The continuous increase in electricity demand, combined with factors such as political instability, cyberattacks, and the rising frequency of natural disasters linked to climate change, poses significant challenges to the reliability and stability of the power grid. Failures in the power grid can have cascading effects on the communication infrastructure, which heavily depends on a stable electricity supply. Enhancing the resilience of computing and communication facilities is therefore essential to support critical aspects of daily life. To address this, we view a group of Base Stations (BSs) in a Radio Access Network (RAN) as consumers and producers within a Micro Grid (MG) equipped with Photovoltaic (PV) panels, energy storage, and interconnected by dedicated power cables for energy exchange. We propose novel RAN resource and energy management strategies designed to maximize RAN Quality of Service (QoS) during Power Grid Outages (PGOs), given the available energy within the MG. Our evaluation considers factors such as the number of BSs in the MG, PV panel capacity, PGO duration, BS traffic profiles, PV panel placement, the energy battery employment, and the geographical extent of the MG. Results demonstrate that the proposed methodology improves QoS, increasing the hourly Managed Traffic by up to more than 300%, an improvement obtained when the MG consists of 4 2-kWp-PV-equipped BSs, compared with isolated 2 kWp PV-equipped BSs, during daily hours. When the MG is implemented, small PV panels ( ≤ 6kWp) perform comparably to large ones ( ≥ 12kWp) in isolated BS setups, making the solution space-efficient. Additionally, performance is mostly unaffected by BS traffic profiles or PGO duration. Effective energy management, accounting for cable losses, and the central placement of PV panels within the MG are critical for optimizing performance.

The on-demand activation of frequency bands in radio access networks can lead to a significant reduction of energy consumption, but risks to adversely impact performance. This approach to frequency band management can be applied either to a group of co-located base stations whose operators adopt a network sharing approach or to a single base station that uses multiple frequency bands. We develop a stochastic model based on the Matrix Analytic Method for the quantification of system performance and energy consumption in the case of coexisting streaming and elastic services. By computing numerical results in a specific setting, we show that the on-demand (de)activation, possibly combined with the adaptation of the data rate of streaming services, succeeds in greatly reducing energy consumption with respect to the case in which frequency bands are always active, with limited impact on the performance experienced by users. We also show that the introduction of a hysteresis in the frequency band activation/deactivation process allows the optimization of the energy/performance trade-off. Finally, we show that performance is not drastically altered by the burstiness of the elastic service request arrival process, and we prove that the separate analysis of streaming and elastic services provides quite optimistic results with respect to the joint analysis made possible by our model.

In recent years, we have witnessed an unprecedented upsurge in popularity and advancement of Real-time Transport Protocol (RTP)-based real-time communication (RTC) applications. For the sake of their optimizations, Quality of Service (QoS) prediction serves as a viable venue for enhancing network monitoring and enabling preemptive solutions. However, existing methodologies are typically tailored and constrained to individual traffic flows and QoS metrics, lagging in correlation capturing and computational efficiency. In light of this, we argue that “one model is enough” to conquer these challenges, and propose a novel deep learning (DL) framework namelyOh, employing a teacher-student scheme with two training stages. The first (teacher) involves a sophisticated Long Short-Term Memory (LSTM) neural network (NN) empowered by a customized attention structure, and the second (student) comprises simple feedforward NNs to distill knowledge and reduce complexity. Specifically,Ohleverages a multi-task learning paradigm, mapping extracted features to four key QoS indicators. It is capable of simultaneously handling unlimited amount of concurrent RTP flows with packet-level information and performing end-to-end predictions of multiple QoS metrics in one single shot. Our work is based on massive traffic collected during real video-teleconferencing calls using various software, and benchmarked against multiple other machine learning (ML)/DL algorithms. As a result,Oh-teacher yields superior prediction performance, whereasOh-student achieves distinctly enhanced temporal efficiency with comparable forecasting outcomes.

Bandwidth estimation (BWE) serves as a pivotal mechanism in real-time communications (RTC), supporting congestion control (CC) by governing traffic sending rate and thereby optimizing network performance. In recent years, reinforcement learning (RL) has surged to prominence, transcending conventional approaches as a superior bandwidth estimator. However, as the hype continues to escalate, the relentless pursuit of increasingly sophisticated algorithms eclipses the investigation into the core principles of CC, while the inherent flaws of RL appear to be overlooked. In this work, we rethink the necessity of RL in RTC, and contend that BWE itself, rather than reward-driven learning, is the deterministic factor, thus rendering a regressor sufficient. We first underscore the paramount importance of BWE accuracy and propose a simple feedforward neural network-based regressor with dual training stages: an offline stage imitating the perfect estimator and an online stage accommodating traffic dynamics. Utilizing an advanced RTC platform, we benchmark our solution against multiple state-of-the-art RL-based BWE algorithms. Conclusively, our regressor achieves superior performance, potentially charting a new course for BWE and CC in RTC.

To design efficient Radio Access Networks (RANs) capable of handling the increasing power demand of modern networks, it is crucial to accurately assess the power consumption of a Base Station (BS). Since existing models are outdated, we propose a new data-driven model that accurately reflects the power demand of modern BSs. We derive the model using real-world measurements from operative three-sector BSs, differing for technologies and transmission frequencies. Our findings suggest that models tailored to specific technologies and transmission frequencies outperform generalized ones. Moreover, linear regression models consistently perform up to 96% better than those based on Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs) and benchmarks from the literature, highlighting a predominantly linear relationship between input features and power needs. Finally, the most accurate power estimations are produced by a linear model using traffic volume, load, maximum transmission power, and cable power losses, as regressors, with errors ranging from 4 W to 38 W.

In light of the unprecedented proliferation of cloud-native applications, cyber threats targeting cloud services have escalated markedly, emerging as a critical concern for stake-holders. The intrinsic nature of cloud infrastructures renders them particularly susceptible to diverse attacks. In this context, effective attack identification becomes pivotal, facilitating swift responses and preventive measures to mitigate risks and bolster overall security resilience. Despite a range of solutions in literature, attack classification remains an arduous task in industrial environments. To address this, we propose a comprehensive and deployment-friendly graph-based framework. It leverages cloud activity traces, transforming system events to graph structures, and we enumerate 4 different types of attack within a controlled environment. We frame a multi-classification problem, and construct multi-level features to characterize distinct attacks amidst background activities, bypassing the complexity of Deep Learning (DL). To evaluate the efficacy, we compare with multiple Graph Neural Networks (GNNs), and our solution yields comparable performance, demonstrating a promising candidate for the practical and efficient cyber threat detection.

Network Sharing (NS) has gained increasing interest for Mobile Operators (MOs) because of the high investment costs of 5G combined with a period of low return of investment. The benefits that NS can offer include reduced capital and operational expenditures, because of fewer equipment, and lower energy consumption, possibly combined with higher network resiliency. While these aspects have been investigated in the literature, in those works more attention was paid to the overall benefits, disregarding asymmetries between the involved MOs. In this paper we address the issue of fairness in sharing network infrastructure among MOs and we introduce a Fair Cooperative Network Sharing (FCNS) framework that dynamically offloads traffic among co-located BSs owned by different MOs with two primary objectives: distributing active operational time more equitably between BSs in a pair, and significantly decreasing the failure rate of BS pairs. Simulation results based on empirical mobile traffic data demonstrate that the proposed FCNS framework effectively balances the BS active time across operators. In addition, FCNS achieves energy savings of up to 38% for each MO within a BS pair and reduces the failure rate by approximately 20%. These findings highlight the potential of cooperative network sharing as a feasible and sustainable solution for resilient 5G deployments.

This paper introduces a novel analytical framework for evaluating energy-efficient, QoS-aware network-sharing strategies in cellular networks. Leveraging stochastic geometry, our framework enables the systematic assessment of network performance across a range of sharing paradigms, including both conventional single-operator scenarios and advanced hybrid strategies that enable full integration and cooperation among multiple mobile network operators. Our framework incorporates diverse user densities, rate requirements, and energy consumption models to ensure comprehensive analysis. Applying our results to real-world datasets from French mobile network operators, we demonstrate that hybrid network sharing can yield substantial energy savings, up to 35%, while maintaining QoS. Furthermore, our results allow us to characterize how the benefits of network sharing vary as a function of the geographical and functional characteristics of the deployment area. These findings highlight the potential of collaborative sharing strategies to enhance operational efficiency and sustainability in next-generation cellular networks.

The growing demand for mobile connectivity is pushing terrestrial radio access networks (RANs) toward congestion, especially during peak hours in dense urban areas. High Altitude Platform Stations (HAPSs) provide a flexible means to support overloaded base stations (BSs) through aerial traffic offloading. This paper presents a simulation-based framework that evaluates HAPS-assisted offloading under realistic traffic traces, considering baseline and upscaled demand scenarios. We assess packet-level delay, user denial, energy consumption, and infrastructure expansion. Results show that HAPS markedly reduce queuing delay and nearly eliminate user denial under moderate overload, while also lowering operational and capital expenditures by relieving congested BSs. These findings confirm the viability of HAPS as a complementary aerial layer for congestion mitigation, postponing the need for terrestrial densification in the path toward B5G networks.

Artificial Intelligence (AI) applications are becoming more widespread, raising significant environmental concerns due to the high energy use required to train large Deep Neural Networks (DNNs). To address this issue, we conduct a detailed quantitative analysis of the energy consumption of various models across different training steps and datasets. Our study measures energy use at each training stage with a comprehensive review of selected models. By tracking energy consumption during each training step, we find that the backpropagation phase is the most energy-intensive. Additionally, we evaluate the power limits and performance features of Graphics Processing Units (GPUs), collecting empirical data on their behavior under different GPU power settings. Based on these insights, we explore the integration of locally installed renewable energy sources, such as solar power and battery systems, with the electrical grid to enhance the energy sustainability of GPU operations. We introduce and test an innovative approach for managing energy and computing resources, aiming to optimize energy use and reduce operational costs. Our results demonstrate that this method can reduce energy consumption by more than 40% and operational costs by almost 25%, paving the way for greener AI solutions.

Telecom base stations increasingly rely on solar power and battery storage to achieve sustainable, cost-effective energy solutions, but battery degradation poses a significant challenge to system reliability and longevity. This paper introduces an innovative optimization framework that accounts for lithium-ion battery aging, modeling both calendar and cycle degradation with a novel segment-based approach. Designed for seamless integration into cost-effective energy planning, the framework optimizes photovoltaic (PV) panel and battery sizing to minimize costs and extend system lifespan. Validated using real-world base station power consumption data, our approach outperforms traditional rainflow-based aging models, reducing battery cycle wear by up to 65.5% compared to aging-unaware methods and by an additional 10% over rainflow-based methods. By enabling real-time battery health tracking, it supports dynamic energy management, offering a practical solution for sustainable telecom networks.

In this paper, we consider a Radio Access Network (RAN) powered by the Smart Grid (SG), which provides monetary incentives to users who respond to the grid's explicit requests to increase or decrease their energy consumption. The typical solution for reducing the RAN's power needs is to employ Base Station (BS) sleep modes, but this may lead to a drop in user coverage. For this reason, we propose dynamically adjusting the bandwidth allocated to users in response to SG requests. Our study considers a realistic urban RAN and evaluates the effectiveness of bandwidth limitation in meeting the SG's power reduction requests. Results indicate that this approach is particularly effective in high-density user environments, reducing power requirements by up to 20% while maintaining sufficient bandwidth for essential applications such as audio and video streaming. In contrast, BS deactivation can lead to substantial coverage losses, with user coverage falling below 90%.

In spite of promising outcomes in optimizing energy usage for Radio Access Network (RAN) Base Station (BS) hardware, deployment, and resource management, existing methods frequently lack flexibility for scenarios involving multiple frequencies and technologies of BSs. This investigation presents a comprehensive BS switching strategy based on a threshold, tailored for real-world multi-frequency and multi-technology BSs within the RAN. The proposed approach strategically deactivates BSs using a threshold parameter that determines the maximum allowable growth in transmission power for active BSs, ensuring both coverage for users affected by BS deactivation and energy saving . Simulations conducted on a realistic multi-technology 5G New Radio (NR) RAN in an urban environment validate the efficacy of the proposed strategy, achieving up to 73% of energy saving. The study assesses the influence of the frequency order of BS deactivation and examines user re-association strategies aimed at minimizing either path loss or transmission power.

Due to the flourishing development of networks, and abetted by the Covid-19 pandemic, we have witnessed an exponential surge in the global proliferation of Real-Time Communications (RTC) applications in recent years. In light of this, the necessity for robust, scalable, and intelligent network infrastructures and technologies has become increasingly apparent. Among the principal challenges encountered in RTC lies the issue of packet loss. Indeed, the occurrence of losses leads to communication degradation and reallocation that adversely affect the Quality of Experience (QoE). In this paper, we investigate the feasibility of predicting packet loss phenomena through the utilization of machine learning techniques, solely based on statistics derived directly from packets. We provide different definitions of packet loss, subsequently focusing on the most critical scenario, which is defined as the first loss of a series. By delineating the concept of loss, we propose different problem formulations to determine whether there exists a mathematically advantageous scenario over others. To substantiate our analysis, we demonstrate that these phenomena can be correctly identified with a recall up to 66%, leveraging three ample datasets of RTC traffic, which were collected under distinct conditions at different times, further solidifying the validity of our findings.

In recent years, an exponential upsurge in the global proliferation of Real-Time Communications (RTC) applications has been witnessed, due to the prosperous development of networks and further fueled by the ramifications of the COVID-19 pandemic. Consequently, the imperative for development of intelligent, resilient, and scalable network infrastructures and technologies has grown significantly. Real-time bitrate prediction could play a crucial role, offering network observability and bolstering proactive system management. By accurately forecasting bitrate, it becomes possible to implement improvements at either application level or network level, such as swift and appropriate bandwidth adaptation. In this paper, we propose a novel Transformer-based deep learning framework called BitFormer designed to predict the short-term bitrate. Our work is based on extensive traffic data collected under various conditions using two prevalent RTC applications, and our model relies solely on packet-level information, which contains the fundamental traffic characteristics and facilitates effortless feature extraction. Through comprehensive evaluations and comparisons, we achieve a superior accuracy of 74% in identifying peak bitrates, while simultaneously ensuring commendable overall performance.

The transition towards the era of 5G and beyond is currently fostered by the extensive penetration of extremely demanding communication services, characterized by the need for exchanging increasingly larger traffic volumes with tight throughput and latency constraints. Nevertheless, the consequent massive densification of radio access networks (RANs) entails remarkable sustainability concerns, related to the staggering increase of energy demand and to the costly deployment of new infrastructure that, being dimensioned for future peak demands, may result underutilized for long periods of time. Furthermore, new potential vulnerabilities emerge that may impair the provisioning of resilient communication services. In this context, sharing network resources among different mobile operators (MOs) may play a key role to improve energy efficiency and to enhance resilience of future mobile networks. We hence investigate the potential benefits derived from the sharing of network infrastructure (primarily Base Stations with their portion of spectrum) among different MOs, comparing different areas, from a urban densely populated environment to a rural region. Based on real mobile traffic data, we design data-driven strategies to dynamically offload traffic among Base Stations owned by different MOs, allowing the switch off of unneeded resources. Our results shows that network sharing (NS) is effective in achieving huge energy saving and significant reduction of the electricity bill. Furthermore, proper configuration settings of the offloading strategies allow to trade off between sustainability goals and Quality of Service, hence enabling a feasible deployment of 5G scenarios and a sustainable evolution towards 6G.

Amidst the thriving advancement of networks, further catalyzed by the COVID-19 pandemic, we have witnessed a marked escalation in the worldwide adoption of Real-Time Communications (RTC) applications. In this context, there is a compelling necessity to cultivate intelligent and robust network infrastructures and technologies. Real-time throughput prediction emerges as a promising candidate for this purpose to foster network observability and provide preemptive functions, supporting advanced system management, e.g., bandwidth allocation and adaptive streaming. Nonetheless, contemporary solutions grapple with predicting extreme conditions in traffic throughput, notably peaks, valleys, and abrupt changes. To address the challenges, we propose a Transformer-based Deep Learning (DL) Neural Network (NN), leveraging solely packet-level information and adopting a multi-task learning paradigm, to predict short-term throughput, with an emphasis on critical values. In particular, our work is grounded in voluminous traffic traces procured from real video-teleconferencing sessions, and we formulate a time-series regression problem, comparing numerous technologies, from an adaptive filter to Machine Learning (ML) and DL approaches. Conclusively, our methodology exhibits superior efficacy, especially in forecasting traffic extremities.

The Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, exemplified by video conferencing, have experienced an unparalleled surge in popularity and development in recent years. In pursuit of optimizing their performance, the prediction of Quality of Service (QoS) metrics emerges as a pivotal endeavor, bolstering network monitoring and proactive solutions. However, contemporary approaches are confined to individual RTP flows and metrics, falling short in relationship capture and computational efficiency. To this end, we propose Packet-to-Prediction (P2P), a novel deep learning (DL) framework that hinges on raw packets to simultaneously process concurrent RTP flows and perform end-to-end prediction of multiple QoS metrics. Specifically, we implement a streamlined architecture, namely length-free Transformer with cross and neighbourhood attention, capable of handling an unlimited number of RTP flows, and employ a multi-task learning paradigm to forecast four key metrics in a single shot. Our work is based on extensive traffic collected during real video calls, and conclusively, P2P excels comparative models in both prediction performance and temporal efficiency.

Packet loss, an omnipresent issue that degrades the QoE in Real-time Transport Protocol (RTP)-based real-time communications (RTC) applications, serves as a pivotal indicator for gauging network performance. Conventionally, loss detection hinges on sequence number irregularities. However, many contemporary applications incorporate customized mechanisms that diverge from the standard, confounding loss identification. Although the actual losses are transparent to applications themselves, they remain unobservable to other entities such as network operators, hampering the prospect of overall network management and performance optimization. To address this challenge, we investigate multitudinous RTC traffic gathered across various locations and times. Consequently, we uncover two types of anomalous patterns pertaining to sequence numbers. To discern between factual losses and aberrations in RTP flows, i.e., to detect the unobservable losses, we curate three distinct datasets, aggregating packets into time bins and calculating multiple traffic statistics. Subsequently, we leverage Machine Learning (ML) technologies, training the algorithm on one dataset while testing the remaining two, to classify the loss presence in a bin. Despite the inherent hurdles posed by class imbalance and intricate traffic dynamics, we achieve decent outcomes (0.64 Fl-score), effectively identifying the majority of lossy bins (0.64 recall) while guaranteeing the performance for lossless scenarios (0.94 recall).

The rapidly evolving communication landscape, with the advent of 6G technology, brings new challenges to the design and operation of wireless networks. One of the key concerns is the energy efficiency of the Radio Access Network (RAN), as the exponential growth in wireless traffic demands increasingly higher energy consumption. In this paper, we assess the potential of integrating a High Altitude Platform Station (HAPS) to improve the energy efficiency of a RAN, and quantify the potential energy conservation through meticulously designed simulations. We propose a quantitative framework based on real traffic patterns to estimate the energy consumption of the HAPS-integrated RAN and compare it with the conventional terrestrial RAN. Our simulation results elucidate that HAPS can significantly reduce energy consumption by up to almost 30% by exploiting the unique advantages of HAPS, such as its self-sustainability, high altitude, and wide coverage. We further analyze the impact of different system parameters on performance, and provide insights for the design and optimization of future 6G networks. Our work sheds light on the potential of HAPS-integrated RAN to mitigate the energy challenges in the 6G era, and contributes to the sustainable development of wireless communications.

The increasing adoption of Deep Learning (DL) algorithms for time series forecast has led to a significant environmental concern due to the high computational demands and associated carbon footprint. This study investigates the environmental impact of DL models, particularly Long Short-Term Memory (LSTM) networks, for time series forecasting tasks where frequent retraining of models is essential. We conduct an empirical analysis of carbon emissions produced by LSTM models trained on two distinct time series datasets. By systematically varying model hyperparameters (epochs, train-test split, number of layers and neurons per layer), and by reducing the number of models or the number of input features, we aim to understand the impact of these changes on carbon emissions and model accuracy. Our contributions include a comprehensive analysis of carbon emissions during model training and the identification of possible tradeoffs between emissions and accuracy. The findings indicate that strategic adjustments can significantly reduce environmental impact while maintaining satisfactory accuracy levels.

We consider an access link carrying data generated by streaming and elastic services requested by fixed or mobile end users, and subjected to an admission control (AC) algorithm. For the performance analysis of such link we develop a new queuing model and we show that, with the considered AC, the queuing model admits a product form expression for the joint limiting probability distribution of the numbers of active services of the different types. In addition, we prove that, when mobility can be neglected, i.e., in the case of either fixed access or slow mobility, the queuing model is insensitive to the distribution of the amount of data to be transferred for the fulfilment of the different service requests. Numerical results show unexpected oscillating behaviors for several performance metrics, and provide interesting insight into the link performance.

Recent years have witnessed a remarkable upsurge in the global proliferation of Real-Time Communications (RTC) applications, a trend propelled by the flourishing advancement of network technologies and further amplified by the COVID-19 pandemic. Within this context, there is a burgeoning interest in the innovation of sophisticated and intelligent network infrastructures and technologies. Positioned as a promising candidate for this purpose, real-time throughput prediction emerges as a key enabler to foster network observability and offer proactive functions, upholding advanced system management, including but not limited to, bandwidth allocation and adaptive streaming. Nonetheless, existing methodologies struggle with predicting extreme conditions of throughput, notably peaks, valleys, and abrupt changes, that are critical in RTC traffic. To surmount these obstacles, we introduce DeX, a Deep Learning (DL)-based framework, designed to predict short-term throughput, with a dexterous proficiency and dedicated focus on navigating the complexities of traffic eXtremes. In particular, DeX leverages solely packet-level information as features and is composed of three integral components: a packet selection module that opts for an optimal subset of input features, a feature extraction block that partially incorporates the Transformer architecture, and a multi-task learning pipeline that improves the proficiency in handling traffic extremes. Moreover, our work is anchored in extensive traffic traces garnered during actual video-teleconferencing calls, and we formulate a time-series regression problem, rigorously evaluating a spectrum of technologies ranging from an adaptive filter to diverse Machine Learning (ML) and DL approaches. Initially, we aim at predicting throughput within 500-ms time windows using historical 1024 packets out of 2048, and consequently, our methodology exhibits exceptional efficacy, especially in forecasting traffic extremities. Conclusively, we conduct a series of ablation experiments and thorough analyses to showcase the enhanced performance of various scenarios, further validating the effectiveness and robustness of DeX.

Real-time communication (RTC) platforms have seen a considerable surge in popularity in recent years, largely due to the COVID-19 pandemic which facilitated remote work. To ensure adequate Quality of Experience (QoE) for users, a good congestion control algorithm is needed. RTC applications use UDP, so congestion control is done on the application layer, leaving way for advanced algorithms. In this paper, we propose ReCoCo, a solution for congestion control in RTC applications based on Reinforcement learning (RL). ReCoCo gains information about the network conditions at the receiver-side, such as receiving rate, one-way delay and loss ratio and predicts the available bandwidth in the next time bin. We train ReCoCo on 9 bandwidth trace files that cover a vast array of network types. We try different algorithms, states and parameters, training both specific and general models. We find that ReCoCo outperforms the de-facto standard heuristic algorithm GCC in both specialized and general models. We also make observations on the difficulty of generalization when using RL.

Real-time communication (RTC) platforms have undergone a consistent increase in popularity in recent years, and nowadays, they are fundamental for both work and leisure purposes. To ensure adequate Quality of Experience (QoE) for users of RTC services, we need proper traffic management policies, that, when critical network conditions are detected, react by operating either at the network configuration level or on the application to improve QoE. However, predicting critical network conditions, especially packet losses that are particularly harmful to QoE, is a very challenging task. In this paper, we propose a system for predicting packet losses that might occur in the near future (i.e., in a second) for RTP streaming traffic. We analyze several ML algorithms, from standard techniques to deep neural networks and anomaly detection algorithms, and we apply them to more than 66 hours of data from two popular RTC applications. The selection of the algorithm and its tuning turn out to be fundamental to achieving good performance. In one of the best settings, which are based on a Balanced Random Forest classifier, we obtain a recall of 0.82.

The emergence of Power Line Communication (PLC) technology has facilitated the expansion of broadband access networks in remote areas, by utilizing existing wired power infrastructure. However, the growing demand for data, driven by the popularity of communication services, presents a formidable challenge to the underlying PLC technology. Collaborative caching involves the sharing of cached content among neighboring nodes, thereby improving cache hit ratio (CHR), reducing network and backhaul congestion, and ultimately enhancing network performance. Our research proposes a recommendation system integrated into the collaborative caching mechanism on a PLC network that suggests relevant content to the users based on users' preferences and historical usage patterns leading to an increase in CHR and a reduction in network congestion. The results indicate that the proposed system significantly improves network performance by reducing download delay and saving precious backhaul link resources thus making PLC networks more effective for remote areas.

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.

The extensive densification of mobile networks is increasing the network energy consumption and leading to remarkable economical and sustainability concerns. At the same time, regulatory and physical constraints, especially in urban environments, may limit the network expansion and the free installation of Base Stations (BSs). In this context, High Altitude Platform Stations (HAPSs) are emerging as a promising solution to host aerial BSs that can provide additional capacity over a wide geographical area, to offload the on-ground mobile network and support a sustainable transition towards the 6G era. This paper investigates the potential of HAPS offloading to reduce the energy demand from the grid and the operational cost of mobile networks. Our results highlight the effectiveness of HAPS offloading in reducing the size of the RE supply that is required to achieve grid energy reduction on the terrestrial network, thus enhancing the feasibility of a sustainable evolution towards 6G networks. Different allocation strategies are designed and analyzed under several configuration settings, to dynamically adapt the HAPS capacity to the traffic variability in space and over time. A fine tuning of the strategy settings is proved effective in trading off physical constraints, operational cost, sustainability goals, and Quality of Service.

No abstract available.

Due to the evolution in communications technologies and antennas, as well as advances in solar panel efficiency, High Altitude Platforms (HAPS) have been recently considered as a promising aerial network component, to support Radio Access Networks (RANs). Through their directional antenna they can activate beams and provide coverage to up to 1.5 km radius ground area. In this work, we consider a HAPS equipped with a Multi Access Edge Computing (MEC) server, which provides caching capabilities. The HAPS is used to off-load content requests. We analyse an urban environment scenario, as well as the effects of the simultaneous activation of beams in different areas. Results demonstrate that the HAPS is a suitable solution to bring additional capacity to the RAN and highlight that the provided performance strictly depends on the traffic demand profile of the covered portion of RAN.

We consider an access link of a data network loaded with data flows generated by streaming and elastic services requested by fixed or mobile end users, and subjected to an admission control (AC) algorithm. For the performance analysis of such link we develop a new queuing model and we show that, with the considered AC, the queuing model admits a product form expression for the joint limiting probability distribution of the numbers of active services of the different types. Numerical results show unexpected oscillating behaviors for several performance metrics, and provide interesting insight into the link performance.

Real-time communication (RTC) applications have become largely popular in the last decade with the spread of broadband and mobile Internet access. Nowadays, these platforms are a fundamental means for connecting people and supporting businesses that increasingly rely on forms of remote work. In this context, it is of paramount importance to operate at the network level to ensure adequate Quality of Experience (QoE) for users, and appropriate traffic management policies are essential to prioritize RTC traffic. This in turn requires the network to be able to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify media streams generated by RTC applications encapsulated in Secure Real-Time Protocol (SRTP) flows in real-time. Using carefully tuned features extracted from packet characteristics, we train models to classify streams into a variety of classes, including media type (audio/video), video quality, and redundant streams. We validate our approach using traffic from over 62 hours of multi-party meetings conducted using two popular RTC applications, namely Cisco Webex Teams and Jitsi Meet. We achieve an overall accuracy of 96% for Webex and 95% for Jitsi, using a lightweight decision tree model that makes decisions based solely on 1 second of real-time traffic. Our results show that models trained for a particular meeting software have difficulty when used with another one, although domain adaptation techniques facilitate the transfer of pre-trained models.

Real-time communication (RTC) platforms have become increasingly popular in the last decade, together with the spread of broadband Internet access. They are nowadays a fundamental means for connecting people and supporting the economy, which relies more and more on forms of remote working. In this context, it is particularly important to act at the network level to ensure adequate Quality of Experience (QoE) to users, where proper traffic management policies are essential to prioritize RTC traffic. This, in turn, requires in-network devices to identify RTC streams and the type of content they carry. In this paper, we propose a machine learning-based application to classify, in real-time, the media streams generated by RTC applications encapsulated in Secure Real Time Protocol (SRTP) flows. Using carefully tuned features extracted from packet characteristics, we train a model to classify streams into an ample set of classes, including media type (audio/video), video quality and redundant streams. To validate our approach, we use traffic from more than 88 hours of multi-party meeting calls made using the Cisco Webex Teams application. We reach an overall accuracy of 97% with a light-weight decision tree model, which makes decisions using only 1 second of traffic.

In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks.

The staggering growth of mobile traffic fostered by the extensive spreading of 5G technology and massive Internet of Things (IoT) applications is leading to network densification, entailing a boost in network power consumption, with consequent higher operational cost for Mobile Network Operators (MNOs) and raising sustainability issues. To reduce energy consumption when the traffic is low, new BSs feature Advanced Sleep Modes (ASM) that allow to reduce the network energy consumption by gradually deactivating the BSs into progressively deeper sleep modes with lower power consumption. However, the deep sleep modes cause high reactivation delays that may jeopardize the Quality of Service.In this paper, focusing on the periods in which traffic is very low, we extensively investigate the potentiality of ASMs based operation in terms of the trade-off between energy saving and delay under different 5G scenarios and traffic loads. By observing that optimal configuration settings depend on the scenario and on the load, we design a framework based on a stochastic model to perform dynamic tuning of the configuration settings that adapts in real time the parameters to the actual traffic load and scenario.

In this paper we consider radio access network configurations comprising cells of different size (one macro cell and some small cells). Cells have overlapping coverage, and the corresponding base stations offer both elastic and inelastic services. The considered setting is modeled as a queuing network, and we examine the system performance for variable parameter values, showing that some of the emerging behaviors can be unexpected, and providing insight into the effective deployment of small cells within the coverage of one macro cell. In particular, we see that a large fraction of elastic traffic, together with an area of the small cell corresponding to a significant portion of the macro cell area are important aspects for the effective exploitation of the small cell capacity. These behaviors can significantly impact the deployment of small cells, which are expected to become increasingly popular because of the need to provide additional capacity through densification of the cell layout.

Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raises also concerns in terms of sustainability. In this context, renewable energy generators can help to both reduce costs and alleviate the concerns of sustainability of big infrastructures. In this paper, we consider the case of Data Centers (DCs) composed of a few sites located in different geographical positions and powered with solar energy. Due to the intermittent nature of solar energy, different time zones and price of electricity in different locations, load management strategies are fundamental. We consider predictions of the solar energy production performed through Artificial Neural Networks and we assess the impact of predictions on load management decisions and, ultimately, on the DC performance.

In the next generation of Radio Access Networks (RANs), Multi-access Edge Computing (MEC) is considered a promising solution to reduce the latency and the traffic load of backhaul links. It consists of the placement of servers, which provide computing platforms and storage, directly at each Base Station (BS) of these networks. In this paper, the caching feature of this paradigm is considered in a portion of a RAN, powered by a renewable energy generator system, energy batteries and the power grid. The performance of the caching in the RAN is analysed for different traffic characteristics, as well as for different capacity of the caches and different spread of it. Finally, we verify that the usage of a strategy that aims at reducing the energy consumption does not impact the benefits provided by the mobile edge caching.

Real-Time Communication (RTC) applications have become ubiquitous and are nowadays fundamental for people to communicate with friends and relatives, as well as for enterprises to allow remote working and save travel costs. Countless competing platforms differ in the ease of use, features they implement, supported user equipment and targeted audience (consumer of business). However, there is no standard protocol or interoperability mechanism. This picture complicates the traffic management, making it hard to isolate RTC traffic for prioritization or obstruction. Moreover, undocumented operation could result in the traffic being blocked at firewalls or middleboxes. In this paper, we analyze 13 popular RTC applications, from widespread consumer apps, like Skype and Whatsapp, to business platforms dedicated to enterprises - Microsoft Teams and Webex Teams. We collect packet traces under different conditions and illustrate similarities and differences in their use of the network. We find that most applications employ the well-known RTP protocol, but we observe a few cases of different (and even undocumented) approaches. The majority of applications allow peer-to-peer communication during calls with only two participants. Six of them send redundant data for Forward Error Correction or encode the user video at different bitrates. In addition, we notice that many of them are easy to identify by looking at the destination servers or the domain names resolved via DNS. The packet traces we collected, along with the metadata we extract, are made available to the community.

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed.

The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

This paper studies the performance gains resulting from the sharing of energy and network resources in the case of co-located base stations of different mobile network operators, powered by photovoltaic panels, and equipped with energy storage. Three configurations are considered for base station cooperation. The first one assumes two non-cooperating base stations, each one exploiting its own power system and serving its own customers, hence with no sharing. The second considers a shared power system, but no cooperation in customer service. The third looks at cooperation in both energy production and service provisioning, since only one base station handles all customers when traffic is low. Using an analytical modeling framework, we compute performance metrics for the three cases, and we show that significant gains are possible in the case of energy and network sharing.

In the last years, Green Mobile Networks that are powered with renewable energy sources have been designed and deployed with the twofold objective of reducing operational costs and providing service in scenarios in which the power grid is not reliable. At the same time, the introduction of Smart Grids is deeply changing the energy market, by effect of the grid actively interacting with its customers. In this paper, we consider a scenario in which a green mobile network is integrated in a smart grid. The mobile network interacts with the smart grid, responding to its requests by adapting its load. Load adaptation is obtained by resource on demand strategies that operate on Base Stations, and by taking decisions about the use of the renewable energy that is locally produced and that can be used for powering the green mobile network, it can be stored or even returned to the grid. The results, derived through a Markovian model, show that the use of resource on demand strategies in the green mobile network improves the interaction between the network and the smart grid: significant cost gain can be achieved, the responsiveness to the smart grid requests increases, low storage probability decreases.

A considerable percentage of worldwide electrical energy is consumed by information and communication technology. One significant element in this perspective are the data distribution systems via Content Delivery Networks (CDNs). We introduce a new model to compute the total energy consumption of CDNs which is based on a hierarchical Internet map and that takes into account the energy consumption needed to keep servers synchronized. The CDN is represented as a main server storing the whole data set and several surrogate servers, each caching a subset of the entire data set. Servers are located in a hierarchical three-tier network topology. We analyze the energy consumption trends as a function of the number of surrogate server. Results show that increasing the number of surrogate servers decreases the transmission delay but it does not always lead to decreasing energy consumption. Furthermore, the energy consumption profile as a function of the number of servers strongly depends on the ratio between the number of content requests and modifications. Finally, we show that the adoption of a hierarchical network model permits to highlight slightly different energy consumption trends with respect to those of standard “flat” network representation.

We consider a solar power supply for a LTE macro base station (BS) based on a photovoltaic (PV) panel and a battery, and we develop two discrete-time Markov chain (DTMC) models for the analysis and the dimensioning of the system elements (PV panel size and battery capacity). The DTMC models account for the solar irradiance levels in pairs or triples of consecutive days, and for the quantity of energy stored in the battery. From the DTMC steady-state (or transient) solution it is possible to derive performance metrics on which the system dimensioning can be based. We apply our models to BS locations in southern and northern Italy. Results show that the simpler model contains sufficient details for an effective system design.

In this study, the potential of applying different strategies to reduce the energy consumption of a wireless access network, powered by a photovoltaic panel system, during energy shortages is investigated. The goal is to reduce the amount of energy that should be bought from the traditional energy grid during a renewable energy shortage. Three different strategies are compared and the results are looking very promising. Depending on the strategy, up to 72% less energy should be bought compared to the fully operational network for a worst case scenario and a time period of 1 week. However, applying such a strategy has also its influence on the network performance. The influence on the user coverage is limited, with a reduction of 3% at maximum, but the capacity offered by the network decreases significantly with 51% up to even 71%.

Base station sharing is currently considered one of the most promising solutions for reducing the energy consumption costs of cellular networks. This paper presents a game theoretic framework for the study of such cooperative solutions where different mobile network operators (MNOs) decide to switch off subsets of their base stations during off-peak hours and roam their traffic to the remaining stations. The solution is based on a detailed optimization framework that determines exactly which base stations should remain active and how much traffic each one of them should serve, so as to maximize the aggregate energy savings. Accordingly, using the axiomatic Shapley value rule, it is determined how the benefits from the cooperation, i.e., the cost savings, should be dispersed among the cooperating MNOs. It is proved that this coalitional game with transferrable utilities has a nonempty core, and thus there exists a cooperation solution that incentivizes the participation of all operators. Moreover, using a thorough numerical analysis, it is shown that the benefits achieved with the implementation of the cooperation strategy depend mainly on the power consumption characteristics of the MNOs, which in turn are related to the number, type, and technology of their base stations. Overall, the energy savings are found to be most sensitive to the technology of the used base stations, and more precisely to the no-load base station energy consumption which defines the energy waste in a network.

The use of solar energy to power base stations of cellular networks is becoming increasingly interesting, in both areas where the power grid is not present or not reliable, and where the power grid is ubiquitous and reliable, but energy costs keep growing. In this paper, we investigate the dimensioning of the photovoltaic panel and energy storage of a hybrid base station powering system that can exploit both solar and grid energy. The objective of the dimensioning is the minimization of the total capital and operational expenditures over a period of 10 years, accounting for the evolution of technology and traffic load. Results show that in a south European city like Torino, a hybrid base station powering system allows significant cost and size reductions, with respect to the case of solar energy only (and of a diesel power generator), and roughly equals the cost of the grid-only case in 8–9 years. When the extra energy produced by the solar panel can be sold back to the grid, the hybrid systems allow significant savings with respect to the grid-only case. For the city of Aswan, with a production that is much higher than in Torino and more constant over the year, costs of pure solar and hybrid systems are significantly lower in absolute terms; hybrid systems result to be still advantageous with respect to pure solar systems.

Being cheap and easy to deploy, dense WLANs are becoming the most popular solution to providing Internet access in locations where the population of users is large, such as on campuses, large enterprises, etc. The large density of access points (APs) comes from the need to have enough capacity to carry the traffic generated at peak hours although, in these scenarios, traffic varies a lot on a daily, weekly, or seasonal basis. During low or no traffic periods, APs are underutilized, even if they are consuming energy almost in the same amount as if they were fully loaded. Promising solutions to reducing this form of energy waste consist of activating only the number of APs that is strictly needed to carry the actual traffic; in other words, to make capacity dynamically adaptive through resource-on-demand (RoD) strategies. In this paper, we investigate the case of a portion of the dense WLAN on our campus. Through real trace analysis, we investigate users' behavior in accessing the WLAN and formulate a stochastic characterization of it. We propose a simple model that describes RoD strategies and use it to study the system performance that is evaluated in terms of AP activity and inactivity periods, AP switching frequency, and energy saving. Finally, we present some results obtained by experimenting with RoD strategies in a portion of the WLAN. Our results show that RoD strategies for dense WLANs are feasible and effective in trading-off the opposite needs to save some energy and to guarantee a smooth network operation and high quality of service.

In this paper we investigate the potential energy saving inherent in the network sharing approach, whereby all (or significant parts) of the network infrastructures existing in a country can be shared by different network operators. In our study we consider European mobile network operators, and we use simple analytical models to show that in most European countries the amount of energy necessary to run mobile networks can be reduced by 35 to 60% with respect to the case in which each operator manages a separate network infrastructure.

Power efficiency is one of the main issues that will drive the design of data centers, especially of those devoted to provide Cloud computing services. In virtualized data centers, consolidation of Virtual Machines (VMs) on the minimum number of physical servers has been recognized as a very efficient approach, as this allows unloaded servers to be switched off or used to accommodate more load, which is clearly a cheaper alternative to buy more resources. The consolidation problem must be solved on multiple dimensions, since in modern data centers CPU is not the only critical resource: depending on the characteristics of the workload other resources, for example, RAM and bandwidth, can become the bottleneck. The problem is so complex that centralized and deterministic solutions are practically useless in large data centers with hundreds or thousands of servers. This paper presents ecoCloud, a self-organizing and adaptive approach for the consolidation of VMs on two resources, namely CPU and RAM. Decisions on the assignment and migration of VMs are driven by probabilistic processes and are based exclusively on local information, which makes the approach very simple to implement. Both a fluid-like mathematical model and experiments on a real data center show that the approach rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are balanced, so that both resources are exploited efficiently.

Points of Presence (PoP), large aggregation nodes of a telecommunication network in which users lines are interconnected to the ISP backbone network, are relevant elements of the ISP network infrastructure. Motivated by the today interest of both ISPs and researchers to more energy efficient Internet, we investigate the power consumption of PoPs of FASTWEB, a national-wide ISP in Italy. Energy profiling spans a period of one year, and includes both ADSL and FTTH access technologies. This extensive and unique dataset allows us to shed light on energy consumption of ISP networks, which we profile against other measurements, such as external temperature and PoP handled traffic. Results show that energy consumption is independent on the traffic, while it is strongly correlated with both daily and annual variability of temperature, due to air conditioning energy cost. Starting from these results, we investigate some possible strategies to reduce ISP electricity bill. We consider the adoption of energy proportional architectures which are currently being investigated by both manufacturers and researchers. Moreover, we evaluate the possible energy savings using real traffic data and we obtain that simple PoPs energy saving models based on two-three energy operating configuration can achieve results comparable to fully energy proportional model.

This article presents an ant-based approach that enhances the flexibility, robustness and load balancing characteristics of structured P2P systems. Most notably, the approach allows peer indexes and resource keys to be defined on different and independent spaces, so that it overcomes the main limitation of standard structured P2P systems, that is, the need to assign each key to a peer having a specified index. This helps to improve load balancing, especially when the popularity distribution of resource keys is nonuniform, and enables the efficient execution of complex and range queries, which are essential in important types of distributed systems, for example, in Grids and Clouds. Beyond describing the general approach, this article focuses on the specific case of Self-CAN, a self-organizing P2P system that, while relying on the multidimensional structured organization of peers provided by CAN, exploits the operations of ant-based mobile agents to sort the resource keys and distribute them to peers. This system is particularly useful for the management and discovery of the resources that can be conveniently characterized by the values of several independent attributes.

In the recent years, the research community has increased its focus on network monitoring which is seen as a key tool to understand the Internet and the Internet users. Several studies have presented a deep characterization of a particular application, or a particular network, considering the point of view of either the ISP, or the Internet user. In this paper, we take a different perspective. We focus on three European countries where we have been collecting traffic for more than a year and a half through 5 vantage points with different access technologies. This humongous amount of information allows us not only to provide precise, multiple, and quantitative measurements of "What the user do with the Internet" in each country but also to identify common/uncommon patterns and habits across different countries and nations. Considering different time scales, we start presenting the trend of application popularity; then we focus our attention to a one-month long period, and further drill into a typical daily characterization of users activity. Results depict an evolving scenario due to the consolidation of new services as Video Streaming and File Hosting and to the adoption of new P2P technologies. Despite the heterogeneity of the users, some common tendencies emerge that can be leveraged by the ISPs to improve their service.

In this paper we present an extensive analysis of traffic generated by SopCast users and collected from operative networks of three national ISPs in Europe. After more than a year of continuous monitoring, we present results about the popularity of SopCast which is the largely preferred application in the studied networks. We focus on analysis of (i) application and bandwidth usage at different time scales, (ii) peer lifetime, arrival and departure processes, (iii) peer localization in the world. Results provide useful insights into users' behavior, including their attitude towards P2P-TV application usage and the consequent generated load on the network, that is quite variable based on the access technology and geographical location. Our findings are interesting to Researchers interested in the investigation of users' attitude towards P2P-TV services, to foresee new trends in the future usage of the Internet, and to augment the design of their application.

In this paper we consider mesh based P2P streaming systems focusing on the problem of regulating peer upload rate to match the system demand while not overloading each peer upload link capacity. We propose Hose Rate Control (HRC), a novel scheme to control the speed at which peers offer chunks to other peers, ultimately controlling peer uplink capacity utilization. This is of critical importance for heterogeneous scenarios like the one faced in the Internet, where peer upload capacity is unknown and varies widely. HRC nicely adapts to the actual peer available upload bandwidth and system demand, so that users' Quality of Experience is greatly enhanced. Both simulations and actual experiments involving up to 1000 peers are presented to assess performance in real scenarios. Results show that HRC consistently outperforms the Quality of Experience achieved by non-adaptive schemes.

Several successful commercial P2P-TV applications are already available. Unfortunately, some algorithms and protocols they adopt are unknown, since many follow a closed and proprietary design. This calls for tools and methodologies that allow the investigation of the application behavior. In this paper, we present a novel approach to analyze the graph properties and the traffic generated by P2P-TV applications run by customers in operative networks. The proposed methodology allows us to distinguish and investigate three different graphs: (i) the social networks that link users based on their interest, (ii) the overlay networks created by peers that are watching the same channel, and (iii) the distribution networks that involve the subset of peers that are contributing to the video distribution. We apply this methodology to the traffic collected for more than one year from three national ISPs in Europe, where SopCast is the largely preferred application. Considering users' behavior, we uncover the attitude to use the P2P-TV application mainly to follow live sport events. P2P-TV systems have then to deal with both flash crowd and sudden peer departures that happen at the beginning and end of an event. Furthermore, zapping among channels offering the same event is also relevant. SopCast deals with this by implementing a very robust and greedy overlay topology discovery process in which more than 170 peers are contacted every 60 s. Considering video distribution, we provide evidence that SopCast implements algorithms that restrict traffic within Autonomous System boundaries. Still, high bandwidth peers must be present to supply the necessary upload capacity to sustain the video service.

Abstract We investigate the broadcast problem in suburban and highway inter‐vehicular networks, aiming at providing a definitive comparison of two antipodean broadcast algorithm classes: the first one makes use of someinstantaneous informationlocally available at the vehicles (such as vehicle position and speed), while the second one relies onlong‐term knowledgegained through a beaconing procedure. Using a realistic microscopic model to represent the vehicular traffic flow, we investigate the performance of the above broadcast algorithm classes by simulation, considering different classes of network services (e.g., Critical, Normal, and Low‐priority). In order to explore a very large algorithmic design space, we devise a convex hull framework that allows us to effectively compare and compactly present the boundaries of the solution space for each algorithm class. By the use of such framework, we show that the beaconless performance encompasses a wider spectrum with respect to the beaconed one, with lower complexity and overhead. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P live streaming systems. Indeed, by effectively exploiting it, the overall performance of the system is significantly improved. This requires the adoption of smart schemes for both the overlay topology construction and chunk scheduling mechanisms that discriminate among peers based on their bandwidth.

We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the system performance. In particular, we show that the adoption of different strategies leads to overlay topologies with different macroscopic properties. Representing the possible overlay topologies with different families of random graphs, we develop simple, yet accurate, fluid models that capture the dominant dynamics of the chunk distribution process over several families of random graphs. Our fluid models allow us to compare the performance of different strategies providing a guidance for the design of new and more efficient systems. In particular, we show that system performance can be significantly improved when possibly available information about peers location and/or peer access bandwidth is carefully exploited in the overlay topology formation process.

Peer-to-Peer streaming systems (or P2P-TV) have been studied in the literature for some time, and they are becoming popular among users as well. P2P- TV systems target the real time delivery of a video stream, therefore posing different challenges compared to more traditional P2P applications like the better known file sharing P2P application. In this paper, we focus on mesh based systems in which the peers form a generic overlay topology upon which peers exchange small "chunks" of video. In particular, we study the signaling mechanisms that must be in place to trade chunks and to match the demand from other peers in a quick and efficient way, by automatically adapting a peer service rate to its upload capacity. The goal is to maximize peer upload capacity utilization, while avoiding forming long transmission queue, therefore minimizing the chunk delivery time, a crucial parameter for P2P-TV systems. Our results show that the proposed solution achieves several desirable goals: i) it limits the overhead due to signaling messages, ii) it achieves a fair resource utilization, making peers contribute proportionally to their bandwidth, iii) it improves system performance, reducing loss probability and chunk delivery delay with respect to mechanisms with non adaptive number of contacted peers.

The increasing success of P2P-TV applications, that may overwhelm the network with their large volume of traffic in the near future, calls for the need of new traffic models that can effectively represent the traffic generated by these applications. In this paper, we study the traffic generated by PPLive and SopCast, that are among the most popular P2P-TV applications of today, and propose Hidden-Markov chains for modeling the traffic they generate. Our results show that the models are quite accurate and can be effectively used in many networking tasks such as network performance analysis, network planning and dimensioning, traffic engineering.

In this paper, we extend the concept of Stochastic Packet Inspection (SPI) to support TCP traffic classification. SPI is a method based on the statistical fingerprint of the application-layer headers: by characterizing the frequencies of observed symbols, SPI can identify application protocol formats by automatically recognizing group of bits that take e.g., constant values, or random values, or are part of a counter. To correctly characterize symbol frequencies, SPI needs volumes of traffic to obtain statistically significant signatures. Earlier proposed for UDP traffic, SPI has to be modified to cope with the connection oriented service offered by TCP, in which application-layer headers are only found at the beginning of a TCP connection. In this paper, we extend SPI to support TCP traffic, and analyze its performance on real network data. The key idea is to move the classification target from single flows to endpoints, which aggregates all traffic sent/received by the same IP address and TCP port pair. The first few packets of flows sent from (or destined to) the same endpoint are then aggregated to yield a single SPI signature. Results show that SPI is able to achieve remarkably good results, with an average true positive rate of about 98%.

This paper presents a systematic performance analysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: (1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; (2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; (3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support.

This paper proposes KISS, a novel Internet classification engine. Motivated by the expected raise of UDP traffic, which stems from the momentum of Peer-to-Peer (P2P) streaming applications, we propose a novel classification framework that leverages on statistical characterization of payload. Statistical signatures are derived by the means of a Chi-Square (χ2)-like test, which extracts the protocol “format,” but ignores the protocol “semantic” and “synchronization” rules. The signatures feed a decision process based either on the geometric distance among samples, or on Support Vector Machines. KISS is very accurate, and its signatures are intrinsically robust to packet sampling, reordering, and flow asymmetry, so that it can be used on almost any network. KISS is tested in different scenarios, considering traditional client-server protocols, VoIP, and both traditional and new P2P Internet applications. Results are astonishing. The average True Positive percentage is 99.6%, with the worst case equal to 98.1,% while results are almost perfect when dealing with new P2P streaming applications.

This paper presents “Self-Chord,” a peer-to-peer (P2P) system that inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomous behavior, self-organization, and capacity to adapt to a changing environment. As opposed to the structured P2P systems deployed so far, resource indexing and placement is uncorrelated with network structure and topology, and resource keys are organized and managed by self-organizing mobile agents through simple local operations driven by probabilistic choices. Self-Chord has three main features that are particularly advantageous in Grid and Cloud Computing: 1) it is possible to give a semantic meaning to keys, which enables the execution of range queries; 2) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; 3) maintenance load is also limited because it is not necessary to reassign keys when new peers or resources are added to the system-the mobile agents will spontaneously reorganize the keys. The efficiency and effectiveness of Self-Chord were assessed both with a simulation framework and with an analytical model inspired by fluid dynamics.

This paper presents ldquoSelf-Chordrdquo, a bio-inspired P2P algorithm that can be profitably adopted to build the information service of distributed systems, in particular Computational Grids and Clouds. Self-Chord inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from the activity of ant-inspired mobile agents, such as autonomy behavior, self-organization and capacity to adapt to a changing environment. Self-Chord features three main benefits with respect to classical P2P structured systems: (i) it is possible to give a semantic meaning to keys, which enables the execution of "class" queries, often issued in Grids and Clouds; (ii) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; (iii) maintenance load is reduced because, as new peers join the ring, the mobile agents will spontaneously reorganize the keys in logarithmic time.

In this paper we consider IPTV systems with an hierarchical architecture. The lowest elements of the architecture are set-top boxes (STBs) at the user homes; a STB is connected to a central office (CO) which is in charge of delivering the video content to the end user. Since COs have limited storage capabilities, they may need to retrieve a particular video content that is requested by a user but temporary not stored in the local memory. Thus, COs exchange video contents with similar COs in a peer-to-peer fashion. At a higher hierarchical level, video source offices (VSOs) offer the video contents that cannot be retrieved at the CO level. Video content caching strategies at the COs and VSOs influence the system performance in terms of traffic exchanged between the network nodes. In this paper we propose two simple strategies that aim at reducing both the intra- and inter-level traffic. The strategies are studied by means of an analytical model that is validated against simulation results. Our results show that the hierarchical architecture allow good system performance even with limited overall storage capacity. The proposed strategies, in particular, can be helpful in improving system performance.

Skype is one of the most successful VoIP application in the current Internet spectrum. One of the most peculiar characteristics of Skype is that it relies on a P2P infrastructure for the exchange of signaling information amongst active peers. During August 2007, an unexpected outage hit the Skype overlay, yielding to a service blackout that lasted for more than two days: this paper aims at throwing light to this event. Leveraging on the use of an accurate Skype classification engine, we carry on an experimental study of Skype signaling during the outage. In particular, we focus on the signaling traffic before, during and after the outage, in the attempt to quantify interesting properties of the event. While it is very difficult to gather clear insights concerning the root causes of the breakdown itself, the collected measurement allow nevertheless to quantify several interesting aspects of the outage: for instance, measurements show that the outage caused, on average, a 3-fold increase of signaling traffic and a 10-fold increase of number of contacted peers, topping to more than 11 million connections for the most active node in our network - which immediately gives the feeling of the extent of the phenomenon.

In this paper we define a simple experimental setup to analyze the behavior of commercial P2P-TV applications under adverse network conditions. Our goal is to reveal the ability of different P2P-TV applications to adapt to dynamically changing conditions, such as delay, loss and available capacity, e.g., checking whether such systems implement some form of congestion control. We apply our methodology to four popular commercial P2P-TV applications: PPLive, SOPCast, TVants and TVUPlayer. Our results show that all the considered applications are in general capable to cope with packet losses and to react to congestion arising in the network core. Indeed, all applications keep trying to download data by avoiding bad paths and carefully selecting good peers. However, when the bottleneck affects all peers, e.g., it is at the access link, their behavior results rather aggressive, and potentially harmful for both other applications and the network.

In this paper, we propose a simple and fully distributed mechanism for constructing and maintaining the overlay topology in mesh-based P2P-TV systems. Our algorithm optimizes the topology to better exploit large bandwidth peers, so that they are automatically moved close to the source. This improves the chunk delivery delay so that all peers benefit, not just the high bandwidth ones. A key property of the proposed scheme is its ability to indirectly estimate the upload bandwidth of peers without explicitly knowing or measuring it. Simulation results show that our scheme significantly outperforms overlays with homogeneous properties, achieving up to 50% performance improvement. Moreover, the algorithm is robust to both parameter setting and changing conditions, e.g., peer churning.

Skype is beyond any doubt the VoIP application in the current Internet application spectrum. Its amazing success has drawn the attention of telecom operators and the research community, both interested in knowing its internal mechanisms, characterizing its traffic, understanding its users' behavior. In this paper, we investigate the characteristics of traffic streams generated by voice and video communications, and the signaling traffic generated by Skype. Our approach is twofold, as we make use of both active and passive measurement techniques to gather a deep understanding on the traffic Skype generates. From extensive testbed experiments, we devise a source model which takes into account: i) the service type, i.e., SkypeOut calls or calls between two Skype clients, ii) the selected source Codec, iii) the adopted transport layer protocol, and iv) network conditions. Leveraging on the use of an accurate Skype classification engine that we recently proposed, we study and characterize Skype traffic based on extensive passive measurements collected from our campus LAN.

In this paper, we consider two popular peer-to-peer TV (P2P-TV) systems: PPLive, one of the today most widely used P2P-TV systems, and Joost, a promising new generation application of which no previous measurement study has been considered. Besides the traditional measurements like the amount of generated traffic for signaling and data transmission, the novel contribution of the paper consists in investigating the content distribution mechanisms. In particular, we evaluate the characteristics of both data distribution and signaling process for the overlay network discovery and maintenance. By considering two or more clients in the same sub-network, we observe the capability of the system to exploit the locality of peers. We also explore how the system adapts to different network conditions. The methodology we develop allows also to identify periodic behavior of the application, highlighting bursts of both data and signaling traffic.

We investigate the broadcast problem in intervehicular networks, aiming at assessing a definitive comparison of two antipodean algorithm classes: the first one makes use of instantaneous information, while the second one relies on longer- term knowledge gained through a beaconing procedure. Using a realistic microscopic model to represent the vehicular traffic flow, we investigate the performance of the above broadcast algorithm classes by simulation. In order to explore a very large algorithmic design space, we devise a Convex Hull framework that allows us to effectively compare and compactly present the boundaries of the solution space for each algorithm class. By the use of such framework we show that the beaconing approach is not justified for broadcast in suburban and highway VANETs, as there is no performance gain that justifies the complexity entailed by the beaconing procedure.

Skype is beyond any doubt the most popular VoIP application in the current Internet application spectrum. Its amazing success drawn the attention of telecom operators and the research community, both interested in knowing Skype's internal mechanisms, characterizing traffic and understanding users' behavior. We dissect the following fundamental components: data traffic generated by voice and video communication, and signaling traffic generated by Skype. We use both active and passive measurement techniques to gather a deep understanding on the traffic Skype generates. From extensive testbed experiments, we devise a source model which takes into account: (i) the service type, i.e., voice or video calls (ii) the selected source Codec, (iii) the adopted transport-layer protocol, and (iv) network conditions. Furthermore, leveraging on the use of an accurate Skype classification engine that we recently proposed, we study and characterize Skype traffic based on extensive passive measurements collected from our campus LAN.

P2P-TV systems distribute live streaming contents by organizing the information flow in small chunks that are exchanged among peers. Different strategies can be implemented at the peers to select the chunk to distribute and the destination neighboring peer. Recent work showed that a good strategy consists in selecting the latest received chunk and a random neighboring peer (latest useful chunk, random peer). In this paper, leveraging on the idea that it is convenient to favor those peers that can contribute the most to the chunk distribution, we propose to select the destination peer with a probability proportional to the peer upload bandwidth. We show that the proposed scheme has a limited sensitivity to cheating peers that maliciously declare higher bandwidth than they actually have. Considering the overlay topology, we evaluate both systems in which nodes have fixed degree and systems whose overlay setup takes into account the actual peer bandwidth by assigning more neighbors to peer with higher bandwidth. We evaluate the performance in terms of delay percentiles and loss probability and evaluate the achieved improvements. Simulation results considering scenarios with up to 10,000 peers shows that the proposed schemes significantly outperform the traditional ones, so that the chunk distribution delay drops to less than 2 s from about 12 s.

This paper focuses on inter-vehicular networks providing warning delivery service. As soon as a danger is detected, the propagation of a warning message is triggered, with the aim of guaranteeing a safety area around the point in which the danger is located. Multiple broadcast cycles can be generated so that a given lifetime of the safety area is guaranteed. The service is based on multi-hop ad hoc inter-vehicular communications with a probabilistic choice of the relay nodes. The scenario we consider consists of high speed streets, such as highways, in which vehicles exhibit one-dimensional movements along the direction of the road. We propose an analytical model for the study of this service and derive performance indices such as the probability that a vehicle is informed, the average number of duplicate messages received by a vehicle and the average delay. Moreover, we use the model to discuss system design issues, which include the proper setting of the forwarding probability at each vehicle, so that a given probability to receive the warning can be guaranteed to all vehicles in the safety area. The model is validated against simulation results. Since it is very accurate, the model can be instrumental to the performance evaluation and design of broadcasting techniques in inter-vehicular networks.

In this paper, we present measurement results collected from real traces on the network FastWeb, an ISP provider that is the main broadband telecommunication company in Italy. The network relies on a fully IP architecture and delivers to the user services such as data, VoIP and IP television over a single broadband connection. Our measurements, that are based on a passive measurement technique, focus on IP Television (IPTV) multicast, that consists of 83 digital TV channels encoded using different MPEG-2 encoders. The results show that, depending on the encoder and based on the bitrate, flows can be classified as being: CBR, 2-VBR (i.e., two typical bitrate values) and VBR. Measurement of the packet loss, jitter and inter-packet gap show that, independently from the class, packet generation process of the flows can have various degrees of burstiness. Despite the packet level burstiness, average jitter is limited to few milliseconds and no packet loss was ever observed, showing that the quality of IPTV offered by FastWeb is excellent.

Skype is a very popular VoIP software which has recently attracted the attention of the research community and network operators. Following a closed source and proprietary design, Skype protocols and algorithms are unknown. Moreover, strong encryption mechanisms are adopted by Skype, making it very difficult to even glimpse its presence from a traffic aggregate. In this paper, we propose a framework based on two complementary techniques to reveal Skypetraffic in real time. The first approach, based on Pearson'sChi-Square test and agnostic to VoIP-related trafficcharacteristics, is used to detect Skype's fingerprint from the packet framing structure, exploiting the randomness introduced at the bit level by the encryption process. Conversely, the second approach is based on a stochastic characterization of Skype traffic in terms of packet arrival rate and packet length, which are used as features of a decision process based on Naive Bayesian Classifiers.In order to assess the effectiveness of the above techniques, we develop an off-line cross-checking heuristic based on deep-packet inspection and flow correlation, which is interesting per se. This heuristic allows us to quantify the amount of false negatives and false positives gathered by means of the two proposed approaches: results obtained from measurements in different networks show that the technique is very effective in identifying Skype traffic. While both Bayesian classifier and packet inspection techniques are commonly used, the idea of leveraging on randomness to reveal traffic is novel. We adopt this to identify Skype traffic, but the same methodology can be applied to other classification problems as well.

This paper introduces WiSE, a sender-side, transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or to radio channel errors. At the same time, the available bandwidth of the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth. If the current path is severely congested and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to TCP and the standard SCTP implementation.

In this paper we focus on passive measurements of TCP traffic, main component of nowadays traffic. We propose a heuristic technique for the classification of the anomalies that may occur during the lifetime of a TCP flow, such as out-of-sequence and duplicate segments. Since TCP is a closed-loop protocol that infers network conditions by means of losses and reacts accordingly, the possibility of carefully distinguishing the causes of anomalies in TCP traffic is very appealing, since it may be instrumental to the deep understanding of TCP behavior in real environments and to protocol engineering as well. We apply the proposed heuristic to traffic traces collected at both networks edges and backbone links. By studying the statistical properties of TCP anomalies, we find that their aggregate exhibits Long Range Dependence phenomena, but that anomalies suffered by individual long-lived flows are on the contrary uncorrelated. Interestingly, no dependence to the actual link load is observed.

We address the problem of wireless stations self-configuration in a WLAN environment with overlapping access point coverages. We propose and investigate a transport-layer solution called AISLE (autonomic interface selection) that builds on top of the SCTP protocol and exploits its multihoming features. Through simulation, we evaluate AISLE's capability to maximize the throughput of multi-interface stations and to achieve an optimal partition of the stations across overlapping WLANs. Although we focus on a WLAN scenario, AISLE is independent of the technology used at the physical and MAC layers

Peer-to-peer (P2P) file sharing applications consist of nodes that share part of their local memory. A node can download a file (also called content) from another node participating to the P2P community, and must allow others to download files stored in its own shared local memory. Since nodes join and leave the P2P community at their will in an uncoordinated way, copies of the same file must be stored in many nodes. By means of this redundancy, the probability that a given content cannot be retrieved because all nodes storing it are temporary inactive can be kept small. Content management policies are the distributed strategies implemented at the nodes to decide the set of files to be stored in the local memory. In this paper, we propose, analyze and compare different content management policies. The considered performance indices include the probability that content cannot be retrieved due to lack of available copies of the content, and the amount of traffic generated in the network.

This paper presents WiSE, a transport-layer protocol that modifies the standard SCTP protocol. WiSE aims at exploiting SCTP's multihoming capabilities by selecting in real time the best choice among available, alternate paths to the same destination. Through the use of bandwidth estimation techniques, WiSE tries to infer whether losses are due to congestion or radio channel errors. At the same time, the available bandwidth on the current path used for transmission is matched to that of an alternate path, also probed for available bandwidth; if the current path is severely congested, and the alternate path is lightly loaded, WiSE switches the transmission onto the alternate path, using SCTP's flexible path management capabilities. Extensive simulations under different scenarios highlight the superiority of the proposed solution with respect to the standard SCTP implementation.

In peer-to-peer networks, nodes can be both resource providers and resource consumers at the same time. In this sense, the services offered by a peer-to-peer network rely on resource sharing among peers. This work focuses on how peers share their access link capacity between upload and download rates. In our model peers are rational agents, and choose their strategy in order to maximize their own utility. We suppose that the bottleneck is not in the network core, but in the network edge: the access link capacity of each peer connected to the network is a scarce resource and the peers have to compete for it. Instead of trying to settle the controversies which can arise, we imagine that every peer organizes an auction to give away its bandwidth. While the service rate is always granted to the peer who makes the lowest request, the amount of allocated rate depends on the implemented auction mechanism. Numerical experiments show that in a peer-to-peer game where the access link capacities are homogeneous, a second-price auction guarantees that the equilibrium rate allocation is optimal.

TCP Smart Framing, or TCP-SF for short, enables the Fast Retransmit/Recovery algorithms even when the congestion window is small. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP-SF adopts a novel segmentation algorithm: while Classic TCP always tries to send full-sized segments, a TCP-SF source adopts a more flexible segmentation algorithm to try and always have a number of in-flight segments larger than 3 so as to enable Fast Recovery. We motivate this choice by real traffic measurements, which indicate that today's traffic is populated by short-lived flows, whose only means to recover from a packet loss is by triggering a Retransmission Timeout. The key idea of TCP-SF can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server TCP stack only, and can be easily coupled with recent TCP enhancements. The performance of the proposed TCP modification were studied by means of simulations, live measurements and an analytical model. In addition, the analytical model we have devised has a general scope, making it a valid tool for TCP performance evaluation in the small window region. Improvements are remarkable under several buffer management schemes, and maximized by byte-oriented schemes.

In this paper, we propose a MMPP (Markov modulated Poisson process) traffic model that accurately approximates the LRU (long range dependence) characteristics of Internet traffic traces. Using the notion of sessions and flows, the proposed MMPP model mimics the real hierarchical behavior of the packet generation process by Internet users. Thanks to its hierarchical structure, the proposed model is both simple and intuitive: it allows the generation of traffic with the desired characteristics by easily setting a few input parameters which have a clear physical meaning. Results prove that the queuing behavior of the traffic generated by the MMPP model is coherent with the one produced by the real traces collected at our institution edge router under different networking scenarios and loads. Due to its characteristics, the proposed MMPP traffic model can be used as a simple and manageable tool for IP network performance analysis, as well as for network planning and dimensioning.

Wireless local area networks (WLANs) based on the IEEE 802.11 technology are becoming increasingly popular and widely deployed. However, the growing need for quality of service (QoS) guarantees is difficult to implement in distributed systems like WLANs, where the random access protocol and the unpredictability of the wireless channel hamper their interaction with well-established architectures like DiffServ. Even without trying to provide deterministic QoS guarantees, simpler requirements are hard to get by. For example, the basic requirement of providing fair access to all users is conflicting with the nature of higher-layer protocols: TCP is fair only under certain conditions, hardly met by 802.11b WLANs. Another basic requirement is the protection for short-lived TCP flows, that are sensitive to losses during the early stages of the TCP window growth. The main contribution of this paper is the proposal of an LLC-layer algorithm that can be implemented at both access point (AP) and wireless stations (WSs). The algorithm aims at guaranteeing fair access to the medium to every user, by awarding longer transmission opportunities to WSs that experienced short channel failures. At the same time, such award mechanism can protect short-lived flows while they strive to get past the critical "small window regime". We outline the proposed solution and present a simulation study that shows the effectiveness of the new algorithm in comparison to the standard 802.11b implementation.

This paper presents a new analytical model for the estimation of the performance of TCP connections. The model is based on the description of the behavior of TCP in terms of a closed queueing network. The model is very accurate, deriving directly from the finite state machine description of the protocol. The assessment of the accuracy of the analytical model is based on comparisons against detailed simulation experiments developed with the ns-2 package. The protocol model interacts with an IP network model that can take into account meshed topologies with several bottlenecks. Numerical results indicate that the proposed closed queueing network model provides accurate performance estimates in all situations. A novel and interesting property of the model is the possibility of deriving ensemble distributions of relevant parameters, such as, for instance, the transmission window size or the timeout probability, which provide useful insight into the protocol behavior and properties.

In this paper, we propose a new technique for the performance analysis of cellular mobile communication networks based on a frequency division multiple access/time division multiple access scheme (such as global system for mobile communications), in which the utilization of two separate frequency bands leads to a complex cellular structure with overlapping microcells and macrocells. Call durations and dwell times are described by random variables with general distributions; the call duration distribution is then approximated by a two-phase hyper-exponential distribution with the same first and second moments as the original general distribution. The analysis technique is based on Markovian assumptions as regards the traffic flows entering both microcells and macrocells, as well as an assumption of flow balance between handovers into and out of any cell. The analytical model is validated against results of detailed simulation experiments for various system configurations, and shown to provide quite accurate predictions.

Analytical approaches for the performance investigation of portions of the Internet often consider the behavior of TCP over congested (or bottleneck) links. In several cases, the analysis is based on an iterative fixed point approximation (FPA) to compute the equilibrium point, in terms of packet loss rate and offered load, that represents the operating point of the network. Almost invariably, the FPA is conjectured to converge, but no proof of convergence is provided. This paper proves that a general model of a reliable protocol (such as TCP) over congested links converges to a unique stable solution under mild regularity conditions. This provides a justification of the convergence observed in the literature and a solid base for the further development of analytical approaches based on FPAs.

In this paper we describe an analytical model to compute the packet delay distribution in a cell of a wireless network operating according to the GSM/GPRS standard. GSM (Global System for Mobile communications) is the most widely deployed wireless telephony standard, and GPRS (Generalized Packet Radio Service) is the technology that is now available to integrate packet data services into GSM networks. By comparing the performance estimates produced by the analytical model against those generated by detailed simulation experiments, we show that the proposed modeling technique is quite accurate. In addition, we show that the results produced by the analytical model are extremely useful in the design and planning of a wireless voice and data network.

In this paper we consider the problem of resource management in GSM/GPRS cellular networks offering not only mobile telephony services, but also data services for the wireless access to the Internet. In particular we investigate channel allocation policies that can provide a good tradeoff between the QoS guaranteed to voice and data services end users, considering three different alternatives, and developing analytical techniques for the assessment of their relative merits. The first channel allocation policy is called voice priority, since it gives priority to voice in the access to radio channels; we show that this policy cannot provide acceptable performance to data services, and we discuss the reasons for this shortcoming. The second channel allocation policy is called R-reservation; it statically reserves a fixed number of channels to data services, thus drastically improving their performance, but subtracting resources from voice users, even when these are not needed for data, thus inducing an unnecessary performance degradation for voice services. The third channel allocation policy is called dynamic reservation; as the name implies, it dynamically allocates channels to data when necessary, using the information about the queue length of GPRS data units within the base station. A threshold on the queue length is used in order to decide when channels must be allocated to data. Numerical results, show that the dynamic reservation channel allocation policy can provide very effective performance tradeoffs for data and voice services, with the additional advantage of being easily managed through the setting of the threshold value.

Wireless access to the Internet requires that information integrity is preserved while transmitting data over the radio channel. ARQ schemes and TCP are often used as error-control techniques at the link layer and at the transport layer, respectively. We study the interactions between an ARQ protocol and TCP when a data traffic connection includes both wired and wireless links. By using standard Markovian techniques, we analyze the impact of different parameter settings of the ARQ scheme and of the radio channel conditions on the TCP performance.

Consider a communication link where the last hop is wireless and TCP is used as transport protocol over the end-to-end connection. We study the capability of the link layer to hide losses over the wireless link to TCP in spite of the time varying transmission quality. We focus on link-layer retransmission mechanisms and determine their parameter setting in such a way that a reliable communication link is provided. In particular, we choose a significant QoS metric at the transport layer and fixed its targeted value, and we adapt the maximum number of link-layer transmissions to the characteristics of the wireless link so that the desired QoS at the transport layer is provided. Results showing the impact of the link-layer retransmissions on the TCP performance are derived by using analytical models based on Markovian techniques.

In this paper we propose an enhancement to the TCP protocol, called TCP Smart-Framing(TCP-SF), that enables the Fast Recovery algorithm for short lived flows, as most of the current Internet traffic is. Without modifying the TCP congestion control based on the additive-increase/multiplicative-decrease paradigm, TCP Smart-Framing adopts a novel segmentation algorithm: while classic TCP starts sending one segment, a TCP-SF source is allowed to send an initial window of 4 smaller segments, whose aggregate payload is equal to the connection's MSS. This key idea can be implemented on top of any TCP flavor, from Tahoe to SACK, and requires modifications to the server behavior only.

This paper presents a new analytical model for the estimation of the performance of TCP connections. The model is based on the description of the behavior of TCP-Tahoe in terms of a closed queueing network, whose solution can be obtained with very low cost, even when the number of TCP connections that interact over the underlying IP network is huge. The protocol model can be very accurate, deriving directly from the finite state machine description of the protocol. The assessment of the accuracy of the analytical model is based on comparisons against detailed simulation experiments developed with the ns-2 package. Numerical results indicate that the proposed closed queueing network model provides extremely accurate performance estimates, not only for average values, but even for distributions, in the case of the classical single-bottleneck configuration, as well as in more complex networking setups.

We present an analytical approach for the joint optimization of quality of service (in terms of call blocking probability) and tariffs for a mobile telephony network. The analytical approach is based on the combination of traditional telecommunication system design techniques and econometric approaches for profit maximization.

A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephony networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments, results of a previously proposed Markovian analysis approach, and upper and lower bounds to the call blocking probability. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.

We present a framework for the analysis of a set of adaptive variable-bit-rate voice sources in a packet network. The instantaneous bit rate of each source is determined by an end-to-end control mechanism that, based on measurements of packet delay and loss rate, selects the rate that best matches current network conditions. Several such algorithms can be analyzed with the proposed framework, which consists of a detailed Markovian model of the source behavior and of an approximate description of the interaction between the sources and the underlying network. The model of the source takes into account time intervals during which a connection is active as well as intervals of inactivity; within a given conversation, it also models on/off (speech/silence) periods. The interaction of a source with the rest of the system is derived through an iterative procedure that evaluates the feedback that a source receives from the network. A case study presenting the results relative to an adaptive system transmitting at bit rates typical of widely used speech coding standards (64 kb/s, 13 kb/s and 8 kb/s) illustrates the proposed framework.

A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephone networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.

In this paper, we outline a methodology that can be applied to model the behavior of TCP flows. The proposed methodology stems from a Markovian model of a single TCP source, and eventually considers the superposition and interaction of several such sources using standard queueing analysis techniques. Our approach allows the evaluation of such performance indices as throughput, queueing delay and packet loss of TCP flows. The results obtained through our model are validated by means of simulation, under several topology and traffic settings.

In this paper we consider dual-band GSM networks, where voice and data services are offered to users moving over an area covered with overlapping macrocells and microcells. For this wireless network context we develop simple approximate analytical models of the system dynamics, and we exploit such analytical models for the design and planning of the critical system parameters, with particular attention to the number of traffic channels to be activated within macrocells.

This paper proposes an analytical approach for the evaluation of the quality of service (QoS) perceived by end users in cellular communication systems which provide radio coverage of suburban loads or highways, where the user mobility is typically very high. The approach is based on the interaction between the queueing model of an individual cell and an accurate description of the end user behavior, the latter comprising a probabilistic description of the user mobility and call duration. Different classes of user mobility and services can be considered. As a measure of the QoS perceived by end users, the probability that a call completes successfully is derived.

This paper illustrates the development of an approximate analytical model for a communication network providing integrated services to a population of mobile users, and presents performance results to both validate the analytical approach, and assess the quality of the services offered to the end users. The analytical model is based on continuous-time multidimensional birth-death processes, and it is focused on just one of the cells in the network. The cellular system is assumed to provide three classes of service: the basic voice service, a data service with bit rate higher than the voice service and a multimedia service with one voice and one data component. In order to improve the overall network performance, some channels can be reserved to handovers, and multimedia calls that cannot complete a handover are decoupled, by transferring to the target cell only the voice component and suspending the data connection until a sufficient number of channels becomes free. Numerical results demonstrate the accuracy of the approximate model, as well as the effectiveness of the newly proposed multimedia call decoupling approach.

The paper considers medium access control protocols for all-optical packet networks based on WDM multichannel ring topologies where nodes are equipped with one fixed-wavelength receiver and one wavelength-tunable transmitter. Such networks provide separate channels for slotted transmissions to disjoint subsets of destination nodes. Some simple access protocols based on local status information are described. Since these protocols are not able to enforce fairness by themselves, fairness control algorithms derived from those adopted in the Metaring high-speed metropolitan area network are also proposed. Analytical and simulation results are presented to assess the capacity of the proposed protocols in uniform traffic conditions, with a particular focus on the case where at each node the packet to be transmitted is randomly selected. In spite of the simplicity of the proposed access schemes, numerical results show that good performance can be achieved and the fairness problems inherent in the considered network topologies can be overcome.

Process algebras are a suitable formalism both for system-level description and for ATPG with formal verification techniques. A functional fault model for system-level descriptions is presented and experimental data are reported. The contributions of this paper are the definition of a general-purpose fault model for concurrently evolving processes and the implementation of a test pattern generation procedure, as a variant of the testing equivalence proof. A complete test system is implemented, allowing one to describe systems, describe faults and generate test patterns within the same environment.>