View on 5G Architecture: Version 1.0

The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Towards cognitive in-operation network planning

Next-generation internet services such as live TV and video on demand require high bandwidth and ultra-low latency. The ever-increasing volume, dynamicity and stringent requirements of these services’ demands are generating new challenges to nowadays telecom networks. To decrease expenses, service-layer content providers are delivering their content near the end users, thus allowing a low latency and tailored content delivery. As a consequence of this, unseen metro and even core traffic dynamicity is arising with changes in the volume and direction of the traffic along the day. A tremendous effort to efficiently manage networks is currently ongoing towards the realisation of 5G networks. This translates in looking for network architectures supporting dynamic resource allocation, fulfilling strict service requirements and minimising the total cost of ownership (TCO). In this regard, in-operation network planning was recently proven to successfully support various network reconfiguration use cases in prospective scenarios. Nevertheless, additional research to extend in-operation planning capabilities from typical reactive optimization schemes to proactive and predictive schemes based on the analysis of network monitoring data is required. A hot topic raising increasing attention is cognitive networking, where an elevated knowledge about the network could be obtained as a result of introducing data analytics in the telecom operator’s infrastructure. By using predictive knowledge about the network traffic, in-operation network planning mechanisms could be enhanced to efficiently adapt the network by means of future traffic prediction, thus achieving cognitive in-operation network planning. In this thesis, we focus on studying mechanisms to enable cognitive in-operation network planning in core networks. In particular, we focus on dynamically reconfiguring virtual network topologies (VNT) at the MPLS layer, covering a number of detailed objectives. First, we start studying mechanisms to allow network traffic flow modelling, from monitoring and data transformation to the estimation of predictive traffic model based on this data. By means of these traffic models, then we tackle a cognitive approach to periodically adapt the core VNT to current and future traffic, using predicted traffic matrices based on origin-destination (OD) predictive models. This optimization approach, named VENTURE, is efficiently solved using dedicated heuristic algorithms and its feasibility is demonstrated in an experimental in-operation network planning environment. Finally, we extend VENTURE to consider core flows dynamicity as a result of metro flows re-routing, which represents a meaningful dynamic traffic scenario. This extension, which entails enhancements to coordinate metro and core network controllers with the aim of allowing fast adaption of core OD traffic models, is evaluated and validated in terms of traffic models accuracy and experimental feasibility.Els serveis d’internet de nova generació tals com la televisió en viu o el vídeo sota demanda requereixen d’un gran ample de banda i d’ultra-baixa latència. L’increment continu del volum, dinamicitat i requeriments d’aquests serveis està generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveïdors de contingut estan disposant aquests més a prop dels usuaris finals, aconseguint així una entrega de contingut feta a mida. Conseqüentment, estem presenciant una dinamicitat mai vista en el tràfic de xarxes de metro amb canvis en la direcció i el volum del tràfic al llarg del dia. Actualment, s’està duent a terme un gran esforç cap a la realització de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin l’assignació dinàmica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicació de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguració de xarxa en escenaris prospectius. No obstant, és necessari dur a terme més recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimització cap a un nou esquema proactiu basat en l’analítica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es també troba al centre d’atenció, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir analítica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el tràfic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicció de tràfic, assolint així el que anomenem com a “cognitive in-operation network Planning”. En aquesta tesi ens centrem en l’estudi de mecanismes que permetin establir “el cognitive in-operation network Planning” en xarxes de core. En particular, ens centrem en reconfigurar dinàmicament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de tràfic de xarxa, des del seu monitoritzat i transformació fins a l’estimació de models predictius de tràfic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament la VNT utilitzant matrius de tràfic basades en predicció de parells origen-destí (OD). Aquesta optimització, anomenada VENTURE, és resolta eficientment fent servir heurístiques dedicades i és posteriorment avaluada sota escenaris de tràfic de xarxa dinàmics. A continuació, estenem VENTURE considerant la dinamicitat dels fluxos de tràfic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de tràfic. Aquesta extensió involucra millores per coordinar els operadors de metro i core amb l’objectiu d’aconseguir una ràpida adaptació de models de tràfic OD. Finalment, proposem dues arquitectures de xarxa necessàries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-l’art com són OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluació numèrica fent servir un simulador de xarxes íntegrament dissenyat i desenvolupat per a aquesta tesi. Després d’aquesta validació basada en simulació, la factibilitat experimental de les arquitectures de xarxa proposades és avaluada en un entorn de proves distribuït

Towards cognitive in-operation network planning

Next-generation internet services such as live TV and video on demand require high bandwidth and ultra-low latency. The ever-increasing volume, dynamicity and stringent requirements of these services’ demands are generating new challenges to nowadays telecom networks. To decrease expenses, service-layer content providers are delivering their content near the end users, thus allowing a low latency and tailored content delivery. As a consequence of this, unseen metro and even core traffic dynamicity is arising with changes in the volume and direction of the traffic along the day. A tremendous effort to efficiently manage networks is currently ongoing towards the realisation of 5G networks. This translates in looking for network architectures supporting dynamic resource allocation, fulfilling strict service requirements and minimising the total cost of ownership (TCO). In this regard, in-operation network planning was recently proven to successfully support various network reconfiguration use cases in prospective scenarios. Nevertheless, additional research to extend in-operation planning capabilities from typical reactive optimization schemes to proactive and predictive schemes based on the analysis of network monitoring data is required. A hot topic raising increasing attention is cognitive networking, where an elevated knowledge about the network could be obtained as a result of introducing data analytics in the telecom operator’s infrastructure. By using predictive knowledge about the network traffic, in-operation network planning mechanisms could be enhanced to efficiently adapt the network by means of future traffic prediction, thus achieving cognitive in-operation network planning. In this thesis, we focus on studying mechanisms to enable cognitive in-operation network planning in core networks. In particular, we focus on dynamically reconfiguring virtual network topologies (VNT) at the MPLS layer, covering a number of detailed objectives. First, we start studying mechanisms to allow network traffic flow modelling, from monitoring and data transformation to the estimation of predictive traffic model based on this data. By means of these traffic models, then we tackle a cognitive approach to periodically adapt the core VNT to current and future traffic, using predicted traffic matrices based on origin-destination (OD) predictive models. This optimization approach, named VENTURE, is efficiently solved using dedicated heuristic algorithms and its feasibility is demonstrated in an experimental in-operation network planning environment. Finally, we extend VENTURE to consider core flows dynamicity as a result of metro flows re-routing, which represents a meaningful dynamic traffic scenario. This extension, which entails enhancements to coordinate metro and core network controllers with the aim of allowing fast adaption of core OD traffic models, is evaluated and validated in terms of traffic models accuracy and experimental feasibility.Els serveis d’internet de nova generació tals com la televisió en viu o el vídeo sota demanda requereixen d’un gran ample de banda i d’ultra-baixa latència. L’increment continu del volum, dinamicitat i requeriments d’aquests serveis està generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveïdors de contingut estan disposant aquests més a prop dels usuaris finals, aconseguint així una entrega de contingut feta a mida. Conseqüentment, estem presenciant una dinamicitat mai vista en el tràfic de xarxes de metro amb canvis en la direcció i el volum del tràfic al llarg del dia. Actualment, s’està duent a terme un gran esforç cap a la realització de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin l’assignació dinàmica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicació de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguració de xarxa en escenaris prospectius. No obstant, és necessari dur a terme més recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimització cap a un nou esquema proactiu basat en l’analítica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es també troba al centre d’atenció, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir analítica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el tràfic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicció de tràfic, assolint així el que anomenem com a “cognitive in-operation network Planning”. En aquesta tesi ens centrem en l’estudi de mecanismes que permetin establir “el cognitive in-operation network Planning” en xarxes de core. En particular, ens centrem en reconfigurar dinàmicament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de tràfic de xarxa, des del seu monitoritzat i transformació fins a l’estimació de models predictius de tràfic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament la VNT utilitzant matrius de tràfic basades en predicció de parells origen-destí (OD). Aquesta optimització, anomenada VENTURE, és resolta eficientment fent servir heurístiques dedicades i és posteriorment avaluada sota escenaris de tràfic de xarxa dinàmics. A continuació, estenem VENTURE considerant la dinamicitat dels fluxos de tràfic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de tràfic. Aquesta extensió involucra millores per coordinar els operadors de metro i core amb l’objectiu d’aconseguir una ràpida adaptació de models de tràfic OD. Finalment, proposem dues arquitectures de xarxa necessàries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-l’art com són OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluació numèrica fent servir un simulador de xarxes íntegrament dissenyat i desenvolupat per a aquesta tesi. Després d’aquesta validació basada en simulació, la factibilitat experimental de les arquitectures de xarxa proposades és avaluada en un entorn de proves distribuït.Postprint (published version

A quantitative survey of the power saving potential in IP-Over-WDM backbone networks

The power consumption in Information and Communication Technologies networks is growing year by year; this growth presents challenges from technical, economic, and environmental points of view. This has lead to a great number of research publications on "green" telecommunication networks. In response, a number of survey works have appeared as well. However, with respect to backbone networks, most survey works: 1) do not allow for an easy cross validation of the savings reported in the various works and 2) nor do they provide a clear overview of the individual and combined power saving potentials. Therefore, in this paper, we survey the reported saving potential in IP-over-WDM backbone telecommunication networks across the existing body of research in that area. We do this by mapping more than ten different approaches to a concise analytical model, which allows us to estimate the combined power reduction potential. Our estimates indicate that the power reduction potential of the once-only approaches is 2.3x in a Moderate Effort scenario and 31x in a Best Effort scenario. Factoring in the historic and projected yearly efficiency improvements ("Moore's law") roughly doubles both values on a ten-year horizon. The large difference between the outcome of Moderate Effort and Best Effort scenarios is explained by the disparity and lack of clarity of the reported saving results and by our (partly) subjective assessment of the feasibility of the proposed approaches. The Moderate Effort scenario will not be sufficient to counter the projected traffic growth, although the Best Effort scenario indicates that sufficient potential is likely available. The largest isolated power reduction potential is available in improving the power associated with cooling and power provisioning and applying sleep modes to overdimensioned equipment

Reconfigurable Optically Interconnected Systems

With the immense growth of data consumption in today's data centers and high-performance computing systems driven by the constant influx of new applications, the network infrastructure supporting this demand is under increasing pressure to enable higher bandwidth, latency, and flexibility requirements. Optical interconnects, able to support high bandwidth wavelength division multiplexed signals with extreme energy efficiency, have become the basis for long-haul and metro-scale networks around the world, while photonic components are being rapidly integrated within rack and chip-scale systems. However, optical and photonic interconnects are not a direct replacement for electronic-based components. Rather, the integration of optical interconnects with electronic peripherals allows for unique functionalities that can improve the capacity, compute performance and flexibility of current state-of-the-art computing systems. This requires physical layer methodologies for their integration with electronic components, as well as system level control planes that incorporates the optical layer characteristics. This thesis explores various network architectures and the associated control plane, hardware infrastructure, and other supporting software modules needed to integrate silicon photonics and MEMS based optical switching into conventional datacom network systems ranging from intra-data center and high-performance computing systems to the metro-scale layer networks between data centers. In each of these systems, we demonstrate dynamic bandwidth steering and compute resource allocation capabilities to enable significant performance improvements. The key accomplishments of this thesis are as follows. In Part 1, we present high-performance computing network architectures that integrate silicon photonic switches for optical bandwidth steering, enabling multiple reconfigurable topologies that results in significant system performance improvements. As high-performance systems rely on increased parallelism by scaling up to greater numbers of processor nodes, communication between these nodes grows rapidly and the interconnection network becomes a bottleneck to the overall performance of the system. It has been observed that many scientific applications operating on high-performance computing systems cause highly skewed traffic over the network, congesting only a small percentage of the total available links while other links are underutilized. This mismatch of the traffic and the bandwidth allocation of the physical layer network presents the opportunity to optimize the bandwidth resource utilization of the system by using silicon photonic switches to perform bandwidth steering. This allows the individual processors to perform at their maximum compute potential and thereby improving the overall system performance. We show various testbeds that integrates both microring resonator and Mach-Zehnder based silicon photonic switches within Dragonfly and Fat-Tree topology networks built with conventional equipment, and demonstrate 30-60% reduction in execution time of real high-performance benchmark applications. Part 2 presents a flexible network architecture and control plane that enables autonomous bandwidth steering and IT resource provisioning capabilities between metro-scale geographically distributed data centers. It uses a software-defined control plane to autonomously provision both network and IT resources to support different quality of service requirements and optimizes resource utilization under dynamically changing load variations. By actively monitoring both the bandwidth utilization of the network and CPU or memory resources of the end hosts, the control plane autonomously provisions background or dynamic connections with different levels of quality of service using optical MEMS switching, as well as initializing live migrations of virtual machines to consolidate or distribute workload. Together these functionalities provide flexibility and maximize efficiency in processing and transferring data, and enables energy and cost savings by scaling down the system when resources are not needed. An experimental testbed of three data center nodes was built to demonstrate the feasibility of these capabilities. Part 3 presents Lightbridge, a communications platform specifically designed to provide a more seamless integration between processor nodes and an optically switched network. It addresses some of the crucial issues faced by the works presented in the previous chapters related to optical switching. When optical switches perform switching operations, they change the physical topology of the network, and they lack the capability to buffer packets, resulting in certain optical circuits being unavailable. This prompts the question of whether it is safe to transmit packets by end hosts at any given time. Lightbridge was developed to coordinate switching and routing of optical circuits across the network, by having the processors gain information about the current state of the optical network before transmitting packets, and being able to buffer packets when the optical circuit is not available. This part describes details of Lightbridge which is constituted by a loadable Linux kernel module along with other supporting modifications to the Linux kernel in order to achieve the necessary functionalities

Results and achievements of the ALLIANCE Project: New network solutions for 5G and beyond

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high-capacity access/metro/core optical networks, and intra-datacentre (DC) network and computational resources into a single converged 5G network infrastructure. The present paper overviews the main achievements obtained in the ALLIANCE project. This project ambitiously aims at architecting a converged 5G-enabled network infrastructure satisfying those needs to effectively realise the envisioned upcoming Digital Society. In particular, we present two networking solutions for 5G and beyond 5G (B5G), such as Software Defined Networking/Network Function Virtualisation (SDN/NFV) on top of an ultra-high-capacity spatially and spectrally flexible all-optical network infrastructure, and the clean-slate Recursive Inter-Network Architecture (RINA) over packet networks, including access, metro, core and DC segments. The common umbrella of all these solutions is the Knowledge-Defined Networking (KDN)-based orchestration layer which, by implementing Artificial Intelligence (AI) techniques, enables an optimal end-to-end service provisioning. Finally, the cross-layer manager of the ALLIANCE architecture includes two novel elements, namely the monitoring element providing network and user data in real time to the KDN, and the blockchain-based trust element in charge of exchanging reliable and confident information with external domains.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under contract FEDER TEC2017-90034-C2 (ALLIANCE project) and by the Generalitat de Catalunya under contract 2017SGR-1037 and 2017SGR-605.Peer ReviewedPostprint (published version

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029