Enabling data analytics and machine learning for 5G services within disaggregated multi-layer transport networks

Recent advances, related to the concepts of Artificial Intelligence (AI) and Machine Learning (ML) and with applications across multiple technology domains, have gathered significant attention due, in particular, to the overall performance improvement of such automated systems when compared to methods relying on human operation. Consequently, using AI/ML for managing, operating and optimizing transport networks is increasingly seen as a potential opportunity targeting, notably, large and complex environments.Such AI-assisted automated network operation is expected to facilitate innovation in multiple aspects related to the control and management of future optical networks and is a promising milestone in the evolution towards autonomous networks, where networks self-adjust parameters such as transceiver configuration.To accomplish this goal, current network control, management and orchestration systems need to enable the application of AI/ML techniques. It is arguable that Software-Defined Networking (SDN) principles, favouring centralized control deployments, featured application programming interfaces and the development of a related application ecosystem are well positioned to facilitate the progressive introduction of such techniques, starting, notably, in allowing efficient and massive monitoring and data collection.In this paper, we present the control, orchestration and management architecture designed to allow the automatic deployment of 5G services (such as ETSI NFV network services) across metropolitan networks, conceived to interface 5G access networks with elastic core optical networks at multi Tb/s. This network segment, referred to as Metro-haul, is composed of infrastructure nodes that encompass networking, storage and processing resources, which are in turn interconnected by open and disaggregated optical networks. In particular, we detail subsystems like the Monitoring and Data Analytics or the in-operation planning backend that extend current SDN based network control to account for new use cases.Peer ReviewedPostprint (author's final draft

On the complexity of configuration and orchestration for enabling disaggregated server provisioning in optical composable data centers

Due to the limitations of traditional data center (DC) architectures, the concept of infrastructure disaggregation has been proposed. DC resources are separated into multiple blades to be exploited independently. As a result, composable DC (CDC) infrastructures are achieved, enhancing the modularity of resource provisioning. However, disaggregation introduces additional challenges that need to be carefully analyzed. One relates to the potential complexity increase on the orchestration and infrastructure configuration that need to be performed when provisioning resources to support services. This aspect is highly influenced by the distribution of resources at the physical infrastructure. As such, when analyzing the performance of a CDC, it becomes essential to also study the related operational complexity of the resource orchestration and configuration phases. Furthermore, the requirements of several tenant services may impose heterogeneous deployments over the shared physical infrastructure in the form of either disaggregated single-server or multi-server distributions. The associated orchestration/configuration cost is again highly influenced by the data plane architecture of the CDC. With these aspects in mind, in this paper, we provide a methodology for analysis of the complexity of resource orchestration for a service deployment and the associated configuration cost in optical CDCs, considering various service deployment setups. A selected set of CDC architectures found in the literature is employed to quantitatively illustrate how the data plane design and service deployment strategies affect the complexity of infrastructure configuration and resource orchestration.This work has been supported by the Spanish Government through project TRAINER-B (PID2020-118011GB-C22) with FEDER contribution.Peer ReviewedPostprint (author's final draft

A control and management architecture supporting autonomic NFV services

The proposed control, orchestration and management (COM) architecture is presented from a high-level point of view; it enables the dynamic provisioning of services such as network data connectivity or generic network slicing instances based on virtual network functions (VNF). The COM is based on Software Defined Networking (SDN) principles and is hierarchical, with a dedicated controller per technology domain. Along with the SDN control plane for the provisioning of connectivity, an ETSI NFV management and orchestration system is responsible for the instantiation of Network Services, understood in this context as interconnected VNFs. A key, novel component of the COM architecture is the monitoring and data analytics (MDA) system, able to collect monitoring data from the network, datacenters and applications which outputs can be used to proactively reconfigure resources thus adapting to future conditions, like load or degradations. To illustrate the COM architecture, a use case of a Content Delivery Network service taking advantage of the MDA ability to collect and deliver monitoring data is experimentally demonstrated.Peer ReviewedPostprint (author's final draft

Demonstration of latency-aware 5G network slicing on optical metro networks

The H2020 METRO-HAUL European project has architected a latency-aware, cost-effective, agile, and programmable optical metro network. This includes the design of semi-disaggregated metro nodes with compute and storage capabilities, which interface effectively with both 5G access and multi-Tbit/s elastic optical networks in the core. In this paper, we report the automated deployment of 5G services, in particular, a public safety video surveillance use case employing low-latency object detection and tracking using on-camera and on-the-edge analytics. The demonstration features flexible deployment of network slice instances, implemented in terms of ETSI NFV Network Services. We summarize the key findings in a detailed analysis of end-to-end quality of service, service setup time, and soft-failure detection time. The results show that the round-trip-time over an 80 km link is under 800 µs and the service deployment time under 180 seconds.Horizon 2020 Framework Programme (761727); Bundesministerium für Bildung und Forschung (16KIS0979K).Peer ReviewedArticle signat per 25 autors/es: B. Shariati, Fraunhofer HHI, Berlin, Germany / L. Velasco, Universitat Politècnica de Catalunya, Barcelona, Spain / J.-J. Pedreno-Manresa, ADVA, Munich, Germany / A. Dochhan, ADVA, Munich, Germany / R. Casellas, Centre Tecnològic Telecomunicacions Catalunya, Castelldefels, Spain / A. Muqaddas, University of Bristol, Bristol, UK / O. Gonzalez de Dios, Telefónica, Madrid, Spain / L. Luque Canto, Telefónica, Madrid, Spain / B. Lent, Qognify GmbH, Bruchsal, Germany / J. E. Lopez de Vergara, Naudit HPCN, Madrid, Spain / S. Lopez-Buedo, Naudit HPCN, Madrid, Spain / F. Moreno, Universidad Politécnica de Cartagena, Cartagena, Spain / P. Pavon, Universidad Politécnica de Cartagena, Cartagena, Spain / M. Ruiz, Universitat Politècnica de Catalunya, Barcelona, Spain / S. K. Patri, ADVA, Munich, Germany / A. Giorgetti, CNIT, Pisa, Italy / F. Cugini, CNIT, Pisa, Italy / A. Sgambelluri, CNIT, Pisa, Italy / R. Nejabati, University of Bristol, Bristol, UK / D. Simeonidou, University of Bristol, Bristol, UK / R.-P. Braun, Deutsche Telekom, Germany / A. Autenrieth, ADVA, Munich, Germany / J.-P. Elbers, ADVA, Munich, Germany / J. K. Fischer, Fraunhofer HHI, Berlin, Germany / R. Freund, Fraunhofer HHI, Berlin, GermanyPostprint (author's final draft

AI-powered edge computing evolution for beyond 5G communication networks

Edge computing is a key enabling technology that is expected to play a crucial role in beyond 5G (B5G) and 6G communication networks. By bringing computation closer to where the data is generated, and leveraging Artificial Intelligence (AI) capabilities for advanced automation and orchestration, edge computing can enable a wide range of emerging applications with extreme requirements in terms of latency and computation, across multiple vertical domains. In this context, this paper first discusses the key technological challenges for the seamless integration of edge computing within B5G/6G and then presents a roadmap for the edge computing evolution, proposing a novel design approach for an open, intelligent, trustworthy, and distributed edge architecture.VERGE has received funding from the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101096034.Peer ReviewedPostprint (author's final draft

dReDBox: A Disaggregated Architectural Perspective for Data Centers

Data centers are currently constructed with fixed blocks (blades); the hard boundaries of this approach lead to suboptimal utilization of resources and increased energy requirements. The dReDBox (disaggregated Recursive Datacenter in a Box) project addresses the problem of fixed resource proportionality in next-generation, low-power data centers by proposing a paradigm shift toward finer resource allocation granularity, where the unit is the function block rather than the mainboard tray. This introduces various challenges at the system design level, requiring elastic hardware architectures, efficient software support and management, and programmable interconnect. Memory and hardware accelerators can be dynamically assigned to processing units to boost application performance, while high-speed, low-latency electrical and optical interconnect is a prerequisite for realizing the concept of data center disaggregation. This chapter presents the dReDBox hardware architecture and discusses design aspects of the software infrastructure for resource allocation and management. Furthermore, initial simulation and evaluation results for accessing remote, disaggregated memory are presented, employing benchmarks from the Splash-3 and the CloudSuite benchmark suites.This work was supported in part by EU H2020 ICT project dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft

Research challenges in nextgen service orchestration

Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world