research-article

Towards an efficient VNF placement in network function virtualization

Authors:

Guanghui WangAuthors Info & Claims

Volume 138, Issue C

Pages 81 - 89

https://doi.org/10.1016/j.comcom.2019.03.005

Published: 15 April 2019 Publication History

Abstract

Network Function Virtualization (NFV) decouples network function (also called middlebox function) software from specified appliances onto general shared servers. Thus, it has been being regarded as a promising technology to overcome high Capital Expenditures (CAPEX) and Operational Expenditures (OPEX) on the middlebox deployment and maintenance. In NFV, the network function deployed on servers with virtual machine is named as the virtualized network function (VNF). One critical issue is VNF placement for provisioning service function chains (SFC), which deals with the resource allocation to VNFs and routing path between them. The problem is inherently NP-hard. Current VNF placement algorithms do not scale with respect to the network size, leading to these algorithms not applicable in large-scaled scenarios where VNFs have to be placed in a timely way. Therefore, this paper aims to solve the problem of VNF placement in a scalable way. We attempt to narrow the target searching space of VNF placement by introducing a smaller accessible scope where the locations of VNFs are confined. The accessible scope constraint is generic for different conventional VNF placement algorithms, which can be used in conjunction with existing algorithms to improve time efficiency. Two algorithms to be evaluated are chosen to run with the accessible scope constraint under medium and large scales of scenarios. Results show that the algorithms with the constraint of accessible scope have significant time efficiency improvements especially in large-scale scenario and the solution quality is at least comparable.

References

[1]

ETSI GS NFV 002 V1.2.1, Network Functions Virtualisation (NFV) Architectural Framework, ETSI, 2014.

[2]

Network Functions Virtualisation – Introductory White Paper. http://portal.etsi.org/NFV/NFV_White_Paper.pdf.

[3]

P. Quinn, T. Nadeau, RFC 7948, Problem Statement for Service Function Chaining, Internet Engineering Task Force (IETF), ed, 2015.

[4]

Yi Bo, Wang Xingwei, Li Keqin, Das Sajal k., Huang Min, A comprehensive survey of network function virtualization, Comput. Netw. 133 (2018) 212–262.

[5]

Herrera Juliver Gil, Botero Juan Felipe, Resource allocation in NFV:A comprehensive survey, IEEE Trans. Netw. Serv. Manag. 13 (3) (2016) 518–532.

[6]

M.C. Luizelli, L.R. Bays, L.S. Buriol, M.P. Barcellos, L.P. Gaspary, Piecing together the nfv provisioning puzzle: Efficient placement and chaining of virtual network functions, in: IFIP/IEEE IM, 2015.

[7]

Moens H., De Turck F., Vnf-p: a model for efficient placement of virtualized network functions, in: Proceedings of the 10th International Conference on Network and Service Management (CNSM 2014), 2014, pp. 418–423.

[8]

Faizul Bari Md., Chowdhury Shihabur Rahman, Ahmed Reaz, Boutaba Raouf, Muniz Otto Carlos, Duarte Bandeira, Orchestrating virtualized network functions, IEEE Trans. Netw. Serv. Manag. 13 (4) (2016) 725–739.

[9]

Lewin-Eytan L., Naor J., Cohen R., Raz D., Near optimal placement of virtual network functions, in: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM 2015), IEEE, New York, NY, USA, 2015, pp. 1346–1354.

[10]

Addis B., Belabed D., Bouet M., Secci S., Virtual network functions placement and routing optimization, in: IEEE 4th International Conference on Cloud Networking (CloudNet), 2015, pp. 171–177.

[11]

Gu Lin, Tao Sheng, Zeng Deze, Jin Hai, Communication cost efficient virtualized network function placement for big data processing, in: IEEE INFOCOM First International Workshop on Big Data Sciences, Technologies and Applications (BDSTA), 2016.

[12]

Ghribi Chaima, Mechtri Marouen, Zeghlache Djamal, A dynamic programming algorithm for joint VNF placement and chaining, in: Acm Workshop on Cloud-assisted Networking, 2016, pp. 19–24.

[13]

Liu Junjie, Lu Wei, Zhou Fen, Lu Ping, Zhu Zuqing, On dynamic service function chain deployment and readjustment, IEEE Trans. Netw. Serv. Manag. 14 (3) (2017) 543–553.

Digital Library

[14]

Pham C., Tran N.H., Ren S., Saad W., Hong C.S., Traffic-aware and energy efficient vNF placement for service chaining: joint sampling and matching approach, IEEE Trans. Serv. Comput. PP (99) (2017) 1–13,.

[15]

Li X., Qian C., The virtual network function placement problem, in: 2015 IEEE Conference on Computer Communications Workshops, INFOCOM Workshops, Hong Kong, China, April 26 - May 1, 2015, 2015, pp. 69–70.

[16]

Rankothge Windhya, Le Franck, Russo Alessandra, Lobo Jorge, Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms, IEEE Trans. Netw. Serv. Manag. 14 (2) (2017).

[17]

Mijumbi R., Serrat J., Gorricho J., Bouten N., De Turk F., Davy S., Design and evaluation of algorithms for mapping and scheduling of virtual network functions, in: Network Softwarization, IEEE 1st International Conference on, 2015, pp. 1–9.

[18]

Fajjari I., Aitsaadi N., Pujolle G., Zimmermann H., VNE-AC: virtual network embedding algorithm based on ant colony metaheuristic, in: 2011 IEEE International Conference on Communications (ICC), IEEE, 2011, pp. 1–6.

[19]

Ma S., et al., Demonstration of online spectrum defragmentation enabled by openflow in software-defined elastic optical networks, in: Proc. OFC, 2014, pp. 1–3.

[20]

Beck Michael Till, Botero Juan Felipe, Scalable and coordinated allocation of service function chains, Comput. Commun. 102 (1) (2017) 78–88.

[21]

Sahhaf Sahel, Tavernier Wouter, Rost Matthias, Schmid Stefan, Colle Didier, Pickavet Mario, Demeester Piet, Comput. Netw. 93 (3) (2015) 492–505.

[22]

Chun B., Iannaccone G., Iannaccone G., Katz R., Lee G., Niccolini L., An energy case for hybrid datacenters, Oper. Syst. Rev. 44 (1) (2010) 76–80.

[23]

Bolla R., Bruschi R., Davoli F., Cucchietti F., Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures, IEEE Commun. Surv. Tutor. 13 (2) (2011) 223–244,.

[24]

Nguyen H.T., Pham N.N., Truong T.H., Tran N.T., Nguyen M.D., Nguyen V.G., Nguyen T.H., Ngo Q.T., Hock D., Schwartz C., Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks, Simul. Modell. Pract. Theory 39 (2013) 20–40,.

[25]

Rivoire S., Ranganathan P., Kozyrakis C., A comparison of high level full-system power models, in: Proc. USENIX Conf. Power Aware Comput. Syst., 2008, p. 3.

[26]

Fan X., Weber W., Barroso L., Power provisioning for a warehouse-sized computer, in: Proc. 34th Annu. ACM Int. Symp. Comput. Archit., 2007, pp. 13–23.

[27]

Economou D., Rivoire S., Kozyrakis C., Ranganathan P., Fullsystem power analysis and modeling for server environments, in: Proc. Workshop Modeling, Benchmarking, Simulation, 2006, pp. 70–77.

[28]

Bouet M., Leguay J., Conan V., Cost-based placement of vDPI functions in NFV infrastructures, in: 1st IEEE Conference on Network Softwarization (NetSoft), 2015, pp. 1–9.

[29]

Papagiannaki K., Taft N., Zhang Zhi-Li., Diot C., Long-term forecasting of Internet backbone traffic: observations and initial models, in: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications, Vol. 2, IEEE Societies, 2003, pp. 1178–1188.

[30]

Gmach D., Rolia J., Cherkasova L., Kemper A., Workload analysis and demand prediction of enterprise data center applications, in: Proc. IEEE IISWC, 2007.

[31]

Zhang Boxun, Kreitz Gunnar, Isaksson Marcus, Ubillos Javier, Urdaneta Guido, Pouwelse Johan A., Epema Dick, Understanding user behavior in spotify, in: IEEE INFOCOM, 2013.

[32]

Yang Yang, Chang Xiaolin, Liu Jiqiang, Li Lin, Towards robust green virtual cloud data center provisioning, IEEE Trans. Cloud Comput. 5 (2) (2017) 168–181.

[33]

Eramo Vincenzo, Miucci Emanuele, Ammar Mostafa, Lavacca Francesco Giacinto, An approach for service function chain routing and virtual function network instance migration in network function virtualization architectures, IEEE/ACM Trans. Netw. 25 (4) (2017).

[34]

Al-Fares M., Loukissas A., Vahdat A., A scalable, commodity data center network architecture, in: Proc. ACM SIGCOMM, 2008, pp. 63–74.

[35]

Z. Liu, S. Wang, Y. Wang, Service function chaining resource allocation: A survey, Cornell Univ. Library, Ithaca, NY, USA, Tech. Rep., arXiv:1608.00095,2016. [Online]. Available: https://arxiv.org/abs/1608.00095.

[36]

Ghaznavi M., et al., Elastic virtual network function placement, in: Proc. 4th IEEE Conf. Cloud Netw. (CloudNet), 2015, pp. 1–6.

[37]

Kikuchi H., Takahashi K., Zipf distribution model for quantifying risk of re-identification from trajectory data, in: Proc. 13th Annu. Conf. Privacy, Secur. Trust (PST), 2015, pp. 14–21.

Cited By

Yue YTang XLiang YCao CXu LYang WZhang Z(2025)DeepSelector: A Deep Learning-Based Virtual Network Function Placement Approach in SDN/NFV-Enabled NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.348377924:3(1759-1773)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TMC.2024.3483779
Moradi MAhmadi MPourKarimi L(2024)Virtualized network functions resource allocation in network functions virtualization using mathematical programmingComputer Communications10.1016/j.comcom.2024.107963228:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.comcom.2024.107963
Yue YSun SZhang XLi RYang WCao CTang X(2024)VNF placement in NFV-enabled networks: considering time-varying workloads and multi-tenancy with a throughput optimization heuristicComputing10.1007/s00607-024-01336-4106:11(3657-3690)Online publication date: 10-Aug-2024
https://dl.acm.org/doi/10.1007/s00607-024-01336-4
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Index Terms

Towards an efficient VNF placement in network function virtualization
1. Networks
  1. Network services
    1. Network management
2. Software and its engineering

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Computer Communications

Computer Communications Volume 138, Issue C

Apr 2019

116 pages

ISSN:0140-3664

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 April 2019

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Cited By

Yue YTang XLiang YCao CXu LYang WZhang Z(2025)DeepSelector: A Deep Learning-Based Virtual Network Function Placement Approach in SDN/NFV-Enabled NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2024.348377924:3(1759-1773)Online publication date: 1-Mar-2025
https://dl.acm.org/doi/10.1109/TMC.2024.3483779
Moradi MAhmadi MPourKarimi L(2024)Virtualized network functions resource allocation in network functions virtualization using mathematical programmingComputer Communications10.1016/j.comcom.2024.107963228:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.comcom.2024.107963
Yue YSun SZhang XLi RYang WCao CTang X(2024)VNF placement in NFV-enabled networks: considering time-varying workloads and multi-tenancy with a throughput optimization heuristicComputing10.1007/s00607-024-01336-4106:11(3657-3690)Online publication date: 10-Aug-2024
https://dl.acm.org/doi/10.1007/s00607-024-01336-4
Attaoui WSabir EElbiaze HGuizani M(2023)VNF and CNF Placement in 5G: Recent Advances and Future TrendsIEEE Transactions on Network and Service Management10.1109/TNSM.2023.326400520:4(4698-4733)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1109/TNSM.2023.3264005
Lübben CSchäffner SPahl M(2022)Continuous Microservice Re-Placement in the IoTNOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS54207.2022.9789780(1-6)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1109/NOMS54207.2022.9789780
Liu HDing SWang SZhao GWang C(2022)Multi-objective Optimization Service Function Chain Placement Algorithm Based on Reinforcement LearningJournal of Network and Systems Management10.1007/s10922-022-09673-530:4Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10922-022-09673-5
Lahlou LFia AKara NLeivadeas A(2021)VAPNIC: A VersAtile shortest path-free VNF Placement using a divide-and-coNquer tactIC2021 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM46510.2021.9685195(1-7)Online publication date: 7-Dec-2021
https://dl.acm.org/doi/10.1109/GLOBECOM46510.2021.9685195
Laroui MNour BMoungla HCherif MAfifi HGuizani M(2021)Edge and fog computing for IoTComputer Communications10.1016/j.comcom.2021.09.003180:C(210-231)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.comcom.2021.09.003

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