NDLI: Effective and efficient AI-based approaches to cloud resource provisioning

Content Provider	IEEE Xplore Digital Library
Author	Yang Yang Xiaolin Chang Xuanni Du Jiqiang Liu Lin Li
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China (Yang Yang; Xiaolin Chang; Xuanni Du; Jiqiang Liu; Lin Li)
Abstract	This paper aims to design efficient and effective approaches to virtual network embedding (VNE) problem, which deals with the embedding of a requested virtual network (VN) in an underlying physical (substrate network) infrastructure. When the node and link constraints (including CPU, memory, network bandwidth, and network delay) are both taken into account, the VN embedding problem is NP-hard, even in the offline case. The capabilities of some Artificial Intelligence (AI) techniques have been validated in handling the VN problem. In this paper, we propose two efficient and effective VNE algorithms based on differential evolutionary (DE) technique. The extensive simulation results show that DE technique performs some orders of magnitude faster than GA and PSO-based VNE algorithms in achieving the comparable long-term revenue of Infrastructure providers.
Starting Page	1
Ending Page	6
File Size	124829
Page Count	6
File Format	PDF
e-ISBN	9781479920846
DOI	10.1109/ICON.2013.6781960
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-11
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Substrates Simulation Bandwidth Network topology Equations Mathematical model Differential Evolutionary Algorithm Resource Allocation Virtual Network Embedding Artificial Intelligence
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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