arXiv subjects

Cristina Rottondi

Publications and source records attributed to Cristina Rottondi.

10 recordsLinked to original sources

arXiv2026

Exploiting the Alternatives: Coordinated Learning via Hierarchical RL for Dynamic VNEAP

Virtual Network Embedding (VNE) is a key enabler of network slicing, yet most formulations assume that each Virtual Network Request (VNR) has a fixed topology. Recently, VNE with Alternatives (VNEAP) was introduced to capture malleable VNRs, where each request can be instantiated using one of several functionally equivalent topologies that trade resources differently. This flexibility can improve embedding feasibility, but only if the orchestrator can jointly select suitable alternatives and embed them under dynamic arrivals. This paper proposes HRL-VNEAP, a hierarchical reinforcement learning approach for dynamic VNEAP. A high-level policy selects the most suitable alternative topology (or rejects the request), and a low-level policy embeds the chosen topology onto the substrate network. Experiments on realistic substrate topologies under varying arrival rates show that naive exploitation strategies provide only modest gains, whereas HRL-VNEAP outperforms state of the art approaches, improving acceptance ratio by up to 22%, and net profit by up to 20%. An offline MILP upper bound is also used on tractable instances to quantify the remaining optimality gap.

cs.NI↗

arXiv2026

Application-Integrated Slicing towards 6G: The Musical Metaverse Use Case

Emerging immersive applications are expected to support heterogeneous groups of users with fundamentally different communication and computation requirements. Using the Musical Metaverse (MM) as a representative example, we show how such applications expose limitations of current 5G network slicing and orchestration frameworks, which remain largely service-centric and operate through decoupled application and network management mechanisms. While existing Quality of Service (QoS) and slicing techniques provide traffic differentiation, they cannot jointly account for application semantics, shared resources, and differentiated end-to-end requirements across multiple user classes within the same service instance. To address these limitations, we introduce application-integrated slicing, a framework that unifies application and network orchestration within a common end-to-end service model and enables differentiated KPI targets for multiple user classes within a single logical slice. Using a MM reference scenario deployed over cloud-edge network, we show that application-integrated slicing reduces both resource provisioning cost and QoS violation rates compared with conventional decoupled approaches.

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arXiv2025

MuMeNet: A Network Simulator for Musical Metaverse Communications

The Metaverse, a shared and spatially organized digital continuum, is transforming various industries, with music emerging as a leading use case. Live concerts, collaborative composition, and interactive experiences are driving the Musical Metaverse (MM), but the requirements of the underlying network and service infrastructures hinder its growth. These challenges underscore the need for a novel modeling and simulation paradigm tailored to the unique characteristics of MM sessions, along with specialized service provisioning strategies capable of capturing their interactive, heterogeneous, and multicast-oriented nature. To this end, we make a first attempt to formally model and analyze the problem of service provisioning for MM sessions in 5G/6G networks. We first formalize service and network graph models for the MM, using "live audience interaction in a virtual concert" as a reference scenario. We then present MuMeNet, a novel discrete-event network simulator specifically tailored to the requirements and the traffic dynamics of the MM. We showcase the effectiveness of MuMeNet by running a linear programming based orchestration policy on the reference scenario and providing performance analysis under realistic MM workloads.

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arXiv2024

Demonstration of a Networked Music Performance Experience with MEVO

In this paper we present a Networked Music Performance system currently under development at Politecnico di Torino. We demonstrate its use in a distributed concert held in June 2023, which featured three musicians in Turin (Italy) and three musicians in Wrocław (Poland). Although in its early stages, the system proved to be already stable enough to appear transparent to the remote audience.

cs.NI↗

arXiv2024

Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures

Adapting to concept drift is a challenging task in machine learning, which is usually tackled using incremental learning techniques that periodically re-fit a learning model leveraging newly available data. A primary limitation of these techniques is their reliance on substantial amounts of data for retraining. The necessity of acquiring fresh data introduces temporal delays prior to retraining, potentially rendering the models inaccurate if a sudden concept drift occurs in-between two consecutive retrainings. In communication networks, such issue emerges when performing traffic forecasting following a~failure event: post-failure re-routing may induce a drastic shift in distribution and pattern of traffic data, thus requiring a timely model adaptation. In this work, we address this challenge for the problem of traffic forecasting and propose an approach that exploits adaptive learning algorithms, namely, liquid neural networks, which are capable of self-adaptation to abrupt changes in data patterns without requiring any retraining. Through extensive simulations of failure scenarios, we compare the predictive performance of our proposed approach to that of a reference method based on incremental learning. Experimental results show that our proposed approach outperforms incremental learning-based methods in situations where the shifts in traffic patterns are drastic.

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arXiv2020

Scheduling of Emergency Tasks for Multiservice UAVs in Post-Disaster Scenarios

Single-task UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication support, and other specific tasks. When the geographical area of operation of single-task missions is common, e.g., in post-disaster recovery scenarios, it is more efficient to have multiple tasks carried out as part of a single UAV mission. In these scenarios, the UAVs' equipment and mission plan must be carefully selected to minimize the carried load and overall resource consumption. In this paper, we investigate the joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, two heuristic solutions are developed for large-scale environments to cope with the increased complexity of the optimization framework. The joint planning is applied to a specific scenario of a flood in the San Francisco area. Results show the effectiveness of the proposed heuristic solutions, which provide good performance and allow for drastic savings in the computational time required to plan the UAVs' trajectories with respect to the optimal approach, thus enabling prompt reaction to the emergency events.

cs.NI↗

arXiv2020

A Deep Learning Approach for Low-Latency Packet Loss Concealment of Audio Signals in Networked Music Performance Applications

Networked Music Performance (NMP) is envisioned as a potential game changer among Internet applications: it aims at revolutionizing the traditional concept of musical interaction by enabling remote musicians to interact and perform together through a telecommunication network. Ensuring realistic conditions for music performance, however, constitutes a significant engineering challenge due to extremely strict requirements in terms of audio quality and, most importantly, network delay. To minimize the end-to-end delay experienced by the musicians, typical implementations of NMP applications use un-compressed, bidirectional audio streams and leverage UDP as transport protocol. Being connection less and unreliable,audio packets transmitted via UDP which become lost in transit are not re-transmitted and thus cause glitches in the receiver audio playout. This article describes a technique for predicting lost packet content in real-time using a deep learning approach. The ability of concealing errors in real time can help mitigate audio impairments caused by packet losses, thus improving the quality of audio playout in real-world scenarios.

cs.SD↗

arXiv2019

Multiservice UAVs for Emergency Tasks in Post-disaster Scenarios

UAVs are increasingly being employed to carry out surveillance, parcel delivery, communication-support and other specific tasks. Their equipment and mission plan are carefully selected to minimize the carried load an overall resource consumption. Typically, several single task UAVs are dispatched to perform different missions. In certain cases, (part of) the geographical area of operation may be common to these single task missions (such as those supporting post-disaster recovery) and it may be more efficient to have multiple tasks carried out as part of a single UAV mission using common or even additional specialized equipment. In this paper, we propose and investigate a joint planning of multitask missions leveraging a fleet of UAVs equipped with a standard set of accessories enabling heterogeneous tasks. To this end, an optimization problem is formulated yielding the optimal joint planning and deriving the resulting quality of the delivered tasks. In addition, a heuristic solution is developed for large-scale environments to cope with the increased complexity of the optimization framework. The developed joint planning of multitask missions is applied to a specific post-disaster recovery scenario of a flooding in the San Francisco area. The results show the effectiveness of the proposed solutions and the potential savings in the number of UAVs needed to carry out all the tasks with the required level of quality.

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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↗

arXiv2018

Imprecise Markov Models for Scalable and Robust Performance Evaluation of Flexi-Grid Spectrum Allocation Policies

The possibility of flexibly assigning spectrum resources with channels of different sizes greatly improves the spectral efficiency of optical networks, but can also lead to unwanted spectrum fragmentation.We study this problem in a scenario where traffic demands are categorised in two types (low or high bit-rate) by assessing the performance of three allocation policies. Our first contribution consists of exact Markov chain models for these allocation policies, which allow us to numerically compute the relevant performance measures. However, these exact models do not scale to large systems, in the sense that the computations required to determine the blocking probabilities---which measure the performance of the allocation policies---become intractable. In order to address this, we first extend an approximate reduced-state Markov chain model that is available in the literature to the three considered allocation policies. These reduced-state Markov chain models allow us to tractably compute approximations of the blocking probabilities, but the accuracy of these approximations cannot be easily verified. Our main contribution then is the introduction of reduced-state imprecise Markov chain models that allow us to derive guaranteed lower and upper bounds on blocking probabilities, for the three allocation policies separately or for all possible allocation policies simultaneously.

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