Abstract
The cellular network architecture is evolving to support a wide variety of applications with different traffic characteristics expected for 5G and beyond. Providing shared computing and network resources, Cloud based Radio Access Network (Cloud-RAN) in conjunction with Mobile Edge Computing (MEC) are considered key enablers to building 5G networks in a cost-efficient way. Understanding the limits and constraints of deploying the Cloud-RAN on MEC servers, allows the system to be engineered meeting latency and capacity requirements. By conducting a literature review, this paper discusses sharing computing and networking resources in MEC servers, which run software implementation of the Base Band Unit (vBBU) along with collocated applications.
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References
Bhushan, N., et al.: Network densification: the dominant theme for wireless evolution into 5g. IEEE Commun. Mag. 52(2), 82–89 (2014)
Ge, X., Song, T., Mao, G., Wang, C.-X., Han, T.: 5g ultra-dense cellular networks. IEEE Wirel. Commun. 23(1), 72–79 (2016)
Checko, A., et al.: Cloud ran for mobile networks-a technology overview. IEEE Commun. Surv. Tutor. 17(1), 405–426 (2015)
Hu, Y.C., Patel, M., Sabella, D., Sprecher, N., Young, N.: Mobile edge computing-a key technology towards 5g. ETSI White Paper 11(11), 1–16 (2015)
CPRI. Common public radio interface: ECPRI interface specification. CPRI Specification V7.0 (2015)
CPRI: Common public radio interface: ECPRi interface specification. eCPRI Specification V2.0 (2019)
OBSAI: Open base station architecture initiative. BTS System Reference Document version, 2 (2006)
Checko, A., et al.: Cloud ran for mobile networks-a technology overview. IEEE Commun Surv. Tutor. 17(1), 405–426 (2015)
3GPP TR 38.801. Study on new radio access technology: Radio access architecture and interfaces (2017)
IEEE. IEEE STD 1914.1-2019: Standard for packet-based fronthaul transport network. IEEE Standards (2019)
Larsen, L.M.P., Checko, A., Christiansen, H.K.: A survey of the functional splits proposed for 5g mobile crosshaul networks. IEEE Commun. Surv. Tutor. 21(1), 146–172 (2019)
RCRWireless. Exploring functional splits in 5g ran: Tradeoffs and use cases. Accessed Dec 2021
Assimakopulos, P., Birring, G.S., Kenan Al-Hares, M., Gomes, N.J. Ethernet-based fronthauling for cloud-radio access networks. In: 2017 19th International Conference on Transparent Optical Networks (ICTON), pp. 1–4 (2017)
IEEE. IEEE standard for packet-based fronthaul transport networks. IEEE Std. 1914.1-2019, pp. 1–94 (2020)
IEEE. IEEE standard for radio over ethernet encapsulations and mappings. IEEE Std. 1914.3-2018, pp. 1–77 (2018)
Gomes, N.J., Chanclou, P., Turnbull, P., Magee, A., Jungnickel. V.: Fronthaul evolution: from CPRI to ethernet. Opt. Fiber Technol. 26, 50–58 (2015)
Finn, N.: Introduction to time-sensitive networking. IEEE Commun. Stand. Mag. 2(2), 22–28 (2018)
IEEE-P802.1CM. IEEE 802.1qbu-2016 - IEEE standard for local and metropolitan area networks - bridges and bridged networks - amendment 26: Frame preemption. IEEE Std. 802.1Q-2014 (2016)
IEEE-P802.1CM. IEEE 802.1qbv-2015 - IEEE standard for local and metropolitan area networks - bridges and bridged networks - amendment 25: Enhancements for scheduled traffic. IEEE Std. 802.1Q-2014 (2015)
Bhattacharjee, S., Schmidt, R., Katsalis, K., Chang, C.-Y., Bauschert, T., Nikaein, N.: Time-sensitive networking for 5g fronthaul networks. In: ICC 2020–2020 IEEE International Conference on Communications (ICC), pp. 1–7 (2020)
Bhattacharjee, S., et al.: Network slicing for TSN-based transport networks. IEEE Access 9, 62788–62809 (2021)
Tomaszewski, L., Kukliński, S., Kołakowski, R.: A new approach to 5G and MEC integration. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) AIAI 2020. IAICT, vol. 585, pp. 15–24. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49190-1_2
Reghenzani, F., Massari, G., Fornaciari, W.: The real-time linux kernel: A survey on preempt_rt. ACM Comput. Surv. 52(1), 36 (2019)
Mosnier, A.: Embedded/real-time linux survey (2005)
Timmerman, M.: Real-time capabilities in the standard linux kernel: How to enable and use them? Int. J. Recent Innov. Trends Comput. Commun. 3(1), 131–135 (2015)
Yodaiken, V., et al.: The rtlinux manifesto. In: Proceedings of the 5th Linux Expo (1999)
Molnar, I.: Linux low latency patch. Accessed Dec 2021
The Linux Foundation. Preempt_rt patch. https://wiki.linuxfoundation.org/realtime/preempt_rt_versions
Nikaein, N., et al.: Openairinterface: an open LTE network in a PC. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, pp. 305–308 (2014)
Giacobbi, G.: The GNU Netcat project. Accessed Nov 2021
Kaltenberger, F., Wagner, S.: Experimental analysis of network-aided interference-aware receiver for LTE MU-MIMO. In: 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 325–328, June 2014
Alyafawi, I., Schiller, E., Braun, T., Dimitrova, D., Gomes, A., Nikaein, N.: Critical issues of centralized and cloudified LTE-FDD radio access networks. In: 2015 IEEE International Conference on Communications (ICC), pp. 5523–5528. IEEE (2015)
Bhaumik, S., et al.: 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. ACM (2012)
Fajjari, I., Aitsaadi, N., Amanou, S.: Optimized resource allocation and RRH attachment in experimental SDN based cloud-ran. In: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–6. IEEE (2019)
Molnar, I.: Linux low latency patch. Accessed Dec 2021
Huang, S.-C., Luo, Y.-C., Chen, B.L., Chung, Y.-C., Chou, J.: Application-aware traffic redirection: a mobile edge computing implementation toward future 5g networks. In: 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), pp. 17–23 (2017)
Younis, A., Tran, T.X., Pompili, D.: Bandwidth and energy-aware resource allocation for cloud radio access networks. IEEE Trans. Wireless Commun. 17(10), 6487–6500 (2018)
Nikaein, N.: 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, MCS 2015, pp. 36–43, New York, NY, USA, Association for Computing Machinery (2015)
Foukas, X., Nikaein, N., Kassem, M.M., Marina, M.K., Kontovasilis, K.: Flexran: a flexible and programmable platform for software-defined radio access networks. In: Proceedings of the 12th International on Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2016, pp. 427–441, New York, NY, USA Association for Computing Machinery (2016)
Kim, H., Rajkumar, R.: Predictable shared cache management for multi-core real-time virtualization. ACM Trans. Embed. Comput. Syst. 17 (1) (2017)
Reghenzani, F., Massari, G., Fornaciari, W.: The real-time linux kernel: a survey on preempt_rt. ACM Comput. Surv. 52(1), 36 (2019)
Xi, S., et al.: Real-time multi-core virtual machine scheduling in xen. In: 2014 International Conference on Embedded Software (EMSOFT), pp. 1–10 (2014)
Pahl, C.: Containerization and the PAAS cloud. IEEE Cloud Comput. 2(3), 24–31 (2015)
Struhár, V., Behnam, M., Ashjaei, M., Papadopoulos, A.V.: Real-time containers: a survey. In: Cervin, A., Yang, Y. (eds.) 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020), volume 80 of OpenAccess Series in Informatics (OASIcs), pp. 7:1–7:9, Dagstuhl, Germany. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik (2020)
Li, Z., Kihl, M., Lu, Q., Andersson, J.A.: Performance overhead comparison between hypervisor and container based virtualization. In: 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), pp 955–962 (2017)
Nikaein, N., Schiller, E., Favraud, R., Knopp, R., Alyafawi, I., Braun, T.: Towards a cloud-native radio access network. In: Mavromoustakis, C.X., Mastorakis, G., Dobre, C. (eds.) Advances in Mobile Cloud Computing and Big Data in the 5G Era. SBD, vol. 22, pp. 171–202. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-45145-9_8
Mao, C.N., et al.: Minimizing latency of real-time container cloud for software radio access networks. In: 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), pp. 611–616 (2015)
Cavicchioli, R., Capodieci, N., Bertogna, N.: Memory interference characterization between CPU cores and integrated GPUs in mixed-criticality platforms. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–10 (2017)
De, P., Mann, V., Mittaly, U.: Handling OS jitter on multicore multithreaded systems. In: 2009 IEEE International Symposium on Parallel & Distributed Processing, pp. 1–12 (2009)
Barletta, M.C., De Simone, L., Corte, R.D.: Achieving isolation in mixed-criticality industrial edge systems with real-time containers. In: 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik (2022)
Burns, A., Davis, R.I.: Mixed Criticality Systems-A Review (February 2022). (2022)
Reghenzani, F., Massari, G., Fornaciari. W.: Mixed time-criticality process interferences characterization on a multicore linux system. In: 2017 Euromicro Conference on Digital System Design (DSD), pp. 427–434 (2017)
Shekhar, S., Gokhale, A.: Dynamic resource management across cloud-edge resources for performance-sensitive applications. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 707–710 (2017)
Singh, A.K., Shafique, M., Kumar, A., Henkel, J.: Mapping on multi/many-core systems: survey of current and emerging trends. In: 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC), pp. 1–10 (2013)
Fried, J., Ruan, Z., Ousterhout, A., Belay, A.: Caladan: Mitigating interference at microsecond timescales. In: Proceedings of the 14th USENIX Conference on Operating Systems Design and Implementation, OSDI 2020, USA. USENIX Association (2020)
Fornaciari, W., Pozzi, G., Reghenzani, F., Marchese, A., Belluschi, M.: Runtime resource management for embedded and HPC systems. In: PARMA-DITAM 2016, pp. 31–36, New York, NY, USA. Association for Computing Machinery (2016)
Niknafs, M., Ukhov, I., Eles, P., Peng, Z.: Runtime resource management with workload prediction. In: Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019, New York, NY, USA. Association for Computing Machinery (2019)
Khasanov, R., Castrillon, J.: Energy-efficient runtime resource management for adaptable multi-application mapping. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 909–914 (2020)
Khasanov, R., Robledo, J., Menard, C., Goens, A., Castrillon, J.: Domain-specific hybrid mapping for energy-efficient baseband processing in wireless networks. ACM Trans. Embed. Comput. Syst. 20(5s), (2021)
Manvi , S.S., Shyam, G.K.: Resource management for infrastructure as a service (IAAS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)
Alves, M.P., Delicato, F.C., Santos, I.L., Pires, P.F.: Lw-coedge: a lightweight virtualization model and collaboration process for edge computing. World Wide Web 23(2), 1127–1175 (2020)
Azarmipour, M., Elfaham, H., Grothoff, J., von Trotha, C., Gries, G., Epple, U.: Dynamic resource management for virtualization in industrial automation. In: IECON 2018–44th Annual Conference of the IEEE Industrial Electronics Society, pp. 2878–2883 (2018)
Begam, R., Wang, W., Zhu, D.: Timer-cloud: Time-sensitive VM provisioning in resource-constrained clouds. IEEE Trans. Cloud Comput. 8(1), 297–311 (2020)
Doan, T.V., et al.: Containers vs virtual machines: choosing the right virtualization technology for mobile edge cloud. In: 2019 IEEE 2nd 5G World Forum (5GWF), pp. 46–52 (2019)
Foukas, X., Radunovic, B.: Concordia: teaching the 5g VRAN to share compute. In: Proceedings of the 2021 ACM SIGCOMM 2021 Conference, SIGCOMM ’21, pp. 580–596, New York, NY, USA. Association for Computing Machinery (2021)
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Ocampo, A.F., Bryhni, H. (2023). On the Realization of Cloud-RAN on Mobile Edge Computing. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-28694-0_56
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