridges the gap, on the one hand, among ends-edges-clouds, on the other hand, between the multiple AI computing power and the heterogeneous AI requirements. The emerging new opportunities have spawned the deep integration between computing and networking. However, the complete development of the integrated system is under-addressed, including adaptability, flexibility, and profitability. In this article, we propose a computing-power networking framework for ubiquitous AI by establishing Networking in AI computing-power pool, denoted as Net-in-AI. We design the framework to enable the adaptability for computing-power users, the flexibility for networking, and the profitability for computing-power providers. We then formulate a computing-networking resource allocation problem, with the joint perspective of these three aspects. Experimental results prove the superior performance of the proposed framework in comparison to the current popular schemes.

Original languageEnglish
Article number9293089
Pages (from-to)280-288
Number of pages9
JournalIEEE NETWORK
Volume35
Issue number1
DOIs
Publication statusPublished - Mar 2021
MoE publication typeA1 Journal article-refereed

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