research-article

An Efficient Service Function Chaining Placement Algorithm in Mobile Edge Computing

Authors:

Junliang ChenAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 4

Article No.: 32, Pages 1 - 21

https://doi.org/10.1145/3388241

Published: 15 October 2020 Publication History

Abstract

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming–(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.

References

[1]

ETSI GS NFV 003. 2014. Network Functions Virtualisation (NFV): Terminology for Main Concepts in NFV. Retrieved from https://www.etsi.org/deliver/etsi_gs/nfv/001_099/003/01.02.01_60/gs_nfv003v010201p.pdf.

[2]

Mohammad Al-Fares, Alexander Loukissas, and Amin Vahdat. 2008. A scalable, commodity data center network architecture. In Proceedings of the ACM Special Interest Group on Data Communication Conference (SIGCOMM’08).

Digital Library

[3]

H. A. Almohamad and Salih O. Duffuaa. 1993. A linear programming approach for the weighted graph matching problem. IEEE Trans. Pattern Anal. Mach. Intell. 15 (1993), 522--525.

Digital Library

[4]

Monarch Network Architects. 2012. Sample Optical Network Topology Files. Retrieved from http://www.monarchna.com/.

[5]

Md. Faizul Bari, Shihabur Rahman Chowdhury, Reaz Ahmed, and Raouf Boutaba. 2015. On orchestrating virtual network functions. In Proceedings of the 2015 11th International Conference on Network and Service Management (CNSM’15), 50--56.

Digital Library

[6]

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

Digital Library

[7]

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

Digital Library

[8]

Ilias Benkacem, Tarik Taleb, Miloud Bagaa, and Hannu Flinck. 2018. Optimal VNFs placement in CDN slicing over multi-cloud environment. IEEE J. Select. Areas Commun. 36 (2018), 616--627.

[9]

Yishan Chen, Shuiguang Deng, Hongtao Ma, and Jianwei Yin. 2020. Deploying data-intensive applications with multiple services components on edge. Mobile Netw. Appl. 25 (2020), 426--441.

[10]

Zhiqi Chen, Sheng Zhang, Can Wang, Zhuzhong Qian, Mingjun Xiao, Jie Wu, and Imad Jawhar. 2018. A novel algorithm for NFV chain placement in edge computing environments. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM’18), 1--6.

[11]

Margaret Chiosi, Steve Wright, Javan Erfanian, and Brian Smith. SDN and OpenFlow World Congress. 2012. Network Functions Virtualisation (NFV). Retrieved from http://portal.etsi.org/NFV/NFV_White_Paper.pdf.

[12]

The Internet2 community. 2012. Internet2 research network. Retrieved from https://www.internet2.edu/.

[13]

Richard Cziva, Christos Anagnostopoulos, and Dimitrios P. Pezaros. 2018. Dynamic, latency-optimal vNF placement at the network edge. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’18), 693--701.

[14]

Shuiguang Deng, Zhengzhe Xiang, Javid Taheri, Khoshkholghi Ali Mohammad, Jianwei Yin, Albert Zomaya, and Schahram Dustdar. 2020. Optimal application deployment in resource constrained distributed edges. IEEE Trans. Mobile Comput. Early Access (2020), 1--1.

[15]

Shuiguang Deng, Zhengzhe Xiang, Peng Zhao, Javid Taheri, Honghao Gao, Jianwei Yin, and Albert Y. Zomaya. 2020. Dynamical resource allocation in edge for trustable IoT systems: A reinforcement learning method. IEEE Trans. Industr. Inf. 16 (2020), 6103--6113.

[16]

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

Digital Library

[17]

Yan Guo, Shangguang Wang, Ao Zhou, Jinliang Xu, Jie Yuan, and Ching-Hsien Hsu. 2020. User allocation-aware edge cloud placement in mobile edge computing. Software: Practice and Experience 50 (2020), 489--502.

[18]

Yun Chao Hu, Milan Patel, Dario Sabella, Nurit Sprecher, and Valerie Young. 2015. Mobile Edge Computing: A Key Technology Towards 5G. Retrieved from https://www.etsi.org/images/files/etsiwhitepapers/etsi_wp11_mec_a_key_technology_towards_5g.pdf.

[19]

Fatma Ben Jemaa, Guy Pujolle, and Michel Pariente. 2016. QoS-aware VNF placement optimization in edge-central carrier cloud architecture. In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM’16), 1--7.

[20]

Jian Kong, Inwoong Kim, Xi Wang, Qiong Zhang, Hakki C. Cankaya, Weisheng Xie, Tadashi Ikeuchi, and Jason P. Jue. 2017. Guaranteed-availability network function virtualization with network protection and VNF replication. In Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM’17), 1--6.

[21]

Abdelquoddouss Laghrissi, Tarik Taleb, Miloud Bagaa, and Hannu Flinck. 2017. Towards edge slicing: VNF placement algorithms for a dynamic and realistic edge cloud environment. In Proceedings of the 2017 IEEE Global Communications Conference (GLOBECOM’17), 1--6.

[22]

Yuanzhe Li and Shangguang Wang. 2018. An energy-aware edge server placement algorithm in mobile edge computing. In Proceedings of the 2018 IEEE International Conference on Edge Computing (EDGE’18), 66--73.

[23]

Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2018. Latency-aware application module management for fog computing environments. ACM Trans. Internet Technol. 19 (2018), 9:1--9:21.

Digital Library

[24]

Yuyi Mao, Changsheng You, Jun Zhang, Kaibin Huang, and Khaled Ben Letaief. 2017. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutor. 19 (2017), 2322--2358.

[25]

João G. Martins, Mohamed Ahmed, Costin Raiciu, Vladimir Andrei Olteanu, Michio Honda, Roberto Bifulco, and Felipe Huici. 2014. ClickOS and the art of network function virtualization. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI’14).

[26]

Rashid Mijumbi, Joan Serrat, Juan-Luis Gorricho, Niels Bouten, Filip De Turck, and Raouf Boutaba. 2016. Network function virtualization: State-of-the-art and research challenges. IEEE Commun. Surv. Tutor. 18 (2016), 236--262.

Digital Library

[27]

Pawani Porambage, Jude Okwuibe, Madhusanka Liyanage, Mika Ylianttila, and Tarik Taleb. 2018. Survey on multi-access edge computing for internet of things realization. IEEE Commun. Surv. Tutor. 20 (2018), 2961--2991.

Digital Library

[28]

Gamal Sallam and Bo Ji. 2019. Joint placement and allocation of virtual network functions with budget and capacity constraints. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’19) (2019), 523--531.

[29]

S. Song, C. Lee, H. Cho, G. Lim, and J. Chung. 2019. Clustered virtualized network functions resource allocation based on context-aware grouping in 5G edge networks. IEEE Trans. Mobile Comput. 19 (2019), 1072--1083.

[30]

University of Southern California2001. Retrieved from GT-ITM topology generator, https://www.isi.edu/nsnam/ns/ns-topogen.html.

[31]

University of Southern California. 2001. The Network Simulator—ns-2. Retrieved from https://www.isi.edu/nsnam/ns/.

[32]

Shangguang Wang, Yan Guo, Ning Zhang, Peng Yang, Ao Zhou, and Xuemin Sherman Shen. 2019. Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach. IEEE Trans. Mobile Comput. Early Access (2019), 1--1.

[33]

Shangguang Wang, Yali Zhao, Lin Huang, Jinliang Xu, and Ching-Hsien Hsu. 2019. QoS prediction for service recommendations in mobile edge computing. J. Parallel Distrib. Comput. 127 (2019), 134--144.

Digital Library

[34]

Shangguang Wang, Yali Zhao, Jinlinag Xu, Jie Yuan, and Ching-Hsien Hsu. 2019. Edge server placement in mobile edge computing. J. Parallel Distrib. Comput. 127 (2019), 160--168.

Digital Library

[35]

Zhengzhe Xiang, Shuiguang Deng, Javid Taheri, and Albert Zomaya. 2020. Dynamical service deployment and replacement in resource-constrained edges. Mobile Netw. Appl. 25 (2020), 674--689.

[36]

Jinliang Xu, Shangguang Wang, Bharat K. Bhargava, and Fangchun Yang. 2019. A blockchain-enabled trustless crowd-intelligence ecosystem on mobile edge computing. IEEE Trans. Industr. Inf. 15 (2019), 3538--3547.

[37]

Louiza Yala, Pantelis A. Frangoudis, and Adlen Ksentini. 2018. Latency and availability driven VNF placement in a MEC-NFV environment. In Proceedings of the 2018 IEEE Global Communications Conference (GLOBECOM’18), 1--7.

[38]

Qiang Ye, Weihua Zhuang, Xu Li, and Jaya Rao. 2019. End-to-end delay modeling for embedded VNF chains in 5G core networks. IEEE IoT J. 6 (2019), 692--704.

[39]

Zilong Ye, Xiaojun Cao, Jianping Wang, Hong-Fang Yu, and Chunming Qiao. 2016. Joint topology design and mapping of service function chains for efficient, scalable, and reliable network functions virtualization. IEEE Netw. 30 (2016), 81--87.

Digital Library

[40]

Cheng Zhang, Hailiang Zhao, and Shuiguang Deng. 2018. A density-based offloading strategy for IoT devices in edge computing systems. IEEE Access 6 (2018), 73520--73530.

[41]

Zhilong Zheng, Jianzhao Bi, Heng Yu, Haiping Wang, Chen Sun, Hongxin Hu, and Jianping Wu. 2019. Octans: Optimal placement of service function chains in many-core systems. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM’19) (2019), 307--315.

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Liao ZDeng WHe STang Q(2024)Collaborative Filtering-based Fast Delay-aware algorithm for joint VNF deployment and migration in edge networksComputer Networks10.1016/j.comnet.2024.110300243(110300)Online publication date: Apr-2024
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Index Terms

An Efficient Service Function Chaining Placement Algorithm in Mobile Edge Computing
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing theory, concepts and paradigms
      1. Mobile computing
2. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Network resources allocation

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 4

November 2020

391 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3427795

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 January 2020

Received: 01 September 2019

Published in TOIT Volume 20, Issue 4

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BUPT Excellent Ph.D. Students Foundation
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Mahdizadeh SAbrishami S(2024)An assignment mechanism for workflow scheduling in Function as a Service edge environmentFuture Generation Computer Systems10.1016/j.future.2024.04.003157(543-557)Online publication date: Aug-2024
https://doi.org/10.1016/j.future.2024.04.003
Liao ZDeng WHe STang Q(2024)Collaborative Filtering-based Fast Delay-aware algorithm for joint VNF deployment and migration in edge networksComputer Networks10.1016/j.comnet.2024.110300243(110300)Online publication date: Apr-2024
https://doi.org/10.1016/j.comnet.2024.110300
Li WSun XWan BLiu HFang JWen Z(2023)A hybrid GA-PSO strategy for computing task offloading towards MES scenariosPeerJ Computer Science10.7717/peerj-cs.12739(e1273)Online publication date: 6-Apr-2023
https://doi.org/10.7717/peerj-cs.1273
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: Nov-2023
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Tian FZhang XLiang JYang Z(2023)Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networksIEEE Transactions on Mobile Computing10.1109/TMC.2023.328264523:5(3964-3980)Online publication date: 5-Jun-2023
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Bai JChang XMachida FJiang LHan ZTrivedi K(2023)Impact of Service Function Aging on the Dependability for MEC Service Function ChainIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.315078220:4(2811-2824)Online publication date: 1-Jul-2023
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