Exposing radio network information in a MEC-in-NFV environment: the RNISaaS concept

IEEE Conference on Network Softwarization (2019)The Radio Network Information Service (RNIS) is one of the key services provided by a Multi-access Edge Computing Platform (MEP), as specified in the relevant ETSI MEC standards. It is responsible for interacting with the Radio Access Network (RAN), collecting RAN-level information about User Equipment (UE) and exposing it to mobile edge applications, which can in turn utilize it to dynamically adjust their behavior to optimally match the RAN conditions. Putting the provision of RNIS in the context of the emerging MEC-in-NFV environment, where the components and services of the MEC architecture, including the MEP itself, are integrated in an NFV environment and are delivered on top of a virtualized infrastructure, we present our standards-compliant RNIS implementation based on OpenAirInterface and study critical performance aspects for its provision as a virtual function. Since the RNIS design and operation follows the publish-subscribe model, we provide alternative implementations using different message brokering technologies (RabbitMQ and Apache Kafka), and compare their use and performance in an effort to evaluate their suitability for providing RNIS in an as-a-service manner.This work has been partially funded by the EC H2020 5G-Transformer Project (grant no. 761536)

A MEC-based Extended Virtual Sensing for Automotive Services

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development

A MEC-based Extended Virtual Sensing for Automotive Services

Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.This work was supported by the European Commission through the H2020 5G-TRANSFORMER project (Project ID 761536). The work of Christian Vitale was also supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for Euro-pean Programmes, Coordination, and Development

METIS research advances towards the 5G mobile and wireless system definition

[EN] The Mobile and wireless communications Enablers for the Twenty-twenty Information Society (METIS) project is laying the foundations of Fifth Generation (5G) mobile and wireless communication system putting together the point of view of vendors, operators, vertical players, and academia. METIS envisions a 5G system concept that efficiently integrates new applications developed in the METIS horizontal topics and evolved versions of existing services and systems. This article provides a first view on the METIS system concept, highlights the main features including architecture, and addresses the challenges while discussing perspectives for the further research work.Part of this work has been performed in the framework of the FP7 project ICT-317669 METIS, which is partly funded by the European Commission. The authors would like to acknowledge the contributions of their colleagues in METIS with special thanks to Petar Popovski, Peter Fertl, David Gozalvez-Serrano, Andreas Hoglund, Zexian Li, and Krystian Pawlak. Also thanks to Josef Eichinger and Malte Schellmann for the fruitful discussions during the revision of this article.Monserrat Del Río, JF.; Mange, G.; Braun, V.; Tullberg, H.; Zimmermann, G.; Bulakci, O. (2015). METIS research advances towards the 5G mobile and wireless system definition. EURASIP Journal on Wireless Communications and Networking. 2015(53):1-16. https://doi.org/10.1186/s13638-015-0302-9S116201553Cisco, in Global Mobile Data Traffic Forecast Update, 2014–2019 White Paper, February 2015. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white_paper_c11-520862.pdfMETIS, in Mobile and wireless communications Enablers for the Twenty-twenty Information Society, EU 7th Framework Programme project, http://www.metis2020.com .ICT-317669 METIS project, in Scenarios, requirements and KPIs for 5G mobile and wireless system, Deliverable D1.1, May 2013, https://www.metis2020.com/documents/deliverables/B Ahlgren, C Dannewitz, C Imbrenda, D Kutscher, B Ohlman, A survey of information-centric networking. IEEE Commun Mag 50(7), 26–36 (2012)A Osseiran, F Boccardi, V Braun, K Kusume, P Marsch, M Maternia, O Queseth, M Schellmann, H Schotten, H Taoka, H Tullberg, MA Uusitalo, B Timus, M Fallgren, Scenarios for the 5G mobile and wireless communications: the vision of the METIS project. IEEE Commun Mag 52(5), 26–35 (2014)D Gomez-Barquero, D Calabuig, JF Monserrat, N Garcia and J Perez-Romero, Hopfield neural network - based approach for joint dynamic resource allocation in heterogeneous wireless networks, in Proceedings 64th IEEE Vehicular Technology Conference (VTC), Montreal. 2006JF Monserrat, P Sroka, G Auer, J Cabrejas, D Martin-Sacristan, A Mihovska, R Rossi, A. Saul, R. Schoenen, Advanced Radio Resource Management for IMT-Advanced in WINNER+ (II), in Proc. Future Network and Mobile Summit, pp.1-9, June 2010.F Boccardi, RW Heath, A Lozano, TL Marzetta, P Popovski, Five disruptive technology directions for 5G. IEEE Commun Mag 52(2), 74–80 (2014)JG Andrews, S Buzzi, C Wan, SV Hanly, A Lozano, ACK Soong, JC Zhang, What will 5G be? IEEE J Sel Area Comm 32(6), 1065–1082 (2014)MN Tehrani, M Uysal, H Yanikomeroglu, Device-to-device communication in 5G cellular networks: challenges, solutions, and future directions. IEEE Commun Mag 52(5), 86–92 (2014)N Bhushan, L Junyi, D Malladi, R Gilmore, D Brenner, A Damnjanovic, R Sukhavasi, C Patel, S Geirhofer, Network densification: the dominant theme for wireless evolution into 5G. IEEE Commun Mag 52(2), 82–89 (2014)K Okino, T Nakayama, C Yamazaki, H Sato, Y Kusano, Pico Cell Range Expansion with Interference Mitigation toward LTE-Advanced Heterogeneous Networks, in Proc. of IEEE International Conference on Communications (ICC), 2011.P Mugen, L Dong, W Yao, L Jian Li, C Hsiao-Hwa, Self-configuration and self-optimization in LTE-advanced heterogeneous networks. IEEE Commun Mag 51(5), 36–45 (2013)I Siomina, D Yuan, Load Balancing in Heterogeneous LTE: Range Optimization via Offset and Load-coupling Characterization, in Proc. of IEEE Int. Conference on Communications (ICC). June 2012.KI Pedersen, Y Wang, B Soret, F Frederiksen, eICIC Functionality and Performance for LTE HetNet Co-Channel Deployments, in Proc. of IEEE Vehicular Technology Conf, Sep 2012X Gu, X Deng, Q Li, L Zhang, W Li, Capacity Analysis and Optimization in Heterogeneous Network with Adaptive Cell Range Control, Int. J. Antennas. Propag. 2014(215803), 10 (2014)K Smiljkovikj, P Popovski, L Gavrilovska, Analysis of the Decoupled Access for DL and UL in Wireless Heterogeneous Networks, in IEEE Wireless Communications Letters, in press, doi:10.1109/LWC.2015.2388676.P Agyapong, M. Iwamura, D. Staehle, W. Kiess, A. Benjebbour, Design considerations for a 5G network architecture. IEEE Commun Mag 52(11), 65–75 (2014)L Yan, X Fang, Reliability Evaluation of 5G C/U-plane Decoupled Architecture for High-speed Railway. EURASIP J Wirel Commun Netw 2014, 127 (2014)B Zafar, S Gherekhloo, M Haardt, Analysis of multihop relaying networks: communication between range-limited and cooperative nodes. IEEE Veh Technol Mag 7(3), 40–47 (2012)Study on Mobile Relay for Evolved Universal Terrestrial Radio Access (E-UTRA), 3GPP TR 36.836, V2.0.2, July 2013.A Krendzel, LTE-A Mobile Relay Handling: Architecture Aspects, in Proc. of the 19th European Wireless Conference (EW), Guildford, UK, pp. 1–6, 2013.M Khanfouci, Y Sui, A Papadogiannis, and M Färber, Moving Relays and Mobility aspects, ARTIST4G project deliverable D3.5c-v2.0, 2012.F Haider, M Dianati, and R Tafazolli, A Simulation Based Study of Mobile Femtocell Assisted LTE Networks, in Proc. Of the 7th International Wireless Communications and Mobile Computing Conference (IWCMC), Istanbul, Turkey, pp. 2198–2203, 2011F Haider, W Haiming, H Haas, Y Dongfeng, W Haiming, G Xiqi, Y Xiao-Hu, E Hepsaydir, Spectral efficiency enalysis of mobile Femtocell based cellular systems, in Proc. of the 13th International Conference on Communication Technology (ICCT), Jinan, pp. 347–351, September 2011.ICT-317669 METIS project, Initial report on horizontal topics, first results and 5G system concept, Deliverable D6.2, April 2014, https://www.metis2020.com/documents/deliverables/Study on LTE Device to Device Proximity Services, 3GPP TR 36.843, 2014.V Yazıcı, UC Kozat, M Oguz, Sunay, A new control plane for 5G network architecture with a case study on unified handoff, mobility, and routing management. IEEE Commun Mag 52(11), 76–85 (2014)F Malandrino, C Casetti, C-F Chiasserini, Toward D2D-enhanced heterogeneous networks. IEEE Commun Mag 52(11), 94–100 (2014)A Asadi, Q Wang, V Mancuso, A survey on device-to-Device communication in cellular networks. IEEE Commun Surv Tutor 16(4), 1801–1819 (2014)D Feng, L Lu, YY Wu, GY Li, G Feng, S Li, Device-to-device communications underlaying cellular networks. IEEE Trans Commun 61(8), 3541–3551 (2013)C Xu, L Song, Z Han, Q Zhao, X Wang, X Cheng, B Jiao, Efficiency resource allocation for device-to-device underlay communication systems: a reverse iterative combinatorial auction based approach. IEEE J Sel Area Comm 31(9), 348–358 (2013)S Lingyang, D Niyato, H Zhu, E Hossain, Game-theoretic resource allocation methods for device-to-device communication. IEEE Wireless Commun 21(3), 136–144 (2014)G Aloi, M Di Felice, V Loscrì, P Pace, G Ruggeri, Spontaneous smartphone networks as a user-centric solution for the future internet. IEEE Commun Mag 52(12), 26–33 (2014)PA Frangoudis, GC Polyzos, Security and performance challenges for user-centric wireless networking. IEEE Commun Mag 52(12), 48–55 (2014)ITU-R M.2079, in Technical and operational information for identifying Spectrum for the terrestrial component of future development of IMT-2000 and IMT-Advanced, 2006AB MacKenzie, LA DaSilva, Application of signal processing to addressing wireless data demand [in the spotlight]. IEEE Signal Process Mag 29(6), 168–166 (2012)X Cheng, Y Koucheryavy, Y Li, F Zhao, T Znati (ed.), Dynamic Spectrum Access for Throughput, Delay, and Fairness Enhancement In Cognitive Radio Networks, EURASIP J Wirel Commun Netw, November 2014MR Akdeniz, Y Liu, MK Samimi, S Sun, S Rangan, TS Rappaport, E Erkip, Millimeter wave channel modeling and cellular capacity evaluation. IEEE J Sel Area Comm 32(6), 1164–1179 (2014)A Adhikary, E Al Safadi, M Samimi, R Wang, G Caire, TS Rappaport, AF Molisch, Joint spatial division and multiplexing for mm-wave channels. IEEE J Sel Area Comm 32(6), 1239–1255 (2014)K Pentikousis, Y Wang, W Hu, Mobileflow: toward software-defined mobile networks. IEEE Commun Mag 51(7), 44–53 (2013)E3 D2.4. Cognitive Function mapping to Networks Architectures, Standard Engineering and Software Technologies for Cognitive Radios, E3 Project Deliverable 2.4, December 2009.R Wang, H Hu, X Yang, Potentials and challenges of C-RAN supporting Multi-RATs toward 5G mobile networks. IEEE. Access. 2(1187), 1195 (2014)V Jungnickel, K Manolakis, W Zirwas, B Panzner, V Braun, M Lossow, M Sternad, R Apelfrojd, T Svensson, The role of small cells, coordinated multipoint, and massive MIMO in 5G. IEEE Commun Mag 52(5), 44–51 (2014)E Larsson, O Edfors, F Tufvesson, T Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun Mag 52(2), 186–195 (2014)W Roh, S Ji-Yun, P Jeongho, L Byunghwan, L Jaekon, K Yungsoo, C Jaeweon, C Kyungwhoon, F Aryanfar, Millimeter-wave Beamforming as an Enabling Technology for 5G Cellular Communications: Theoretical Feasibility and Prototype Results. IEEE Commun Mag 2(2), 106–113 (2014)AL Swindlehust, E Ayanoglu, P Heydari, F Capolino, Millimeter-wave massive MIMO: the next wireless revolution? IEEE Commun Mag 52(9), 56–62 (2014)L Lu, GY Li, AL Swindlehurst, A Ashikhmin, Z Rui, An overview of massive MIMO: benefits and challenges. IEEE J Sel Top Signal Process 8(5), 742–758 (2014)S Roger, D Calabuig, J Cabrejas, JF Monserrat, Multi-user non-coherent detection for downlink MIMO communication. IEEE Signal Process Lett 21(10), 1225–1229 (2014)X Wang, M Chen, T Taleb, A Ksentini, V Leung, Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2), 131–139 (2014)ETSI ISG NFV (Operator Group), Network Functions Virtualisation – Network Operator Perspectives on Industry Progress, Updated White Paper, October 2013NGMN Alliance, in Suggestions on potential solutions for C-RAN, White Paper, January 2013ETSI ISG NFV, Network Functions Virtualisation (NFV); Virtual Network Functions Architecture, v1.1.1, Dec 2014.A Tzanakaki, MP Anastasopoulos, GS Zervas, BR Rofoee, R Nejabati, D Simeonidou, Virtualization of heterogeneous wireless-optical network and IT infrastructures in support of cloud and mobile cloud services. IEEE Commun Mag 51(8), 155–161 (2013)A Manzalini, R Saracco, C Buyukkoc, P Chemuouil, S Kukliński, A Gladisch, M Fukui, W Shen, M Fujiwara, K Shimano, E Dekel, D Soldani, M Ulema, W Cerroni, F Callegati, G Schembra, V Riccobene, C Mas Machuca, A Galis, J Mueller, Software-Defined Networks for Future Networks and Services: Main Technical Challenges and Business Implications, IEEE Workshop SDN4FNS, 1–16, 2013CEPT ECC, in Licensed Shared Access (LSA), ECC Report 205, February 2014IEEE 802.11, in IEEE 802.11-2012 Part11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE Standard, March 2012D Martín-Sacristán, JF Monserrat, J Cabrejas-Peñuelas, D Calabuig, S Garrigas, N Cardona, On the way towards fourth-generation mobile: 3GPP LTE and LTE-Advanced. EURASIP J Wirel Commun Netw 2009, 10 (2009)ICT-317669 METIS, Final report on architecture, Deliverable D6.4, January 2015, https://www.metis2020.com/documents/deliverables/ICT-317669 METIS, Report on simulation results and evaluations, Deliverable D6.5, February 2015, https://www.metis2020.com/documents/deliverables/Ö Bulakci, Z Ren, C Zhou, J Eichinger, P Fertl, S Stanczak, Dynamic Nomadic Node Selection for Performance Enhancement in Composite Fading/Shadowing Environments, (IEEE VTC 2014-Spring, Seoul, South Korea)ICT-317669 METIS, Final report on network-level solutions, Deliverable D4.3 Version 1, February 201

Providing Low Latency Guarantees for Slicing-Ready 5G Systems via Two-Level MAC Scheduling

5G comes with the promise of sub-millisecond latency, which is critical for realizing an array of emerging URLLC services, including industrial, entertainment, telemedicine, automotive, and tactile Internet applications. At the same time, slicing-ready 5G networks face the challenge of accommodating other heterogeneous coexisting services with different and potentially conflicting requirements. Providing latency and reliability guarantees to URLLC service slices is thus not trivial. We identify transmission scheduling at the RAN level as a significant contributor to end-to-end latency when considering network slicing. In this direction, we propose a two-level MAC scheduling framework that can effectively handle uplink and downlink transmissions of network slices of different characteristics over a shared RAN, applying different per-slice scheduling policies, and focusing on reducing latency for URLLC services. Our scheme offers the necessary flexibility to dynamically manage radio resources to meet the stringent latency and reliability requirements of URLLC, as demonstrated by our simulation results

CDN slicing over a multi-domain edge cloud

Abstract We present an architecture for the provision of video Content Delivery Network (CDN) functionality as a service over a multi-domain cloud. We introduce the concept of a CDN slice, that is, a CDN service instance which is created upon a content provider’s request, is autonomously managed, and spans multiple, potentially heterogeneous, edge cloud infrastructures. Our design is tailored to a 5G mobile network context, building on its inherent programmability, management flexibility, and the availability of cloud resources at the mobile edge level, thus close to end users. We exploit Network Functions Virtualization (NFV) and Multi-access Edge Computing (MEC) technologies, proposing a system which is aligned with the recent NFV and MEC standards. To deliver a Quality-of-Experience (QoE) optimized video service, we derive empirical models of video QoE as a function of service workload, which, coupled with multi-level service monitoring, drive our slice resource allocation and elastic management mechanisms. These management schemes feature autonomic compute resource scaling, and on-the-fly transcoding to adapt video bit-rate to the current network conditions. Their effectiveness is demonstrated via testbed experiments.</P

Secure Network Management Using a Key Distribution Center

Abstract. The proliferation and growth of modern computer networks have made network management infrastructures an integral part of the administration process. Nevertheless, most of these do not have the notion of security assimilated by design. Thus, existing network equipment cannot be managed securely without additional hardware or software security modules. This paper discusses a simple, yet robust, solution for securing an existing management infrastructure based on the SNMP protocol.