Service function chain embedding in centralized and distributed data centers - A comparison
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DOI:
https://doi.org/10.15625/2525-2518/22956Keywords:
distributed cloud, edge cloud computing, network function virtualizationAbstract
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.
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1. 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. - Modeling and experimenting combined smart sleep and power scaling algorithms in energy-aware data center networks. Simul. Model. Pract. Theory, 39 (2013) 20-40. https://doi.org/10.1016/j.simpat.2013.05.011.
2. 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) (2010) 223-244. https://doi.org/10.1109/surv.2011.071410.00073.
3. Bonomi F. - The eighth ACM international workshop on vehicular inter-networking (VANET), (2011) 13-15.
4. Bonomi F., Milito R., Zhu J., Addepalli S. - Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, (2012) 13-16. https://doi.org/10.1145/2342509.2342513.
5. Gil Herrera J., Botero J. F. - Resource allocation in NFV: A comprehensive survey. IEEE Trans. Netw. Serv. Manage., 13(3) (2016) 518-532. https://doi.org/10.1109/tnsm.2016.2598420.
6. Cohen R., Lewin-Eytan L., Naor J. S., Raz D. - 2015 IEEE conference on computer communications (INFOCOM), IEEE, (2015) 1346-1354. https://doi.org/10.1109/infocom.2015.7218511.
7. Zhao D., Liao D., Sun G., Xu S. - Towards resource-efficient service function chain deployment in cloud-fog computing. IEEE Access, 6 (2018) 66754-66766. https://doi.org/10.1109/access.2018.2875124.
8. Li J., Shi W., Ye Q., Zhuang W., Shen X., Li X. - 2018 IEEE global communications conference (GLOBECOM), IEEE, (2018) 1-6. https://doi.org/10.1109/glocom.2018.8647700.
9. Wang R., Yu X., Wu Q., Yi C., Wang P., Niyato D. - Efficient deployment of partial parallelized service function chains in CPU+DPU-based heterogeneous NFV platforms. IEEE Trans. Mob. Comput., 23(10) (2024) 9090-9107. https://doi.org/10.1109/tmc.2024.3357796.
10. Pham T.-M. - Optimizing service function chaining migration with explicit dynamic path. IEEE Access, 10 (2022) 16992-17002. https://doi.org/10.1109/access.2022.3150352.
11. Erbati M. M., Tajiki M. M., Schiele G. - Service function chaining to support ultra-low latency communication in NFV. Electronics, 12(18) (2023) 3843. https://doi.org/10.3390/electronics12183843.
12. Wang X., Wang X., Shi Y., Wu D., Ma L., Huang M. - Core-selecting auction-based mechanisms for service function chain provisioning and pricing in NFV markets. Comput. Netw., 222 (2023) 109557. https://doi.org/10.1016/j.comnet.2023.109557.
13. Lin R., He L., Luo S., Zukerman M. - Energy-aware service function chaining embedding in NFV networks. IEEE Trans. Serv. Comput., 16(2) (2022) 1158-1171. https://doi.org/10.1109/tsc.2022.3162328.
14. Chintapalli V. R., Partani R., Tamma B. R., C S. R. M. - Energy efficient and delay aware deployment of parallelized service function chains in NFV-based networks. Comput. Netw., 243 (2024) 110289. https://doi.org/10.1016/j.comnet.2024.110289.
15. Sun G., Chen Z., Yu H., Du X., Guizani M. - Online parallelized service function chain orchestration in data center networks. IEEE Access, 7 (2019) 100147-100161. https://doi.org/10.1109/access.2019.2930295.
16. Azhdari A., Ebrahimzadeh A., Afrasiabi S. N., Szabó R., Mouradian C., Li W., Glitho R. H. - GLOBECOM 2023 - 2023 IEEE global communications conference, IEEE, (2023) 5384-5390. https://doi.org/10.1109/globecom54140.2023.10437838.
17. Xiao Y., Zhang Q., Liu F., Wang J., Zhao M., Zhang Z., Zhang J. - Proceedings of the International Symposium on Quality of Service, ACM, (2019) 1-10. https://doi.org/10.1145/3326285.3329056.
18. Dolati M., Hassanpour S. B., Ghaderi M., Khonsari A. - IEEE INFOCOM 2019 - IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, (2019) 879-885. https://doi.org/10.1109/infcomw.2019.8845171.
19. Hantouti H., Benamar N., Taleb T. - Service function chaining in 5G & beyond networks: Challenges and open research issues. IEEE Netw., 34(4) (2020) 320-327. https://doi.org/10.1109/mnet.001.1900554.
20. Adoga H. U., Pezaros D. P. - Network function virtualization and service function chaining frameworks: A comprehensive review of requirements, objectives, implementations, and open research challenges. Future Internet, 14(2) (2022) 59. https://doi.org/10.3390/fi14020059.
21. Sun G., Li Y., Yu H., Vasilakos A. V., Du X., Guizani M. - Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks. Future Gener. Comput. Syst., 91 (2019) 347-360. https://doi.org/10.1016/j.future.2018.09.037.
22. Pei J., Hong P., Xue K., Li D. - Efficiently embedding service function chains with dynamic virtual network function placement in geo-distributed cloud system. IEEE Trans. Parallel Distrib. Syst., 30(10) (2018) 2179-2192. https://doi.org/10.1109/tpds.2018.2880992.
23. Kaur K., Garg S., Aujla G. S., Kumar N., Rodrigues J. J. P. C., Guizani M. - Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE Commun. Mag., 56(2) (2018) 44-51. https://doi.org/10.1109/mcom.2018.1700622.
24. Wang L., Zhang F., Aroca J. A., Vasilakos A. V., Zheng K., Hou C., Li D., Liu Z. - GreenDCN: A general framework for achieving energy efficiency in data center networks. IEEE J. Sel. Areas Commun., 32(1) (2013) 4-15. https://doi.org/10.1109/jsac.2014.140102.
25. Lin B., Huang Y., Zhang J., Hu J., Chen X., Li J. - Cost-driven off-loading for DNN-based applications over cloud, edge, and end devices. IEEE Trans. Ind. Inform., 16(8) (2019) 5456-5466. https://doi.org/10.1109/tii.2019.2961237.
26. Zhang Y., Zhang F., Tong S., Rezaeipanah A. - A dynamic planning model for deploying service functions chain in fog-cloud computing. J. King Saud Univ. Comput. Inf. Sci., 34(10) (2022) 7948-7960. https://doi.org/10.1016/j.jksuci.2022.07.012.
27. Liang W., Cui L., Tso F. P. - Low-latency service function chain migration in edge-core networks based on open Jackson networks. J. Syst. Archit., 124 (2022) 102405. https://doi.org/10.1016/j.sysarc.2022.102405.
28. Ros S., Ryoo I., Kim S. - DRL-driven intelligent SFC deployment in MEC workload for dynamic IoT networks. Sensors, 25(14) (2025) 4257. https://doi.org/10.3390/s25144257.
29. Poltronieri F., Stefanelli C., Suri N., Tortonesi M. - Value is king: The mecforge deep reinforcement learning solution for resource management in 5G and beyond. J. Netw. Syst. Manage., 30(4) (2022) 63. https://doi.org/10.1007/s10922-022-09672-6.
30. Nam T. M., Thanh N. H., Hieu H. T., Manh N. T., Huynh N. V., Tuan H. D. - Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization. Comput. Netw., 125 (2017) 76-89. https://doi.org/10.1016/j.comnet.2017.06.007.
31. Al-Fares M., Loukissas A., Vahdat A. - A scalable, commodity data center network architecture. Comput. Commun. Rev., 38(4) (2008) 63-74. https://doi.org/10.1145/1402946.1402967.
32. Niranjan Mysore R., Pamboris A., Farrington N., Huang N., Miri P., Radhakrishnan S., Subramanya V., Vahdat A. - Proceedings of the ACM SIGCOMM 2009 conference on data communication, ACM, (2009) 39-50. https://doi.org/10.1145/1592568.1592575.
33. Huong T., Schlosser D., Nam P., Jarschel M., Thanh N., Pries R. - 11th würzburg workshop on IP: Joint ITG and euro-NF workshop visions of future generation networks (EuroView2011), (2011)
34. Thanh N. H., Cuong B. D., Thien T. D., Nam P. N., Thu N. Q., Huong T. T., Nam T. M. - 2013 international conference on advanced technologies for communications (ATC 2013), IEEE, (2013) 312-317. https://doi.org/10.1109/atc.2013.6698128.
35. Mahadevan P., Sharma P., Banerjee S., Ranganathan P. - INFOCOM workshops 2009, IEEE, (2009) 1-6. https://doi.org/10.1109/infcomw.2009.5072138.
36. Abdul-Minaam D. S., Al-Mutairi W. M. E. S., Awad M. A., El-Ashmawi W. H. - An adaptive fitness-dependent optimizer for the one-dimensional bin packing problem. IEEE Access, 8 (2020) 97959-97974. https://doi.org/10.1109/access.2020.2985752.
37. Raj P. H., Ravi Kumar P., Jelciana P., Rajagopalan S. - 2020 4th international conference on intelligent computing and control systems (ICICCS), IEEE, (2020) 1107-1110. https://doi.org/10.1109/iciccs48265.2020.9120929.
38. Hartmanis J. - Computers and intractability: a guide to the theory of NP-completeness (michael R. Garey and david S. Johnson). SIAM Rev., 24(1) (1982) 90-91. https://doi.org/10.1137/1024022.
39. Heller B., Seetharaman S., Mahadevan P., Yiakoumis Y., Sharma P., Banerjee S., McKeown N. - NSDI ’10: 7th USENIX symposium on networked systems design and implementation, (2010) 249-264.
40. Instance - Atlanta Network Problem - SNDlib-Library of test instances for Survivable fixed telecommunication Network Design.
41. Zhou H., Tan L., Zeng Q., Wu C. - Traffic matrix estimation: A neural network approach with extended input and expectation maximization iteration. J. Netw. Comput. Appl., 60 (2016) 220-232. https://doi.org/10.1016/j.jnca.2015.11.013.
42. Waxman B. M. - Routing of multipoint connections. IEEE J. Sel. Areas Commun., 6(9) (2002) 1617-1622. https://doi.org/10.1109/49.12889.
43. Fischer A., Botero J. F., Beck M. T., de Meer H., Hesselbach X. - Virtual network embedding: A survey. IEEE Commun. Surv. Tutor., 15(4) (2013) 1888-1906. https://doi.org/10.1109/surv.2013.013013.00155.
44. Zhu Z., Lu H., Li J., Jiang X. - GLOBECOM 2017 - 2017 IEEE global communications conference, IEEE, (2017) 1-6. https://doi.org/10.1109/glocom.2017.8254441.
45. Agarwal S., Cai Q., Agarwal R., Shmoys D., Vahdat A. - 21st USENIX symposium on networked systems design and implementation, (2024) 329-343.
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Trường Đại học Bách Khoa Hà Nội
Grant numbers T2023-PC-038

