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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135515

Title:	Fair VNF Provisioning in NFV Clusters via Node Labeling
Authors:	郭桐惟 Kuo, Tung-Wei Chang, Tzu-Wen Tsai, Ming-Jer
Contributors:	資科系
Keywords:	Max-Min Fairness;Multi-Resource Allocation;Network Function Virtualization;Dominant Resource Fairness
Date:	2020
Issue Date:	2021-06-04 14:36:18 (UTC+8)
Abstract:	We study fair multi-resource allocation in Network Function Virtualization (NFV) clusters, where the relative amounts of (multiple) resources allocated for a virtual network function (VNF) can be flexibly adjusted. In NFV clusters, the fairness across users can benefit from the flexibility of the multi-resource allocation for VNFs, but we also have to address a research challenge: What relative amounts of resources should be allocated to a VNF? Although many studies address fair multi-resource allocation in the literature, they all assume that the relative amounts of resources allocated for a VNF are pre-determined and fixed, which would lead to the poor fairness across users. In this paper, we make the first attempt to propose an algorithm to allocate resources to users under the circumstance of flexible multi-resource allocation for VNFs. Our algorithm is shown to achieve max-min fairness and satisfy two beneficial properties of fair multi-resource allocation: Pareto efficiency and envy-freeness. Simulations also show our algorithm can allocate resources in a fair and efficient way in NFV clusters.
Relation:	IEEE GLOBECOM, IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/GLOBECOM42002.2020.9347991
DOI:	10.1109/GLOBECOM42002.2020.9347991
Appears in Collections:	[資訊科學系] 會議論文

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