research-article

Virtual Network Function Placement Considering Resource Optimization and SFC Requests in Cloud Datacenter

Authors:

jianing PeiAuthors Info & Claims

IEEE Transactions on Parallel and Distributed Systems, Volume 29, Issue 7

Pages 1664 - 1677

https://doi.org/10.1109/TPDS.2018.2802518

Published: 01 July 2018 Publication History

Abstract

Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.

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cover image IEEE Transactions on Parallel and Distributed Systems

IEEE Transactions on Parallel and Distributed Systems Volume 29, Issue 7

July 2018

236 pages

ISSN:1045-9219

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1045-9219 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

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Published: 01 July 2018

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Cited By

He RRen BXie JGuo DZhao L(2024)Efficient Online Scheduling of Service Function Chains Across Multiple Geo-Distributed RegionsIEEE Transactions on Network and Service Management10.1109/TNSM.2024.338321321:3(3440-3453)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3383213
Mao YShang XLiu YYang Y(2024)Joint Virtual Network Function Placement and Flow Routing in Edge-Cloud ContinuumIEEE Transactions on Computers10.1109/TC.2023.334767173:3(872-886)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TC.2023.3347671
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https://dl.acm.org/doi/10.1007/s11277-024-11389-5
Umrao BYadav D(2023)APVNFCCybernetics and Information Technologies10.2478/cait-2023-000323:1(59-74)Online publication date: 1-Mar-2023
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Tian FLiang JLiu J(2023)Joint VNF Parallelization and Deployment in Mobile Edge NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2023.326076722:11(8185-8199)Online publication date: 1-Nov-2023
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Rui LChen SWang SGao ZQiu XLi WGuo S(2023)SFC Orchestration Method for Edge Cloud and Central Cloud Collaboration: QoS and Energy Consumption Joint Optimization Combined With Reputation AssessmentIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.330167034:10(2735-2748)Online publication date: 1-Oct-2023
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He NYang SLi FTrajanovski SZhu LWang YFu X(2023)Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and RoutingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.324040434:4(1186-1201)Online publication date: 1-Apr-2023
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