Smartphones and mobile applications are staple tools in the operation of current-age public demonstrations, where they support organizers and participants in, \eg scaling the management of the events or communicating live about their objectives and traction. % The widespread use of mobile services during protests also presents interesting opportunities to observe the dynamics of these manifestations from a digital perspective. Previous studies in that direction have focused on the analysis of content posted in selected social media so as to forecast, survey or ascertain the success of public protests. In this paper, we take a different viewpoint and present a holistic characterization of the consumption of the whole spectrum of mobile applications during social protests. Hinging upon pervasive measurements in the production network of the incumbent network operator and focusing on the 2023 French pension reform strikes, we unveil how large masses of protesters generate a clearly recognizable footprint on mobile service demands in the examined events. In fact, the footprint is so strong that it lets us develop models informed by the usage of selected mobile applications that are capable of (i) tracking the spatiotemporal evolution of the target demonstrations and (ii) estimate the time-varying number of attendees from aggregate network operator data only. We demonstrate the utility of such privacy-preserving models to perform a-posteriori analyses of the public protests that reveal, e.g., the precise progression of the marches, alternate minor routes taken by participants or their dispersal at the end of the events.

The roll out of 5G, coupled with the traffic monitoring capabilities of modern industry-grade networks, offers an unprecedented opportunity to closely observe the impact that the introduction of a new major wireless technology has on the end users. In this paper, we seize such a unique chance, and carry out a first-of-its-kind in-depth analysis of 5G adoption along spatial, temporal and service dimensions. Leveraging massive measurement data about application-level demands collected in a nationwide 4G/5G network, we characterize the impact of the new technology on when, where and how mobile subscribers consume 5G traffic both in aggregate and for individual types of services. This lets us unveil the overall incidence of 5G in the total mobile network traffic, its spatial and temporal fluctuations, its effect on the way 5G services are consumed, the way individual services and geographical locations contribute to fluctuations in the 5G demand, as well as surprising connections between socioeconomic status of local populations and the way the 5G technology is presently consumed.

Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for orthogonal heterogeneous networks is proposed to minimize transmission power while respecting individual users’ throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.

Indoor cellular networks (ICNs) are anticipated to become a principal component of 5G and beyond systems. ICNs aim at extending network coverage and enhancing users' quality of service and experience, consequently producing a substantial volume of traffic in the coming years. Despite the increasing importance that ICNs will have in cellular deployments, there is nowadays little understanding of the type of traffic demands that they serve. Our work contributes to closing that gap, by providing a first characterization of the usage of mobile services across more than 4, 500 cellular antennas deployed at over 1,000 indoor locations in a whole country. Our analysis reveals that ICNs inherently manifest a limited set of mobile application utilization profiles, which are not present in conventional outdoor macro base stations (BSs). We interpret the indoor traffic profiles via explainable machine learning techniques, and show how they are correlated to the indoor environment. Our findings show how indoor cellular demands are strongly dependent on the nature of the deployment location, which allows anticipating the type of demands that indoor 5G networks will have to serve and paves the way for their efficient planning and dimensioning.

We analyze 4G and 5G transport-layer sessions generated by a wide range of mobile services at over 282,000 base stations (BSs) of an operational mobile network, and carry out a statistical characterization of their demand rates, associated traffic volume and temporal duration. Based on the gained insights, we model the arrival process of sessions at heterogeneously loaded BSs, the distribution of the session-level load and its relationship with the session duration, using simple yet effective mathematical approaches. Our models are fine-tuned to a variety of services, and complement existing tools that mimic packet-level statistics or aggregated spatiotemporal traffic demands at mobile network BSs. They thus offer an original angle to mobile traffic data generation, and support a more credible performance evaluation of solutions for network planning and management. We assess the utility of the models in practical application use cases, demonstrating how they enable a more trustworthy evaluation of solutions for the orchestration of sliced and virtualized networks.

Abstract Tuning application performance on modern computing infrastructures involves choices in a vast design space as modern computing architectures can have several complex structures impacting performance. Moreover, different applications use these structures in different ways, leading to a challenging performance function. Consequently, it is hard for compilers or experts to find optimal compilation parameters for an application that maximizes such performance function. One approach to tackle this problem is to evaluate many possible optimization plans and select the best among them. However, executing an application to measure its performance for every plan can be very expensive. To tackle this problem, previous work has investigated the use of Machine Learning techniques to predict the performance of the applications without executing them quickly. In this work, we evaluate the use of graph neural networks (GNN) to make fast predictions without executing the application to guide the selection of good optimization sequences. We propose a GNN architecture to make such predictions. We train and test it using 30 thousand different compilation plans applied to 300 different applications, using ARM64 and LLVM IR code representations as input. Our results indicate that the control and data flow graph can then learn features from the control and data flow graph to outperform nongraph‐aware Machine Learning models. Our GNN architecture achieved 91% accuracy in our dataset compared to 79% when using a nongraph‐aware architecture–taking only 16ms to predict a given input. If the application been optimized took an average of 10 s to execute, and we evaluated 1000 optimization sequences, it would take almost 9 h to assess all pairs, but only 16 s with our GNN .

The COVID-19 pandemic has affected our lives and how we use network infrastructures in an unprecedented way. While early studies have started shedding light on the link between COVID-19 containment measures and mobile network traffic, we presently lack a clear understanding of the implications of the virus outbreak, and of our reaction to it, on the usage of mobile apps. We contribute to closing this gap, by investigating how the spatiotemporal usage of mobile services has evolved through different response measures enacted in France during a continued seven-month period in 2020 and 2021. Our work complements previous studies in several ways: (i) it delves into individual service dynamics, whereas previous studies have not gone beyond broad service categories; (ii) it encompasses different types of containment strategies, allowing to observe their diverse effects on mobile traffic; (iii) it covers both spatial and temporal behaviors, providing a comprehensive view on the phenomenon. These elements of novelty let us lay new insights on how the demands for hundreds of different mobile services are reacting to the new environment set forth by the pandemics.