Topology-Aware Prediction of Virtual Network Function Resource Requirements

Mijumbi, Rashid and Hasija, Sidhant and Davy, Steven and Davy, Alan and Jennings, Brendan and Boutaba, Raouf (2017) Topology-Aware Prediction of Virtual Network Function Resource Requirements. IEEE Transactions on Network and Service Management, 14 (1). pp. 106-120. ISSN 1932-4537

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Network functions virtualization (NFV) continues to gain attention as a paradigm shift in the way telecommunications services are deployed and managed. By separating network function from traditional middleboxes, NFV is expected to lead to reduced capital expenditure and operating expenditure, and to more agile services. However, one of the main challenges to achieving these objectives is how physical resources can be efficiently, autonomously, and dynamically allocated to virtualized network function (VNF) whose resource requirements ebb and flow. In this paper, we propose a graph neural network-based algorithm which exploits VNF forwarding graph topology information to predict future resource requirements for each VNF component (VNFC). The topology information of each VNFC is derived from combining its past resource utilization as well as the modeled effect on the same from VNFCs in its neighborhood. Our proposal has been evaluated using a deployment of a virtualized IP multimedia subsystem, and real VoIP traffic traces, with results showing an average prediction accuracy of 90%, compared to 85% obtained while using traditional feed-forward neural networks. Moreover, compared to a scenario where resources are allocated manually and/or statically, our technique reduces the average number of dropped calls by at least 27% and improves call setup latency by over 29%.

Item Type: Article
Additional Information: Funding Information: This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. Publisher Copyright: © 2017 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1705
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:06
Last Modified: 10 Aug 2023 07:25

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