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Marco Ruffini

Publications and source records attributed to Marco Ruffini.

65 records · Page 4Linked to original sources
arXiv2022

Optimal Slicing of Virtualised Passive Optical Networks to Support Dense Deployment of Cloud-RAN and Multi-Access Edge Computing

The commercialization of Cloud-RAN, and Open-RAN in particular, is a key factor to enable 5G cell densification, by providing lower cost and more agile deployment of small cells. In addition, the adoption of MEC is important to support ultra-low latency and high reliability required by mission-critical applications, which constitute a milestone of the 5G and beyond vision of a fully connected society. However, connecting antenna site, C-RAN processing and MEC at low cost is challenging, as it requires high-capacity, low latency connectivity delivered through a highly inter-connected topology. While PON is being considered as a solution for providing low-cost connectivity to C-RAN, they only allow data transmission from the endpoints (for example hosting RU at the antenna site) towards a central node (e.g., the central office, hosting computing equipment), thus cannot support traffic from RU towards MEC end nodes that could host DU and possibly CU and network core. This led to research into the evolution of PON architectures with the ability to provide direct communications between endpoints, thus supporting mesh traffic patterns required by MEC installations. In this context, virtualization plays a key role in enabling efficient resource allocation (i.e. optical transmission capacity) to endpoints, according to their communication patterns. In this article, we address the challenge of dynamic allocation of virtual PON slices over mesh-PON architectures to support C-RAN and MEC nodes. We make use of a mixed analytical-iterative model to compute optimal virtual PON slice allocation, with the objective of minimizing the use of MEC node resources, while meeting a target latency threshold (100 $μs$ in our scenario). Our method is particularly effective in reducing computation time, enabling virtual PON slice allocation in timescales compatible with real-time or near real-time operations.

cs.NI↗
arXiv2022

A Min-Max Fair Resource Allocation Framework for Optical x-haul and DU/CU in Multi-tenant O-RANs

The recently proposed open-radio access network (O-RAN) architecture embraces cloudification and network function virtualization techniques to perform the base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). This enables the cloud-RAN vision in full, where mobile network operators (MNOs) could install their own RUs, but then lease on-demand computational resources for the processing of DU and CU functions from commonly available open-cloud (O-Cloud) servers via open x-haul interfaces due to variation of load over the day. This creates a multi-tenant scenario where multiple MNOs share networking as well as computational resources. In this paper, we propose a framework that dynamically allocates x-haul and DU/CU resources in a multi-tenant O-RAN ecosystem with min-max fairness guarantees. This framework ensures that a maximum number of RUs get sufficient resources while minimizing the OPEX for their MNOs. Moreover, in order to provide an access network architecture capable of sustaining low-latency and high capacity between RUs and edge-computing devices, we consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where the PON virtualization technique is used to provide a direct optical connection between end-points. This creates a virtual mesh interconnection among all the nodes such that the RUs can be connected to the Edge-Clouds at macro-cell RU locations as well as to the O-Cloud servers at the central office locations. Furthermore, we analyze the system performance with our proposed framework and show that MNOs can operate with a better cost-efficiency than baseline greedy resource allocation with uniform cost-sharing.

cs.NI↗
arXiv2021

Optimal virtual PON slicing to support ultra-low latency mesh traffic pattern in MEC-based Cloud-RAN

As progressive densification of cells, deployment of Cloud-RAN and \ac{MEC} are coming into reality to support the ultra-low latency with high reliability in 5G and beyond, it generates mesh traffic pattern across fronthaul network. This led to evolution of PON architectural enhancements with virtualization in order to support such mesh traffic pattern. However, allocation of virtual PON slices dynamically over such mesh-PON based fronthaul transport is becoming a research challenge. In this paper, we provide a mixed analytical-iterative model to compute optimal virtual PON slice allocation, providing mesh access connectivity with ultra-low end-to-end latency in next-generation MEC-based Cloud-RAN. Our proposed method can compute optimal virtual PON slice allocation in timescales compatible with real-time or near real-time operations.

cs.NI↗
arXiv2021

Experimental Demonstration of RAN Functional Split over virtual PON Transport Network

Cloud-Radio Access Networks (Cloud-RANs) are separating the mobile networks base station functions into three units, the connection between the two of them is referred to as the fronthaul network. This work demonstrates the transmission of user data transport blocks between the distributed Medium Access Control (MAC) layer and local Physical (PHY) layer in the radiounit over a Passive Optical Network (PON). PON networks provide benefits in terms of economy and flexibility when used for Cloud-RAN fronthaul transport. However, the PON upstream scheduling can introduce additional latency that might not satisfy the requirements imposed by Cloud-RAN functional split. In this work we demonstrate how our virtual Dynamic Bandwidth Allocation(DBA) concept can be used to effectively communicate with the mobile Long Term Evolution (LTE) scheduler, adopting the well known cooperative DBA mechanism, to reduce the PON latency to satisfactory values. Thus, our results show the feasibility ofusing PON technology as transport medium of the fronthaul for the MAC/PHY functional split, in a fully virtualised environment.Further background traffic is added, so that measurements show a more realistic scenario. The obtained round trip times indicates that using PON at fronthaul might be limited to the distance of 11km for a synchronised scenario, or no compliance for a non-synchronised scenario.

cs.NI↗
arXiv2019

A Variable Rate Fronthaul Scheme for Cloud Radio Access Networks (C-RAN)

Cloud Radio Access Network (C-RAN) is considered as one of the most promising candidate for Fifth Generation (5G) wireless communication system. However, C-RAN imposes stringent requirements on fronthaul in terms of capacity which in turn becomes a bottleneck for deployment of cloud-RAN in dense networks. Compressed CPRI and functional split are two primary solutions to overcome this. However, this makes Remote Radio Unit (RRU) more complex and the computational resources in RRU are localized and thus cannot be shared with other RRUs when not in use. In this paper, we introduce the concept of Variable Rate Fronthaul (VRF) for C-RAN. This scheme operates on a CPRI type of interface (e.g., one that transmits I/Q data samples) with the novelty of dynamically changing the cell bandwidth, and consequently the fronthaul data rates, depending on the cell load, with the support of a Software Defined Network (SDN) controller. This allows for a more efficient transport of C-RAN cells data over a shared backhaul. We first propose a mathematical analysis of the VRF performance using a queuing theory approach based on the Markov model. We then provide the results of our simulation framework both for validation and in support of the mathematical analysis. Our results show that the proposed VRF scheme provides significantly lower blocking probability over a shared backhaul than standard CPRI.

eess.SP↗
arXiv2018

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

cs.NI↗
arXiv2017

Complex Systems Science meets 5G and IoT

We propose a new paradigm for telecommunications, and develop a framework drawing on concepts from information (i.e., different metrics of complexity) and computational (i.e., agent based modeling) theory, adapted from complex system science. We proceed in a systematic fashion by dividing network complexity understanding and analysis into different layers. Modelling layer forms the foundation of the proposed framework, supporting analysis and tuning layers. The modelling layer aims at capturing the significant attributes of networks and the interactions that shape them, through the application of tools such as agent-based modelling and graph theoretical abstractions, to derive new metrics that holistically describe a network. The analysis phase completes the core functionality of the framework by linking our new metrics to the overall network performance. The tuning layer augments this core with algorithms that aim at automatically guiding networks toward desired conditions. In order to maximize the impact of our ideas, the proposed approach is rooted in relevant, near-future architectures and use cases in 5G networks, i.e., Internet of Things (IoT) and self-organizing cellular networks.

cs.NI↗
arXiv2016

Multi-Dimensional Convergence in Future 5G Networks

Future 5G services are characterised by unprecedented need for high rate, ubiquitous availability, ultra-low latency and high reliability. The fragmented network view that is widespread in current networks will not stand the challenge posed by next generations of users. A new vision is required, and this paper provides an insight on how network convergence and application-centric approaches will play a leading role towards enabling the 5G vision. The paper, after expressing the view on the need for an end-to-end approach to network design, brings the reader into a journey on the expected 5G network requirements and outlines some of the work currently carried out by main standardisation bodies. It then proposes the use of the concept of network convergence for providing the overall architectural framework to bring together all the different technologies within a unifying and coherent network ecosystem. The novel interpretation of multi-dimensional convergence we introduce leads us to the exploration of aspects of node consolidation and converged network architectures, delving into details of optical-wireless integration and future convergence of optical data centre and access-metro networks. We then discuss how ownership models enabling network sharing will be instrumental in realising the 5G vision. The paper concludes with final remarks on the role SDN will play in 5G and on the need for new business models that reflect the application-centric view of the network. Finally, we provide some insight on growing research areas in 5G networking.

cs.NI↗
arXiv2011

A Combinatorial Optimisation Approach to Designing Dual-Parented Long-Reach Passive Optical Networks

We present an application focused on the design of resilient long-reach passive optical networks. We specifically consider dual-parented networks whereby each customer must be connected to two metro sites via local exchange sites. An important property of such a placement is resilience to single metro node failure. The objective of the application is to determine the optimal position of a set of metro nodes such that the total optical fibre length is minimized. We prove that this problem is NP-Complete. We present two alternative combinatorial optimisation approaches to finding an optimal metro node placement using: a mixed integer linear programming (MIP) formulation of the problem; and, a hybrid approach that uses clustering as a preprocessing step. We consider a detailed case-study based on a network for Ireland. The hybrid approach scales well and finds solutions that are close to optimal, with a runtime that is two orders-of-magnitude better than the MIP model.

cs.AI↗