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Resource Management for Cost-Effective Cloud Services

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Enabling 6G Mobile Networks

Abstract

In line with the virtual network function (VNF ) paradigm, network functions are abstracted and relocated from dedicated appliances to generic servers, thus providing the enabler for savings in terms of total cost of ownership (TCO). In fact, hardware overprovisioning induced costs can be saved due to the on-demand capability of scaling up and down the server capacity via a software setting. Moreover, when the user migrates between networking ecosystems, cloud management services needs to accommodate the migration of virtual resources between networks. Therefore, we address how resources are pooled, forecasted and migrated between abstract servers to have computing resources on-demand. This is demonstrated within a fog-enabled C-V2X architecture and UDN deployment. Furthermore, the pooling of computational resources for implementing RAN functions according to cell load requirements in 5G RAN can provide cost-effective centralized detection at the MEC (mobile edge computing) node. The optimization framework for jointly optimizing and managing MEC resources for baseband processing is considered.

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Notes

  1. 1.

    An open-source, highly portable, microscopic and continuous multimodal traffic simulator, available at: https://www.eclipse.org/sumo/

  2. 2.

    Exact locations of base stations are hidden in the geographical map, not to disclose any information about the mobile operator’s deployment of the network.

  3. 3.

    OpenAirInterface is an open-source software that provides a full implementation of LTE network, both in the core network and radio access network. https://www.openairinterface.org/

  4. 4.

    http://xiang.faculty.polimi.it/files/TechnicalReport.pdf

References

  1. ETSI White Paper No. 28, “MEC in 5G networks”, 2018.

    Google Scholar 

  2. ETSI, “The standard news from ETSI”, Issue 2, 2017, https://www.etsi.org/images/files/ETSInewsletter/etsinewsletter-issue2-2017.pdf

  3. Sarrigiannis, I., Ramantas, K., Kartsakli, E., Mekikis, P.-V., Antonopoulos, A., & Verikoukis, C. (2020). Online VNF lifecycle management in a MEC-enabled 5G IoT architecture. IEEE Internet of Things Journal, 7(5), 4183–4194.

    Article  Google Scholar 

  4. Kreutz, D., Ramos, F. M. V., Veríssimo, P. E., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2015). Software-defined networking: A comprehensive survey. Proceedings of the IEEE, 103(1), 14–76.

    Article  Google Scholar 

  5. ETSI NFV. (2014). Network functions virtualisation (NFV); management and orchestration. GS NFV-MAN, 001.

    Google Scholar 

  6. Gonzalez, A. J., Nencioni, G., Kamisiński, A., Helvik, B. E., & Heegaard, P. E. (2018). Dependability of the NFV orchestrator: State of the art and research challenges. IEEE Communications Surveys & Tutorials, 99.

    Google Scholar 

  7. “Release 14 Description”, 3GPP TR 21.914, Tech. Rep., 2018.

    Google Scholar 

  8. Consortium Architecture Working Group, “OpenFog reference architecture for fog computing”, Feb. 2017.

    Google Scholar 

  9. Sarrigiannis, I., Contreras, L-M., Ramantas, K., Antonopoulos, A., & Verikoukis, C. Fog-enabled Scalable C-V2X Architecture for Distributed 5G and Beyond Applications. IEEE Network, pending publication.

    Google Scholar 

  10. The Openstack foundation, https://openstack.org/. Accessed 10 Aug 2020.

  11. Open Source Mano, https://osm.etsi.org/. Accessed 10 Aug 2020.

  12. Cisco, Cisco Annual Internet Report (2018–2023) White Paper. Online: shorturl.at/cNQTZ. Last read 12/08/2020.

    Google Scholar 

  13. System Architecture for the 5G System (5GS). 3rd Generation Partnership Project (3GPP). TS 23.501. July 2020.

    Google Scholar 

  14. Procedures for the 5G System (5GS). 3rd Generation Partnership Project (3GPP). TS 23.502. July 2020.

    Google Scholar 

  15. IETF draft: https://tools.ietf.org/html/draft-fattore-dmm-n6-cpdp-trafficsteering-01. 2019. Internet Engineering Task Force (IETF) Distributed Mobility Management (DMM) WG.

  16. Sakshi, C., & Sivalingam, K. M. (2015). SDN based evolved packet core architecture for efficient user mobility support. In Proceedings of the 2015 1st IEEE conference on network softwarization (NetSoft). IEEE.

    Google Scholar 

  17. Xin, J. et al. (2013). Softcell: Scalable and flexible cellular core network architecture. In Proceedings of the ninth ACM conference on Emerging networking experiments and technologies.

    Google Scholar 

  18. Fattore, U., Giust, F., & Liebsch, M. (2018). 5GC+: An experimental proof of a programmable mobile core for 5G. IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

    Google Scholar 

  19. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modelling. arXiv preprint arXiv:1412.3555 (2014).

    Google Scholar 

  20. De Vita, F., Nardini, G., Virdis, A., Bruneo, D., Puliafito, A., & Stea, G. (2019). Using deep reinforcement learning for application relocation in multi-access edge computing. IEEE Communications Standards Magazine, 3(3), 71–78.

    Article  Google Scholar 

  21. ETSI. (2019). Multi-access Edge Computing (MEC); Framework and Reference Architecture. TS. European Telecommunication Standards Institute (ETSI).

    Google Scholar 

  22. Fattore, U., Liebsch, M., & Bernardos, C. J. (2018). UPFlight: An enabler for avionic MEC in a drone-extended 5G mobile network. IEEE 23rd Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).

    Google Scholar 

  23. Fattore, U., Liebsch, M., Brik, B., & Ksentini, A. (2020). AutoMEC: LSTM-based user mobility prediction for service management in distributed MEC resources. Proceedings of the 22nd international ACM conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM).

    Google Scholar 

  24. Gambs, S., Killijian, M-O., & del Prado Cortez, M. N. (2012). Next place prediction using mobility markov chains. In Proceedings of the first workshop on measurement, privacy, and mobility, pp. 1–6.

    Google Scholar 

  25. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

    Article  Google Scholar 

  26. Taleb, T., & Ksentini, A. (2013). Follow me cloud: interworking federated clouds and distributed mobile networks. IEEE Network, 27(5), 12–19.

    Article  Google Scholar 

  27. Liu, Q., Wu, S., Liang, W., & Tan, T. (2016). Predicting the next location: A recurrent model with spatial and temporal contexts. In Thirtieth AAAI conference on artificial intelligence.

    Google Scholar 

  28. Ntalampiras, S., & Fiore, M. (2018). Forecasting mobile service demands for anticipatory MEC. In 2018 IEEE 19th international symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM) (pp. 14–19). IEEE.

    Chapter  Google Scholar 

  29. Tkačík, J., & Kordík, P. (2016). Neural turing machine for sequential learning of human mobility patterns. In 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 2790–2797). IEEE.

    Chapter  Google Scholar 

  30. Andrew, M. et al. (2016). Migrating running applications across mobile edge clouds: Poster. In Proceedings of the 22nd annual international conference on mobile computing and networking.

    Google Scholar 

  31. Ventre, P. L., Lungaroni, P., Siracusano, G., Pisa, C., Schmidt, F., Lombardo, F., & Salsano, S. (2018). On the fly orchestration of unikernels: Tuning and performance evaluation of virtual infrastructure managers. IEEE Transactions on Cloud Computing, 2018.

    Google Scholar 

  32. Wang, C., Zhao, Z., Sun, Q., & Zhang, H. (2018). Deep learning based intelligent dual connectivity for mobility management in dense network. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) (pp. 1–5). IEEE.

    Google Scholar 

  33. Wickramasuriya, D. S., Perumalla, C. A., Davaslioglu, K., & Gitlin, R. D. Base station prediction and proactive mobility management in virtual cells using recurrent neural networks. In 2017 IEEE 18th Wireless and Microwave Technology Conference (WAMICON) (pp. 1–6). IEEE.

    Google Scholar 

  34. Chen, K., & Duan, R. (2011). C-RAN the road towards green RAN. China Mobile Research Institute, white paper 2.

    Google Scholar 

  35. Checko, A., Christiansen, H. L., Yan, Y., Scolari, L., Kardaras, G., Berger, M. S., & Dittmann, L. (2014). Cloud RAN for mobile networks—A technology overview. IEEE Communications surveys & tutorials, 17(1), 405–426.

    Article  Google Scholar 

  36. Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655.

    Article  Google Scholar 

  37. Sexton, C., Kaminski, N. J., Marquez-Barja, J. M., Marchetti, N., & DaSilva, L. A. (2017). 5G: Adaptable networks enabled by versatile radio access technologies. IEEE Communications Surveys & Tutorials, 19(2), 688–720.

    Article  Google Scholar 

  38. Okic, A., & Redondi, A. E. C. (2020) Optimal resource allocation in C-RAN through DSP computational load forecasting. In 2019 IEEE 30th annual international symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

    Google Scholar 

  39. Rost, P., Talarico, S., & Valenti, M. C. (2015). The complexity–rate tradeoff of centralized radio access networks. IEEE Transactions on Wireless Communications, 14(11), 6164–6176.

    Article  Google Scholar 

  40. Okic, A., Redondi, A. E., Galimberti, I., Foglia, F., & Venturini, L. (2019). Analyzing different mobile applications in time and space: A city-wide scenario. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1–6). IEEE.

    Google Scholar 

  41. Okic, A., & Redondi, A. E. (2019). Forecasting Mobile cellular traffic sampled at different frequencies. In 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (pp. 189–195). IEEE.

    Chapter  Google Scholar 

  42. Furno, A., Fiore, M., Stanica, R., Ziemlicki, C., & Smoreda, Z. (2016). A tale of ten cities: Characterizing signatures of mobile traffic in urban areas. IEEE Transactions on Mobile Computing, 16(10), 2682–2696.

    Article  Google Scholar 

  43. Checko, A., Christiansen, H. L., & Berger, M. S. (2013). Evaluation of energy and cost savings in mobile Cloud RAN. In OPNETWORK 2013. OPNET.

    Google Scholar 

  44. Alba, A. M., Velásquez, J. H. G., & Kellerer, W. (2019). An adaptive functional split in 5G networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 410–416). IEEE.

    Chapter  Google Scholar 

  45. Bhaumik, S., Chandrabose, S. P., Jataprolu, M. K., Kumar, G., Muralidhar, A., Polakos, P., ... & Woo, T. (2012). CloudIQ: A framework for processing base stations in a data center. In Proceedings of the 18th annual international conference on Mobile computing and networking (pp. 125–136).

    Google Scholar 

  46. Chen, L., et al. (2017). Complementary base station clustering for cost-effective and energy-efficient cloud-RAN. In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE.

    Google Scholar 

  47. Sabella, D., Rost, P., Sheng, Y., Pateromichelakis, E., Salim, U., Guitton-Ouhamou, P., Di Girolamo, M., & Giuliani, G. (2013). RAN as a service: Challenges of designing a flexible RAN architecture in a cloud-based heterogeneous mobile network. In 2013 future network & mobile summit (pp. 1–8). IEEE.

    Google Scholar 

  48. Sabella, D., De Domenico, A., Katranaras, E., Imran, M. A., Di Girolamo, M., Salim, U., Lalam, M., Samdanis, K., & Maeder, A. (2014). Energy efficiency benefits of RAN-as-a-service concept for a cloud-based 5G mobile network infrastructure. IEEE Access, 2, 1586–1597.

    Article  Google Scholar 

  49. Santos, J. F., Kist, M., Rochol, J., & Da Silva, L. A. (2020). Virtual radios, real services: Enabling RANaaS through radio virtualisation. IEEE Transactions on Network and Service Management.

    Google Scholar 

  50. Wubben, D., Rost, P., Bartelt, J. S., Lalam, M., Savin, V., Gorgoglione, M., … Fettweis, G. (2014). Benefits and impact of cloud computing on 5G signal processing: Flexible centralization through cloud-RAN. IEEE Signal Processing Magazine, 31(6), 35–44.

    Article  Google Scholar 

  51. Larsen, L. M., Checko, A., & Christiansen, H. L. (2018). A survey of the functional splits proposed for 5G mobile crosshaul networks. IEEE Communications Surveys & Tutorials, 21(1), 146–172.

    Article  Google Scholar 

  52. Nikaein, N. (2015). Processing radio access network functions in the cloud: Critical issues and modeling. In Proceedings of the 6th international workshop on Mobile cloud computing and services (pp. 36–43).

    Google Scholar 

  53. Jindal, A., Podolskiy, V., & Gerndt, M. (2017). Multilayered cloud applications autoscaling performance estimation. In 2017 IEEE 7th international symposium on cloud and service computing (SC2) (pp. 24–31). IEEE.

    Chapter  Google Scholar 

  54. Xiang, W., Zheng, K., & Shen, X. S. (2017). 5G mobile communications. Springer.

    Book  Google Scholar 

  55. Lyu, X., Tian, H., Ni, W., Zhang, Y., Zhang, P., & Liu, R. P. (2018). Energy-efficient admission of delay-sensitive tasks for Mobile edge computing. IEEE Transactions on Communications, 66(6), 2603–2616.

    Article  Google Scholar 

  56. Cheng, K., Teng, Y., Sun, W., Liu, A., & Wang, X. (2018). Energy-efficient joint offloading and wireless resource allocation strategy in multi-mec server systems. IEEE ICC.

    Book  Google Scholar 

  57. Xiang, B., Elias, J., Martignon, F., & Di Nitto, E. (2019). Joint network slicing and mobile edge computing in 5G networks. IEEE ICC.

    Book  Google Scholar 

  58. Kannan, R., & Monma, C. L. (1978). On the computational complexity of integer programming problems. In Optimization and operations research (pp. 161–172). Springer.

    Chapter  Google Scholar 

  59. Tong, L., Li, Y., & Gao, W. (2016). A hierarchical edge cloud architecture for mobile computing. IEEE INFOCOM.

    Book  Google Scholar 

  60. Xiang, B., Elias, J., Martignon, F., & Di Nitto, E. (2020). Resource calendaring for mobile edge computing in 5G networks. Submitted to IEEE ICC.

    Google Scholar 

  61. Wang, P., Zheng, Z., Di, B., & Song, L. (2019). Joint task assignment and resource allocation in the heterogeneous multi-layer mobile edge computing networks. IEEE Globecom.

    Book  Google Scholar 

  62. Meng, J., Tan, H., Li, X.-Y., Han, Z., & Li, B. (2020). Online deadline-aware task dispatching and scheduling in edge computing. IEEE Transactions on Parallel and Distributed Systems, 31(6), 1270–1286.

    Article  Google Scholar 

  63. Hong, C.-Y., Kandula, S., Mahajan, R., Zhang, M., Gill, V., Nanduri, M., & Wattenhofer, R. (2013). Achieving high utilization with software-driven wan. ACM SIGCOMM Computer Communication Review, 43(4), 15–26.

    Article  Google Scholar 

  64. Kwak, J., Kim, Y., Lee, J., & Chong, S. (2015). DREAM: Dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE Journal on Selected Areas in Communications, 33(12), 2510–2523.

    Article  Google Scholar 

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Acknowledgements

This research work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 722788 (SPOTLIGHT).

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Authors and Affiliations

  1. Politecnico di Milano, Milano, Italy

    Armin Okic, Bin Xiang, Alessandro E. C. Redondi & Elisabetta Di Nitto

  2. Iquadrat, Barcelona, Spain

    Ioannis Sarrigiannis & Kostas Ramantas

  3. NEC Laboratories Europe GmbH, Heidelberg, Germany

    Umberto Fattore & Marco Liebsch

  4. Nearby Computing, Barcelona, Spain

    Angelos Antonopoulos

  5. Centre Tecnològic de Telecomunicacions de Catalunyal, Parc Mediterrani de la Tecnologia (PMT), Castelldefels, Spain

    Christos Verikoukis

  6. Telefónica I+D/Global CTIO Unit, Madrid, Spain

    Luis M. Contreras

Authors
  1. Armin Okic
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  2. Ioannis Sarrigiannis
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  3. Umberto Fattore
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  4. Bin Xiang
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  5. Alessandro E. C. Redondi
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  6. Elisabetta Di Nitto
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  7. Angelos Antonopoulos
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  8. Kostas Ramantas
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  9. Christos Verikoukis
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  11. Marco Liebsch
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Correspondence to Armin Okic .

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Editors and Affiliations

  1. Instituto de Telecommunicações, Campus Universitário Santiago, Aveiro, Portugal

    Jonathan Rodriguez

  2. Centre Tecnològic de Telecomunicacions de Catalunyal, Parc Mediterrani de la Tecnologia (PMT), Castelldefels, Spain

    Christos Verikoukis

  3. Iquadrat Informática SL, Barcelona, Spain

    John S. Vardakas

  4. University of Athens, Panepistimiopolis, Athens, Greece

    Nikos Passas

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Okic, A. et al. (2022). Resource Management for Cost-Effective Cloud Services. In: Rodriguez, J., Verikoukis, C., Vardakas, J.S., Passas, N. (eds) Enabling 6G Mobile Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-74648-3_12

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  • DOI: https://doi.org/10.1007/978-3-030-74648-3_12

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