Service function chain embedding in centralized and  distributed data centers - A comparison

Ma Viet Duc; Nguyen Trung Kien; Dao Dai Hiep; Nguyen Tai Hung; Nguyen Huu Thanh

doi:10.15625/2525-2518/22956

Author affiliations

Authors

Ma Viet Duc \(^1\) School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet street, Ha Noi, Viet Nam https://orcid.org/0009-0004-3329-2529
Nguyen Trung Kien \(^2\) Chair of Communication Networks, University of Würzburg, Sanderring, No. 2, Würzburg, 97070, Bavaria, Germany
Dao Dai Hiep \(^1\) School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet street, Ha Noi, Viet Nam https://orcid.org/0009-0009-2114-236X
Nguyen Tai Hung \(^1\) School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet street, Ha Noi, Viet Nam https://orcid.org/0000-0002-6098-2136
Nguyen Huu Thanh \(^1\) School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet street, Ha Noi, Viet Nam https://orcid.org/0000-0002-7354-1524

DOI:

https://doi.org/10.15625/2525-2518/22956

Keywords:

distributed cloud, edge cloud computing, network function virtualization

Abstract

Cloud computing has played an important role in providing IoT-based services recently, such as healthcare, smart grid, driving-assistant systems and so forth. In such a paradigm, there is a tendency to deploy services in the edge-cloud environment, where data centers or computing clusters are partly moved to the edge of the network to avoid service degradation or disruption due to the scarcity of physical resources. This paper analyzes and discusses the advantages and disadvantages of providing virtualized services based on Network Function Virtualization in two edge-cloud scenarios, in which data centers are in the center or placed at the edge of the network. Furthermore, a novel Service Function Chain Embedding strategy has been proposed, which considers centralized or multiple distributed DCs scenarios, and focuses on DC-internal embedding in fat-tree fabrics under online arrivals and resource fragmentation. Performance evaluation results show that the proposed strategy can improve the efficiency of the cloud system in terms of resource utilization and power consumption.

Downloads

Download data is not yet available.

References

Abdul-Minaam, D. S., Al-Mutairi, W. M. E. S., Awad, M. A., & El-Ashmawi, W. H. (2020). An adaptive fitness-dependent optimizer for the one-dimensional bin packing problem. IEEE access : practical innovations, open solutions, 8, 97959-97974. https://doi.org/10.1109/access.2020.2985752

Adoga, H. U., & Pezaros, D. P. (2022). Network function virtualization and service function chaining frameworks: A comprehensive review of requirements, objectives, implementations, and open research challenges. Future Internet, 14(2), 59. https://doi.org/10.3390/fi14020059

Agarwal, S., Cai, Q., Agarwal, R., Shmoys, D., & Vahdat, A. (2024). Harmony: A congestion-free datacenter architecture [Conference paper]. 21st USENIX symposium on networked systems design and implementation, California, USA.

Al-Fares, M., Loukissas, A., & Vahdat, A. (2008). A scalable, commodity data center network architecture. ACM SIGCOMM Computer Communication Review, 38(4), 63-74. https://doi.org/10.1145/1402946.1402967

Azhdari, A., Ebrahimzadeh, A., Afrasiabi, S. N., Szabó, R., Mouradian, C., Li, W., & Glitho, R. H. (2023). Cost-aware topological decomposition of virtual network function forwarding graphs [Conference paper]. GLOBECOM 2023 - 2023 IEEE global communications conference, Kuala Lumpur, Malaysia.

Bolla, R., Bruschi, R., Davoli, F., & Cucchietti, F. (2010). Energy efficiency in the future internet: a survey of existing approaches and trends in energy-aware fixed network infrastructures. IEEE Communications Surveys & Tutorials, 13(2), 223-244. https://doi.org/10.1109/surv.2011.071410.00073

Bonomi, F. (2011). Connected vehicles, the internet of things, and fog computing [Conference paper]. The eighth ACM international workshop on vehicular inter-networking (VANET), Nevada, USA.

Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things [Conference paper]. Proceedings of the first edition of the MCC workshop on Mobile cloud computing, Helsinki, Finland.

Chintapalli, V. R., Partani, R., Tamma, B. R., & C, S. R. M. (2024). Energy efficient and delay aware deployment of parallelized service function chains in NFV-based networks. Computer Networks, 243, 110289. https://doi.org/10.1016/j.comnet.2024.110289

Cohen, R., Lewin-Eytan, L., Naor, J. S., & Raz, D. (2015). Near optimal placement of virtual network functions [Conference paper]. 2015 IEEE conference on computer communications (INFOCOM), Hong Kong, China.

Dolati, M., Hassanpour, S. B., Ghaderi, M., & Khonsari, A. (2019). DeepViNE: Virtual network embedding with deep reinforcement learning[Conference paper]. IEEE INFOCOM 2019 - IEEE conference on computer communications workshops (INFOCOM WKSHPS), Paris, France.

Erbati, M. M., Tajiki, M. M., & Schiele, G. (2023). Service function chaining to support ultra-low latency communication in NFV. Electronics,12(18), 3843. https://doi.org/10.3390/electronics12183843

Fischer, A., Botero, J. F., Beck, M. T., de Meer, H., & Hesselbach, X. (2013). Virtual network embedding: A survey. IEEE Communications Surveys & Tutorials, 15(4), 1888-1906. https://doi.org/10.1109/surv.2013.013013.00155

Gil Herrera, J., & Botero, J. F. (2016). Resource allocation in NFV: A comprehensive survey. IEEE Transactions on Network and Service Management, 13(3), 518-532. https://doi.org/10.1109/tnsm.2016.2598420

Hantouti, H., Benamar, N., & Taleb, T. (2020). Service function chaining in 5G & beyond networks: Challenges and open research issues. IEEE Network, 34(4), 320-327. https://doi.org/10.1109/mnet.001.1900554

Hartmanis, J. (1982). Computers and intractability: a guide to the theory of NP-completeness (michael R. Garey and david S. Johnson). SIAM Review, 24(1), 90-91. https://doi.org/10.1137/1024022

Heller, B., Seetharaman, S., Mahadevan, P., Yiakoumis, Y., Sharma, P., Banerjee, S., & McKeown, N. (2010). Elastictree: Saving energy in data center networks [Conference paper]. NSDI ’10: 7th USENIX symposium on networked systems design and implementation, California, USA.

Huong, T., Schlosser, D., Nam, P., Jarschel, M., Thanh, N., & Pries, R. (2011). ECODANE-Reducing energy consumption in data center networks based on traffic engineering [Conference paper]. 11th würzburg workshop on IP: Joint ITG and euro-NF workshop visions of future generation networks (EuroView2011), Würzburg, Germany.

Instance - Atlanta Network Problem. SNDlib-Library of test instances for Survivable fixed telecommunication Network Design.

Kaur, K., Garg, S., Aujla, G. S., Kumar, N., Rodrigues, J. J. P. C., & Guizani, M. (2018). Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE Communications Magazine, 56(2), 44-51. https://doi.org/10.1109/mcom.2018.1700622

Li, J., Shi, W., Ye, Q., Zhuang, W., Shen, X., & Li, X. (2018). Online joint VNF chain composition and embedding for 5G networks [Conference paper]. 2018 IEEE global communications conference (GLOBECOM), Abu Dhabi, United Arab Emirates.

Liang, W., Cui, L., & Tso, F. P. (2022). Low-latency service function chain migration in edge-core networks based on open Jackson networks. Journal of Systems Architecture, 124, 102405. https://doi.org/10.1016/j.sysarc.2022.102405

Lin, B., Huang, Y., Zhang, J., Hu, J., Chen, X., & Li, J. (2019). Cost-driven off-loading for DNN-based applications over cloud, edge, and end devices. IEEE Transactions on Industrial Informatics, 16(8), 5456-5466. https://doi.org/10.1109/tii.2019.2961237

Lin, R., He, L., Luo, S., & Zukerman, M. (2022). Energy-aware service function chaining embedding in NFV networks. IEEE Transactions on Services Computing, 16(2), 1158-1171. https://doi.org/10.1109/tsc.2022.3162328

Mahadevan, P., Sharma, P., Banerjee, S., & Ranganathan, P. (2009). Energy aware network operations [Conference paper]. INFOCOM workshops 2009, Rio de Janeiro, Brazil.

Nam, T. M., Thanh, N. H., Hieu, H. T., Manh, N. T., Huynh, N. V., & Tuan, H. D. (2017). Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization. Computer Networks, 125, 76-89. https://doi.org/10.1016/j.comnet.2017.06.007

Nguyen Huu, T., Pham Ngoc, N., Truong Thu, H., Tran Ngoc, T., Nguyen Minh, D., Nguyen, V. G., Nguyen Tai, H., Ngo Quynh, T., Hock, D., & Schwartz, C. (2013). Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks. Simulation Modelling Practice and Theory, 39, 20-40. https://doi.org/10.1016/j.simpat.2013.05.011

Niranjan Mysore, R., Pamboris, A., Farrington, N., Huang, N., Miri, P., Radhakrishnan, S., Subramanya, V., & Vahdat, A. (2009). PortLand: a scalable fault-tolerant layer 2 data center network fabric. [Conference paper]. Proceedings of the ACM SIGCOMM 2009 conference on data communication, Barcelona, Spain.

Pei, J., Hong, P., Xue, K., & Li, D. (2018). Efficiently embedding service function chains with dynamic virtual network function placement in geo-distributed cloud system. IEEE Transactions on Parallel and Distributed Systems, 30(10), 2179-2192. https://doi.org/10.1109/tpds.2018.2880992

Pham, T.-M. (2022). Optimizing service function chaining migration with explicit dynamic path. IEEE access : practical innovations, open solutions, 10, 16992-17002. https://doi.org/10.1109/access.2022.3150352

Poltronieri, F., Stefanelli, C., Suri, N., & Tortonesi, M. (2022). Value is king: The mecforge deep reinforcement learning solution for resource management in 5G and beyond. Journal of Network and Systems Management, 30(4), 63. https://doi.org/10.1007/s10922-022-09672-6

Raj, P. H., Ravi Kumar, P., Jelciana, P., & Rajagopalan, S. (2020). Modified first fit decreasing method for load balancing in mobile clouds[Conference paper]. 2020 4th international conference on intelligent computing and control systems (ICICCS), Madurai, India.

Ros, S., Ryoo, I., & Kim, S. (2025). DRL-driven intelligent SFC deployment in MEC workload for dynamic IoT networks. Sensors, 25(14), 4257. https://doi.org/10.3390/s25144257

Schrijver, A. (1998). Theory of linear and integer programming. John Wiley & Sons.

Sun, G., Chen, Z., Yu, H., Du, X., & Guizani, M. (2019). Online parallelized service function chain orchestration in data center networks. IEEE access : practical innovations, open solutions, 7, 100147-100161. https://doi.org/10.1109/access.2019.2930295

Sun, G., Li, Y., Yu, H., Vasilakos, A. V., Du, X., & Guizani, M. (2019). Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks. Future Generation Computer Systems, 91, 347-360. https://doi.org/10.1016/j.future.2018.09.037

Thanh, N. H., Cuong, B. D., Thien, T. D., Nam, P. N., Thu, N. Q., Huong, T. T., & Nam, T. M. (2013). ECODANE: A customizable hybrid testbed for green data center networks [Conference paper]. 2013 international conference on advanced technologies for communications (ATC 2013), Ho Chi Minh City, Vietnam.

Wang, L., Zhang, F., Aroca, J. A., Vasilakos, A. V., Zheng, K., Hou, C., Li, D., & Liu, Z. (2013). GreenDCN: A general framework for achieving energy efficiency in data center networks. IEEE Journal on Selected Areas in Communications, 32(1), 4-15. https://doi.org/10.1109/jsac.2014.140102

Wang, R., Yu, X., Wu, Q., Yi, C., Wang, P., & Niyato, D. (2024). Efficient deployment of partial parallelized service function chains in CPU+DPU-based heterogeneous NFV platforms. IEEE Transactions on Mobile Computing, 23(10), 9090-9107. https://doi.org/10.1109/tmc.2024.3357796

Wang, X., Wang, X., Shi, Y., Wu, D., Ma, L., & Huang, M. (2023). Core-selecting auction-based mechanisms for service function chain provisioning and pricing in NFV markets. Computer Networks, 222, 109557. https://doi.org/10.1016/j.comnet.2023.109557

Waxman, B. M. (2002). Routing of multipoint connections. IEEE Journal on Selected Areas in Communications, 6(9), 1617-1622. https://doi.org/10.1109/49.12889

Xiao, Y., Zhang, Q., Liu, F., Wang, J., Zhao, M., Zhang, Z., & Zhang, J. (2019). NFVdeep: adaptive online service function chain deployment with deep reinforcement learning [Conference paper]. Proceedings of the International Symposium on Quality of Service, Arizona, USA.

Zhang, Y., Zhang, F., Tong, S., & Rezaeipanah, A. (2022). A dynamic planning model for deploying service functions chain in fog-cloud computing. Journal of King Saud University - Computer and Information Sciences, 34(10), 7948-7960. https://doi.org/10.1016/j.jksuci.2022.07.012

Zhao, D., Liao, D., Sun, G., & Xu, S. (2018). Towards resource-efficient service function chain deployment in cloud-fog computing. IEEE access : practical innovations, open solutions, 6, 66754-66766. https://doi.org/10.1109/access.2018.2875124

Zhou, H., Tan, L., Zeng, Q., & Wu, C. (2016). Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration. Journal of Network and Computer Applications, 60, 220-232. https://doi.org/10.1016/j.jnca.2015.11.013

Zhu, Z., Lu, H., Li, J., & Jiang, X. (2017). Service function chain mapping with resource fragmentation avoidance [Conference paper]. GLOBECOM 2017 - 2017 IEEE global communications conference, Singapore.