• We use deep learning to forecast mobile traffic, with particular emphasis on traffic peaks • We integrated exogenous features with traffic input for closed-loop prediction • We generated synthetic data to address scarcity and enhance model generalization • We validated performance on real-world data, outperforming baseline methods Mobile cellular networks are experiencing rapid growth in data demand, largely driven by data-intensive applications such as video streaming. In particular, the popularity of live events can induce abrupt and localized traffic surges, often resulting in congestion and performance degradation. For these reasons, accurate traffic forecasting is expected to play an increasingly important role in future sixth-generation (6G) mobile networks, supporting both real-time operational responses and long-term capacity planning. In this work, we move beyond classical traffic forecasting approaches and propose an AI-based framework that explicitly conditions traffic predictions on contextual information available in advance, while also leveraging generative data augmentation to address data scarcity. Through a comprehensive analysis conducted on two major Italian cities and several real-world datasets spanning four years, we show that traffic dynamics exhibit strong correlations with the occurrence of football matches. Building on this observation, we design a forecasting methodology that combines historical traffic measurements with scheduled event information to forecast future traffic over the prediction horizon. To improve robustness under event-driven and high-load conditions, we further introduce a lightweight synthetic data generation strategy that mitigates the scarcity and imbalance of rare traffic patterns. Experimental results demonstrate that the proposed framework improves forecasting accuracy, particularly during peak and busy-hour regimes, compared to baseline traffic-only approaches

This paper introduces a novel optimization framework for Network Functions Virtualization (NFV) that addresses the efficient implementation of end-to-end service requests in physical networks. Our approach characterizes each server node by a reliability function reflecting its computational load, which aids in balancing workloads and mitigating congestion. By optimizing the reliability metric along the route, our approach ensures robust end-to-end service quality. We formulate the NFV deployment problem as a non-convex mixed-integer non-linear programming (MINLP) model aimed at minimizing both deployment and operational costs while maximizing resource utilization, addressing also per-node installation conflicts and inter-VNF incompatibilies. Given the NP-hard nature of the problem, we develop efficient linearization techniques and bounding schemes, using also dynamic programming, to convert the formulation into a tractable mixed-integer linear programming (MILP) model. Additionally, a cutting-plane-based heuristic with a warm-start strategy is proposed to further accelerate convergence. Experimental evaluations on real-world network topologies demonstrate that our framework offers scalable and cost-effective solutions compared to existing approaches.

As mobile data traffic continues to increase and network operators expand their infrastructures, energy consumption in Radio Access Networks (RAN) becomes a critical concern, particularly during network expansion. This paper addresses the problem of predicting RAN energy consumption in network expansion scenarios. We develop and evaluate different forecasting strategies, including per-site and network-wide models, as well as Machine Learning (ML)-based approaches, using real-world data from a LTE network. Our results show that while simple network-wide models perform well when the base station (BS) configurations remain constant, ML models are more effective in scenarios where BS configurations change during network expansion. The insights from this study can help mobile network operators improve energy efficiency by adapting their networks to traffic patterns and expansion processes, supporting both cost management and sustainability goals.

We investigate the problem of resilient and energy-aware Virtual Network Function (VNF) placement and routing in softwarized networks under the threat of targeted cyberattacks. We model the system as a bilevel interdiction game, where a malicious attacker strategically disrupts servers within a fixed resource budget, while a network provider reacts by minimizing energy consumption through optimized VNF deployment and flow routing. The lower-level problem includes capacity constraints, service function chaining, and a server energy model accounting for idle and load-dependent consumption. Attack-induced load shifts are captured via additive energy penalties on compromised nodes. To solve this inherently difficult bilevel integer program, we de-velop a single-level reformulation via interdiction cuts and propose a cutting-plane algorithm to explore the attacker's strategy space efficiently. Numerical experiments show the effectiveness of the approach in quantifying trade-offs between resilience and energy efficiency, supporting trustworthy and adaptive NFV deployment in critical infrastructures.

This paper introduces a novel optimization framework for Network Functions Virtualization (NFV) that addresses the efficient implementation of end-to-end service requests in physical networks. Our approach characterizes each server node by a reliability function reflecting its computational load, which aids in balancing workloads and mitigating congestion. By optimizing the reliability metrics along the route, our approach ensures robust end-to-end service quality. We formulate the NFV deployment problem as a non-convex mixed-integer non-linear programming (MINLP) model aimed at minimizing both deployment and operational costs while maximizing resource utilization. Given the NP-hard nature of the problem, we develop efficient linearization techniques and bounding schemes, using also dynamic programming, to convert the formulation into a tractable mixed-integer linear programming (MILP) model. Additionally, a cutting-plane-based heuristic with a warm-start strategy is proposed to further accelerate convergence. Experimental evaluations on real-world network topologies demonstrate that our framework offers scalable and cost-effective solutions compared to existing approaches.

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

Cellular networks worldwide are currently experiencing a significant surge in service demand, forcing operators to focus on the accurate modeling of network dynamics as a key task to enhance efficiency. Besides being useful for optimizing network functioning, mobile data analytics have unleashed unforeseen opportunities to address several social and urban issues on a large scale. In this work, we seize such opportunities and propose a framework capable of profiling urban settlements based on the interplay between their attractiveness and the characteristics of the built environment. Focusing on the impact of the COVID-19 pandemic on mobile users’ behavior, we conduct a comprehensive case study in Italy. Leveraging real-world mobile radio access data, we investigate the spatial variations in people’s visiting patterns, providing insights into how these changes correlate with the social and urban context characterizing the reference area.

Today, video streaming is responsible for about 50 % of all Internet traffic worldwide. To cope with this massive amount of video streaming data, a major concern of network providers is the development of efficient traffic monitoring and management techniques. However, fast and efficient monitoring which leads to intelligent management decisions is becoming highly resource intense and complex, due to the steady increase of the number of streamed videos and the quality of the streamed content. Considering HTTP adaptive streaming applications, we present a simple machine learning free, uplink request based approach to estimate drops in the video playback butter. These drops are the first indicator leading to quality impairment events like downwards quality changes or stalling. With our approach, instead of analyzing thousands of encrypted packets in the network, we only need to consider one single packet every 5 s-10s on average, depending on the video chunk size and independently of the played resolution. Nevertheless, we are able to detect nearly all stalling events or consider a trade-off between stalling detection recall and false positives. Our approach can be implemented completely moving average based, thus not requiring any parameter setup or other expert knowledge. Due to its simplicity, it can be deployed on any access point to collect streaming quality information that is useful for active network management and intelligent resource provisioning but also in a data center to analyze a massive number of parallel video flows.

Mobile network traffic forecasting is a fundamental building block for key management tasks such as resources allocation. In particular, being able to predict traffic volume peaks is of primary importance for a correct network operation. This paper proposes to exploit exogenous inputs to predict such peaks, focusing in particular on football matches. We show with an analysis conducted on 4 of the major cities in Italy for a period of 6 months that volume traffic peaks are strongly correlated with the occurrence of football matches between specific teams and we propose a methodology to exploit the football calendar to forecast traffic peaks. The proposed forecasting frameworks allows to predict more than 50 % of the peaks, improving the forecasting performance compared to a traffic signature based approach and being able to forecast the maximum traffic volume with an average overestimation below +10% of the actual value.

The dramatic growth in cellular traffic volume requires cellular network operators to develop strategies to carefully dimension and manage the available network resources. Forecasting traffic volumes is a fundamental building block for any proactive management strategy and is therefore of great interest in such a context. Differently from what found in the literature, where network traffic is generally predicted in the short-term, in this work we tackle the problem of forecasting busy hour traffic, i.e., the time series of observed daily maxima traffic volumes. We tackle specifically forecasting in the long term (one, two months ahead) and we compare different approaches for the task at hand, considering different forecasting algorithms as well as relying or not on a cluster-based approach which first groups network cells with similar busy hour traffic profiles and then fits per-cluster forecasting models to predict the traffic loads. Results on a real cellular network dataset show that busy hour traffic can be forecasted with errors below 10% for look-ahead periods up to 2 months in the future. Moreover, when clusters are available, we improve forecasting accuracy up to 8% and 5% for look-ahead of 1 and 2 months, respectively.

Network operators need to continuously upgrade their infrastructures in order to keep their customer satisfaction levels high. Crowdsourcing-based approaches are generally adopted, where customers are directly asked to answer surveys about their experience. Since the number of collaborative users is generally low, network operators rely on Machine Learning models to predict the satisfaction levels/QoE of the users rather than directly measuring it through surveys. Finally, combining the true/predicted users satisfaction labels with information on each user mobility (e.g, which network sites each user has visited and for how long), an operator may reveal critical areas in the network and drive/prioritize investments properly. In this work, we propose an empirical framework tailored to assess the quality of the detection of under-performing cells starting from subjective user experience grades. The framework allows to simulate diverse networking scenarios, where a network characterized by a small set of under-performing cells is visited by heterogeneous users moving through it according to realistic mobility models . The framework simulates both the processes of satisfaction surveys delivery and users satisfaction prediction, considering different delivery strategies and evaluating prediction algorithms characterized by different prediction performance. We use the simulation framework to test empirically the performance of under-performing sites detection in general scenarios characterized by different users density and mobility models to obtain insights which are generalizable and that provide interesting guidelines for network operators.

Continuously monitoring the network activity to proactively recognise possible problems and prevent users QoE degradation is a major concern for network operators, for both mobile radio and home networks. Considering video streaming applications, which generate the majority of overall Internet traffic, monitoring the chunk requests from the video client to the video server is of particular interest, as they not only indicate that a download burst is imminent, but their type (e.g., request of an audio or video chunk) and frequency also allow to estimate which and how much data will be downloaded to the client. In this work, we propose a machine-learning based video streaming traffic monitoring architecture able to i) predict when next uplink request will be issued by the video client and ii) classify the type of next uplink request. We evaluate the system performance on a dataset of more than 900 HTTP adaptive streaming sessions and 15,000 request-response exchanges, where both the predictor of the next request arrival and the request type classifier are fed with lightweight features extracted from encrypted traffic in an online fashion, both in the uplink and downlink directions of the traffic. Results show that i) the system is able to classify the type of a HAS uplink requests with an accuracy greater than 95 % and ii) pipe-lining request type classification and prediction of next request arrival time improves the final prediction performance.

Monitoring the Quality of Experience (QoE) of the customer base is a key task for Mobile Network Operators (MNOs), and it is generally performed by collecting users feedbacks through directed surveys. When such feedbacks are few in number, a MNO may predict the users QoE starting from objective network measurements, gathered directly from the users equipments through crowdsourcing. In this work, we compare such a traditional approach with a different one, where the data used for predicting the users QoE is gathered directly at the network access, using Key Performance Indicators (KPI) available on each base station. Although such KPIs are aggregated by design (i.e., they refer to the distribution of a population of users rather than to a single individual), we show through experiments with a country-wide dataset that their predictive power is comparable and in some cases superior than the one of crowdsourcing. Such a result is particularly attractive for MNOs, since network KPIs are generally much easily obtainable than crowdsourcing data.