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AI-Assisted Knowledge-Defined Network Orchestration for Energy-Efficient Data Center Networks

In this article, we discuss the design and implementation of a novel DCN system, which utilizes a knowledge-defined NO-M to operate a HOEDCN cost-effectively and energy-efficiently. The motivations behind the proposed HOE-DCN system are the urgent need to address the scalability, energy, and manageability issues in existing DCN systems. To realize the knowledge-defined NO-M, we follow the principle of predictive analytics in the human brain to design three artificial intelligence modules based on deep learning and make them operate collaboratively. The proposed HOE-DCN system is implemented in a network testbed, and we conduct experiments that involve both control and data plane operations to demonstrate its advantages. The experimental results show that the HOE-DCN simultaneously achieves high-performance service provisioning and improved energy efficiency. Furthermore, by analyzing the pros and cons of the HOE-DCN system, we also point out several directions to work on in the future.

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Informasi Detail

Judul Seri

IEEE COMMUNICATIONS MAGAZINE; Vol.58 No.1 January 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 86 - 92

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

ARTIFICIAL INTELLIGENCE
OPTICAL PACKET SWITCHING
OPTICAL SWITCHES
ENERGY EFFICIENCY
SERVERS
HIGH-SPEED OPTICAL TECHNIQUES

Info Detail Spesifik

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Pernyataan Tanggungjawab

Wei Lu, Lipei Liang, Bingxin Kong, Baojia Li, Zuqing Zhu

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