The rapid evolution of telecommunication networks is leading to increasingly complex systems, requiring adaptive, flexible, and intelligent mechanisms for resource management, orchestration, and access control. In this context, the Network Digital Twin (NDT) paradigm emerges as a powerful tool to model the behavior of devices, communication links, operating environments, and applications in complex networks. This paper introduces FALCON, a Digital-Twin-based orchestration framework designed to optimize horizontal offloading in UAV-based Flying Ad Hoc Networks (FANETs) providing edge computing services to ground devices in remote areas. FALCON integrates multiple Smart Agents (DQN, A2C, PPO) running concurrently on the Digital Twin to dynamically determine the optimal offloading probabilities. A proof-of-concept demonstrates how the framework performs real-time What-if Scenario analyses and adapts to varying workload and channel conditions. Numerical results highlight the gains achieved through coordinated model selection and reuse, showing reduced end-to-end delay and faster convergence compared to standalone DRL-based controllers. • Digital Twin framework for real-time FANET orchestration. • Parallel Smart Agents enable fast What-if scenario evaluation. • Dynamic model selection adapts to FANET state and intents. • Reduced service delay compared to standalone DRL methods. • Model reuse ensures rapid reaction to changing UAV conditions.

Cloud gaming is a rapidly growing domain that requires low-latency and high-responsiveness architectures to deliver an engaging user experience. This paper presents an innovative Digital Twin (DT)-based framework designed to mitigate the effects of variable network conditions on video streaming and input responsiveness. The proposed architecture integrates Console and User Equipment DTs, which synchronize video streams and user inputs using lightweight RTSP and MQTTbased communication, ensuring real-time interaction even in challenging network scenarios. To validate the framework, we developed a custom cloud gaming application simulating a shark-and-fish pursuit game. This use case allows for precise evaluation of system performance under varying latency and delay conditions. Experimental results demonstrate the architecture’s ability to maintain game responsiveness, revealing critical delay thresholds and highlighting the impact of delay variability on user performance. These findings illustrate the potential of DTbased architectures to enhance the robustness and fairness of cloud gaming systems.

Virtual reality (VR) cloud gaming faces significant challenges in terms of latency and bandwidth consumption, particularly in softwarized 5 G and beyond (5G&B) networks. Ensuring an immersive experience on low-end devices, such as smartphones and cardboard-like headsets, requires innovative solutions to mitigate delay effects and optimize resource usage. This research focuses on enhancing VR cloud gaming performance by leveraging Digital Twins (DTs) and hierarchical compression techniques. The proposed system adopts a client-server architecture, where computationally intensive tasks are offloaded to the server, minimizing processing requirements on the client side. A key contribution is a novel delay estimation and management framework that employs DTs to synchronize video streaming and user input, reducing the impact of latency. Additionally, viewport-based hierarchical compression is applied to optimize bandwidth consumption while maintaining high perceived quality. The proposed architecture is evaluated through extensive experiments analyzing the effects of input delay, delay fluctuations, and compression methods on user performance and perceived quality. Results demonstrate that the system effectively mitigates adverse network conditions, ensuring a seamless and immersive VR gaming experience even in suboptimal environments.

Deep reinforcement learning (DRL) techniques have the potential to significantly improve the ability of Unmanned Aerial Vehicles (UAVs) for mobile device localization in disaster scenarios by optimizing flight paths and enhancing signal detection accuracy using Reference Signal Received Power (RSRP) measurements. DRL allows UAVs to learn optimal navigation strategies autonomously in dynamic and complex environments, leading to more efficient and accurate localization of mobile devices. The integration between UAVs and 4G/5G technology allows for more accurate and timely localization of mobile devices under the rubble, thereby improving the overall effectiveness of the system. Smallcells, low-power cellular base stations, are used to enhance coverage and capacity. In this study, we propose a DRL-based UAV-Smallcell system that can quickly and efficiently localize devices in large disaster areas. The performance of the proposed system is evaluated through an extensive simulation campaign to demonstrate that our approach significantly improves the effectiveness of mobile device localization compared to other state-of-the-art approaches.

Job offloading represents one powerful key-enabler for delay-sensitive applications in Intelligent Transport Systems (ITS). Vehicles can rely on job offloading to move their jobs to the Road Side Units (RSUs) devices, which are typically equipped with better computing power. Ultimately, this procedure can help relieve the computational burden on the vehicles, to the benefit of the overall processing latency. However, fixed vehicular infrastructure cannot guarantee ubiquitous, ever-present support for job offloading, especially in the case of hard-to-reach, remote areas where the power grid and/or connectivity are not present. One way to solve this issue is to resort to portable, batterypowered RSUs, which can be installed potentially everywhere and allow for greater flexibility. However, this calls for a management framework that strikes the trade-off between RSU battery-life on one hand, and processing latency on the other. Moreover, this framework should be able to work in a fully-distributed way, without the need of an existing infrastructure. With all of this in mind, our contribution is two-fold. First, we introduce the idea of MEC-in-a-box (M-Box) stations, batterypowered RSUs that can operate even in the absence of a fixed infrastructure or connectivity. Second, in order to support the operation of M-Box stations, we design MANTRA, an offloading framework for vehicular networks that balances energy consumption and processing latency in the M-Box stations. MANTRA allows the M-Box stations to autonomously i) turn on and off their processing units to alternatively increase their processing power or save energy, and ii) manage the amount of jobs offloaded towards the other M-Box stations to balance the network load. We evaluated the performance of MANTRA vs other approaches, including a centralized solution with a full knowledge of the system. First, the results highlight how MANTRA is able to match the performance of the centralized approach, and is also capable of surpassing all the other baselines in various experimental scenarios. Index Terms

The entertainment and gaming industries continue to evolve, so they are becoming increasingly reliant on advanced network capabilities to deliver immersive, real-time experiences. In-network computing (INC) is a transformative paradigm in the design of the network architecture in 6G networks. It facilitates the offloading of computational tasks from devices to edge nodes and central servers, enabling faster data processing in network nodes and reducing latency and response times for high-demand applications, enhancing efficiency. This way, it will be possible to provide the final users with advanced applications like Augmented Reality (AR) and Virtual Reality (VR). In this paper we introduce a 6G Network Infrastructure testbed designed for application execution and testing, supporting INC, and we present an Adaptive 360° Video Streaming service using INC facilities. The testbed has been designed to represent a vertical application for SLICES-RI and SUNRISE-6G projects. Some numerical results will assesses the performance of this dynamic video streaming system.

In this paper we propose MARBLE, a distributed framework based on multi-player multi-armed bandit algorithms that aims to support job offloading procedures in FANETs to comply with strict job latency requirements while also minimizing the energy consumption of the system, thus also increasing the Unmanned Aerial Vehicles (UAVs) flight duration. To demonstrate the effectiveness of the framework, we conduct an extensive evaluation campaign and compare MARBLE with several baselines, including a centralized, oracle-based approach. Our results show that MARBLE outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed manner, in compliance with the latency and energy efficiency requirements. Most notably, MARBLE also improves the FANETs flight duration of up to 69% as compared to the baselines.

In the last few years, Virtual Reality (VR) is assuming a prominent role and is recognized as a pivotal technology in various sectors. However, transmission of immersive videos produced by real-time 360° cameras or stored on remote servers to reproduce 3D environments, or streamed by video games accessible through headsets, would require a lot of network bandwidth that, in many cases, is not available or too expensive to be obtained. In this paper, we leverage on network softwarization provided by new 5G&B networks, and introduce an Adaptive Closed-loop Encoding VNF named 360-ST for adaptive compression of 360° video streaming. This VNF is able to apply a hierarchical compression that takes into account both the bandwidth currently available in the network, and the user viewport. The agent that is in charge of deciding the different compression ratio at runtime uses Deep Reinforcement Learning to optimize a reward function and adapt to the changes of the network bandwidth, the end-to-end latency, the user movements within the scene and the video content. The results indicate that our proposed method consistently outperforms state-of-the-art algorithms by an average of 8% to 46% in terms of achieved Peak Signal-to-Noise Ratio (PSNR).

The contention window (CW) has a significant impact on the efficiency of Wi-Fi networks. Unfortunately, the basic access method employed by 802.11 networks does not scale well for increasing number of stations. Therefore, in this paper we propose a new CW control method which leverages Deep Reinforcement Learning (DRL) to learn the optimal policies under different network conditions. For this reason, we propose the Online Smart Collision Avoidance Reinforcement learning (OSCAR) algorithm, a DRL-based algorithm that can be deployed online to quickly and efficiently find the best contention window that maximizes the throughput. We also demonstrate through a simulation campaign that it is able to learn the optimal policies way faster than the current state of art methods while also being able to keep the computational cost low.

1 The evolution of smart objects towards the Internet of Softwarized Things (IoST) in 6G networks will provide the society with dynamic and programmable systems of interconnected smart devices interacting with little to no human intervention. One pivotal aspect of this evolution is represented by edge learning, which brings machine-learning algorithms at the network edge to achieve massive connectivity, ultra-low latency, energy efficiency, security and privacy. Unfortunately, in many application scenarios commonly envisioned for 6G, edge learning is not feasible neither locally in the smart objects, due to their computation and energy limitations, nor by servers at the edge of the cabled network, because not connected with adequate powerful links. To this purpose, this paper proposes ODEL, an On-Demand Edge-Learning framework that uses a Flying Ad-hoc NETwork (FANET) to bring computing and networking facilities on-site for edge learning. ODEL is based on a marketplace employing a non-cooperative game theoretic approach: UAVs are provided by different third-party providers in exchange of some economic gain. A non-linear optimization problem is formulated in order to determine the optimal distribution of flows that maximizes revenue for each UAV provider, and is solved by means of the Variational Inequality (VI) theory.

The optimization of job offloading procedures in modern vehicular networks is a problem of utmost importance. In this regard, this paper proposes MANTRA, a distributed framework based on multi-player multi-armed bandit (MP-MAB) algorithms for latency- and energy-aware job offloading in vehicular networks. The main goal of MANTRA is to support procedures of job offloading in green vehicular networks to achieve a target tradeoff between energy consumption and job processing latency. In particular, MANTRA is intended to run on so-called MEC-in-a-box (M-Box) devices, portable battery-powered Road Side Units (RSUs) specifically designed to work without mobile connectivity and of a fixed power grid.To demonstrate MANTRA effectiveness, we model the vehicular network using the queueing theory for M/M/m/K systems. We run an extensive evaluation campaign and compare MANTRA with several baselines, including a centralized, oracle-based approach. In such a way, we demonstrate how MANTRA outperforms the baselines and quickly converges to the performance of the centralized approach in a fully-distributed way in terms of job processing latency and network outage probability.

Recently, Flying Ad Hoc Networks (FANET) have been proposed to empower 5G networks to support complex missions and provide ubiquitous connectivity to heterogeneous devices. However, it is needed to cope with the limited UAV capabilities (e.g., limited available energy to supply engines and computing elements, limited computing capabilities), as well as with the need to provide network and application services as foreseen in highly dynamic and time varying 5G ecosystems. This paper presents for the first time a comprehensive framework that integrates a FANET with a 5G network, with the aim of providing services that can be even chained with each other. This model is comprehensive in the sense that it takes into account physical constraints of the devices, as well as features and requirements of traffic flows. For this framework, the paper proposes a mathematical optimization model, allowing Virtual Function (VF) placement and chaining, aimed at minimizing energy consumption and service unsatisfaction probabilities of the FANET as a whole without employing heuristics for the solution of the problem. Two placement strategies named MLP and WMP are introduced and compared with the standard placement strategy named NoShP. An extensive numerical analysis shows that MLP and WMP allow us to well catch network dynamics and to reduce the number of virtual functions needed while decreasing the power consumption, so increasing UAV flight time and network lifetime.

The next generation of wireless communications networks, namely 6G, will be aimed at realizing a fully connected world, and at providing ubiquitous connectivity to people and objects even in remote areas that are very far from the structured Internet core network. These goals include the definition and the design of intelligent communications environments mainly characterized by pervasive artificial intelligence and large-scale automation. The target of this paper is the design of a management framework for edge networks realized with Flying Ad-Hoc Networks (FANET) consisting of a set of Unmanned Aerial Vehicles (UAVs) to provide a remote geographic area with computing and networking facilities for delay-sensitive applications. To this purpose, each UAV is equipped with a Computing Element (CE) to process jobs received through vertical offloading from ground devices. In addition, horizontal offload among UAVs of the FANET is introduced for load balancing purposes, to guarantee that the FANET computation delay for each received job is minimized and is almost independent of the activity state of the area covered by the UAV receiving that job. The proposed FANET management framework is based on Deep Reinforcement Learning (DRL) to allow zero-touch adaptation to the time-variant activity state of the area covered by each UAV. Numerical results demonstrate the power of the proposed framework and the enhancements achieved with respect to the current literature.

Network softwarization is one of the key enablers of the future Internet evolution, also supporting the road from the fifth generation (5G) to the next-generation communication systems, namely 6G, with their main objective of bringing hyper-connected experience to every corner of society.

The fifth generation (5G) of mobile networks has the goal of providing ultra-high-speed access everywhere and enabling connectivity of massive number of devices in an ultra-reliable and affordable way. However, in many environments that are considered strategic for 5G applications, a structured network is not available. A solution to extend the features provided by Multi-access Edge Computing (MEC), one of the main enabler of 5G, in these contexts, is to use fleets of MEC UAVs, each equipped with a computing element (CE), and organized in Flying Ad-hoc Networks (FANET).In this paper, we propose a FANET platform with “horizontal” offload from the most overloaded UAVs to the least overloaded ones, aimed at balancing load among UAVs. A decision policy called UAV Smart Offloading (USO), based on Deep Reinforcement Learning, is also defined to optimize performance in terms of delay perceived by the ground devices connected to the FANET. A numerical analysis is introduced to evaluate performance achieved by the proposed platform.

The Softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and data-center providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on the management of softwarized networks.

Network slices for delay-constrained applications in 5G systems require computing facilities at the edge of the network to guarantee ultra-low latency in processing data flows generated by connected devices, which is challenging with larger volumes of data, and larger distances to the edge of the network. To address this challenge, we propose to extend 5G network slices with Unmanned Aerial Vehicles (UAV) equipped with multi-access edge computing (MEC) facilities. However, onboard computing elements (CE) consume UAV's battery power thus impacting its flight duration. We propose a framework where a System Controller (SC) can turn on and off UAV's CEs, with the possibility of offloading jobs to other UAVs, to maximize an objective function defined in terms of power consumption, job loss, and incurred delay. Management of this framework is achieved by reinforcement learning. A Markov model of the system is introduced to enable reinforcement learning and provide guidelines for the selection of system parameters. A use case is considered to demonstrate the gain achieved by the proposed framework and discuss numerical results.

Internet of Things (IoT) has emerged as a huge paradigm shift by connecting a versatile and massive collection of smart objects to the Internet, coming to play an important role in our daily lives. Data produced by IoT devices can generate a number of computational tasks that cannot be executed locally on the IoT devices. The most common solution is offloading these tasks to external devices with higher computational and storage capabilities, usually provided by centralized servers in remote clouds or on the edge by using the fog computing paradigm. Nevertheless, in some IoT scenarios there are remote or challenging areas where it is difficult to connect an IoT network to a fog platform with appropriate links, especially if IoT devices produce a lot of data that require processing in real-time. To this purpose, in this article, we propose to use unmanned aerial vehicles (UAVs) as fog nodes. Although this idea is not new, this is the first work that considers power consumption of the computing element installed on board UAVs, which is crucial, since it may influence flight mission duration. A System Controller (SC) is in charge of deciding the number of active CPUs at runtime by maximizing an objective function weighing power consumption, job loss probability, and processing latency. Reinforcement Learning (RL) is used to support SC in its decisions. A numerical analysis is carried out in a use case to show how to use the model introduced in the article to decide the computation power of the computing element in terms of number of available CPUs and CPU clock speed, and evaluate the achieved performance gain of the proposed framework.

In this demonstration we illustrate how haptic communication can occur via a mobile application. In particular, we present an Android-based platform that allows connectivity among haptic devices and smartphones. Our app was designed specifically for educational applications, and we used the Hapkit as our first exemplary hardware platform. Our open-source implementation offers several haptic emulations designed for K-12 Physics courses; furthermore, our Android application provides instrumented metrics capable of analyzing latency and other network parameters among communicating hardware and software processes.

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special issues was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

Flow-induced molecular communications are a promising communication paradigm based on the exchange of molecule concentrations in a continuous fluid, guided and directed through microfluidic pipes. An emerging approach to implement this type of communications is based on discrete (or droplet-based) microfluidics. It exploits the idea to represent binary information by encapsulating the signaling molecules inside droplets, which are dispersed in an immiscible fluid and delivered, through micro pipes to the destination. The objective of this paper is to study a binary droplet-based microfluidic system for flow-induced molecular communications from an information theoretical perspective. To this end, we first show that the binary discrete microfluidic channel is characterized by memory and anticipation. Accordingly, we provide a mathematical model based on a finite-state Markov chain. Finally, we evaluate the capacity of the binary droplet-based microfluidic channel.

The softwarization of networks is enabled by the SDN (Software-Defined Networking), NV (Network Virtualization), and NFV (Network Function Virtualization) paradigms, and offers many advantages for network operators, service providers and datacenter providers. Given the strong interest in both industry and academia in the softwarization of telecommunication networks and cloud computing infrastructures, a series of special section was established in IEEE Transactions on Network and Service Management, which aims at the timely publication of recent innovative research results on management of softwarized networks.

This work presents a video surveillance platform for smart cities designed to exploit the facilities offered by SDN/NFV networks to achieve gain in terms of scalability, flexibility, OpEx and CapEx reduction. The main elements of this platform are the Smart Access Nodes (SAN) and the Network Access Nodes (NAN). SAN devices are distributed on the territory to cover small/medium areas and provide connectivity to Data Flow Senders and Users. Data flow Senders may be IP cameras, sensors and camera-equipped drones. Thanks to the platform, they can be installed with no particular configuration setup.

Application of Software Defined Network (SDN) and Network Function Virtualization (NFV) paradigms to telecommunications networks are determining an evolution towards a so-called Software Defined Infrastructure (SDI), where network and service functions are developed and controlled as software applications. Management and orchestration of resources are key elements in this softwarization process. To support it, a theoretical activity of modeling network nodes and networks of nodes is needed in both design and operational phases. To this purpose, this paper defines an analytical model of an SDN/NFV node that can work as a building block for a more complex model of a whole softwarized network.

In the SDN/NFV ecosystem, network services are provided as single Virtual Network Functions (VNFs) or chains of them, each instantiated and executed on dedicated servers. So far, the chaining of those virtual functions, which is also known as the service chain composition problem, has been mostly performed by Telco Operators (TOs) for the great advantages they receive in terms of Capex and OpEX. However, such a fully centralized approach generally results in solutions which do not scale well with the number of customers. The aim of this paper is to provide a distributed, scalable and efficient solution to the service chaining problem. Specifically, we develop an VNF marketplace system where third-party VNF providers sell VNF as a service (VNFaaS) and adapt their pricing policies according to network dynamics. Also, we leverage on game theory to provide a (theoretically proven) efficient distributed solution which accounts for monetary costs, communication latencies and congestion of computational resources.

The network function virtualization (NFV) paradigm has gained increasing interest in both academia and industry as it promises scalable and flexible network management and orchestration. In NFV networks, network services are provided as chains of different virtual network functions (VNFs), which are instantiated and executed on dedicated VNF-compliant servers. The problem of composing those chains is referred to as the service chain composition problem. In contrast to centralized solutions that suffer from scalability and privacy issues, in this paper, we leveragenon-cooperativegame theory to achieve a low-complexity distributed solution to the above-mentioned problem. Specifically, to account for selfish and competitive behavior of users, we formulate the service chain composition problem as an atomic weightedcongestion gamewith unsplittable flows and player-specific cost functions. We show that the game possesses a weighted potential function and admits a Nash equilibrium (NE). We prove that the price of anarchy is upper-bounded, and also propose a distributed and privacy-preserving algorithm which provably converges toward an NE of the game in polynomial time. Finally, through extensive numerical results, we assess the performance of the proposed distributed solution to the service chain composition problem.

Softwarization of networks allows simplifying deployment, configuration, and management of network functions. The driving force toward this evolution is represented by software defined networking that allows more flexible and dynamic network resource allocation and management. The efficient allocation and orchestration of network resources is of extreme importance for this softwarization process, and many centralized solutions have been proposed. However, they are complex and exhibit scalability issues. So, distributed solutions are to be preferred but, in order to be effective, should quickly converge towards equilibrium solutions. In this paper, we focus on making distributed resource allocation and orchestration a viable approach, and prove convergence of the relevant mechanisms. Specifically, we exploit game theory to model interactions between users requesting network functions and servers providing these functions. Accordingly, a two-stage Stackelberg game is presented, where servers act as leaders of the game and users as followers. Servers have conflicting interests and try to maximize their utility; users, on the other hand, use a replicator behavior and try to imitate other user’s decisions to improve their benefit. The framework proves the existence and uniqueness of an equilibrium, and a learning mechanism to converge to such equilibrium is proposed. Numerical results show the effectiveness of the approach.

In the last few years, network “softwarization” is gaining increasing popularity to achieve dynamicity and flexibility. Cloud computing, as well as the new paradigms of software defined networking (SDN) and network functions virtualization (NFV), are supporting this evolution. However, the need to move services closer to users to guarantee low latency in the service fruition on one hand, and the trend to support personalization of services on the other, are stimulating the migration of services toward edge nodes (in the so-called “fog computing” fashion). This is the target of the INPUT platform, proposed in the INPUT project to support future Internet personal cloud services in a more scalable and sustainable way, and with innovative added-value capabilities. The INPUT platform enables next-generation cloud applications to go beyond classical service models, and even replaces physical smart devices, usually placed in users' homes (e.g., set-top-boxes, etc.), with virtual entities, providing them to users “as a Service.” In this paper, we present the INPUT paradigm and discuss a relevant use case-namely, the virtual set-top-box-adopted to prove the feasibility of the softwarized SDN/NFV paradigm jointly with the fog-computing approach for the support of personal cloud services. The INPUT platform is also compared with a legacy approach to evaluate the gain in terms of quality of experience for both static and mobile users.

Costs associated with the power consumption and cooling requirements of servers in a data center are significant. For this reason, a lot of research efforts in the area of power management have been devoted toward greening data centers and clouds in the last years. This work starts from the observation that the main target of a DC manager is not only to save energy for the environment, but also reduce operational costs. To this purpose, the paper focuses on a single server management, and proposes a joint power management and resource allocation strategy that modulates the number of active VMs on a given server according to both the current workload and the current value of the virtual machine power cost, in order to minimize the total server management cost, while matching the SLA with the customers. An analytical model is proposed to support design of the resource allocation controller parameters.

In the last few years, Software Defined Networks (SDN) and Network Functions Virtualization (NFV) have been introduced in the Internet as a new way to design, deploy and manage networking services. Working together, they are able to consolidate and deliver the networking components using standard IT virtualization technologies not only on high-volume servers, but even in the end user premises. In this context, this paper presents the NetFATE architecture, a platform aimed at putting virtual network functions (VNF) at the edge of the network. This platform is based on free and open source software on Provider Equipment (PE) and Customer Premise Equipment (CPE) nodes, so allowing function deployment simplification and management cost reduction. Finally, the paper proposes a case study, consisting in the implementation of two virtual personal firewalls used by two clients moving between two access points located at the edge of the core network.

In the last few years, SDN and NFV have been introduced with the potential to change the ossified Internet paradigm, with the final goal of creating a more agile and flexible network, at the same time reducing both CAPEX and OPEX costs. For this reason, a lot of research efforts have been devoted to optimize the implementation of these technologies, also inheriting experience from data center management. However, orchestration and management of SDN/NFV nodes present new challenges with respect to data center management, mainly due to the telecommunications context where NFV resides. With this in mind, the target of this paper is to define a management model for NFV customers and service providers, a green policy of the customer premises equipment (CPE) nodes, and an analytical model to support their design. The model is then applied to a case study to demonstrate how it can be used to optimize system performance and choose the most important parameters characterizing the design of a CPE node.

The rapid evolution of low-cost standard hardware coupled with the diffusion and the wide spread of new paradigms are stimulating Telco providers to explore the possibility of breaking down the ossification of the current Internet. This paves the way for the introduction of new approaches like Software Defined Network (SDN) and Network Function Virtualization (NFV) into the next generation networks to provide their users with software defined networking services. This context paves the way to software defined services, dynamically allocating the network functions over the network nodes. In this paper we propose an analytical approach to support designers in the allocation of virtualized function over the network, taking into account both the functions requested by the traffic flows and the quality of service (QoS) that the network provides to them. The proposed analytical approach is able to account the routing, the processing capacities of the network nodes and the statistical characterization of the flows requesting each virtualization function. This paper follows the direction of analytically modeling a network with NFV capabilities for performance evaluation and design support.

In the last decade, one of the main goals in wireless telecommunications has been to reduce energy consumption of mobile devices. However, making a network device green can cause performance deterioration. The target of this paper is to propose a cross-layer approach for the design of a mobile video cloud for the uplink transmission towards the Internet. The proposed approach is adaptive in both the video sources and the wireless transmitter. A source Rate Controller is applied to compensate transmission bandwidth reduction due to the energy saving policies. Energy saving in wireless transmission on the mobile cloud cellular channel is achieved by introducing an energy-efficient ARQ protocol. This protocol can apply different transmission laws, in order to exploit the correlation of the cellular channel behavior. An analytical model of the system is defined to compare the transmission laws, and provide some design guidelines to choose one of them and design its parameters.

In the last few years, the proliferation of mobile devices coupled with the ever-increasing popularity of multimedia applications, has stimulated great interest of new figures of service providers. The major objective is to offer mobile users, located in limited areas, with broadband multimedia applications enriched with services and functionalities specific to mobile scenarios. This is the case, for example, of passengers waiting to board in an airport, visitors to a museum, or spectators at a football stadium. Since cellular networks usually cannot deliver bitrates suitable for such kinds of applications to every single user, the most appealing solution for providing mobile users with multimedia applications is to organize users into mobile clouds. In this context, this paper considers delay-constrained multimedia streaming applications over mobile clouds, and defines a business model for managing these kinds of services. Users are divided into two classes, according to the way they intend to pay for the service: in bandwidth and energy for traffic relaying (cheap-tariff) or with some money (full-tariff). An analytical model is proposed for designing the main system parameters and deciding on the tariffs of the business model. Finally, the business model is applied to a case study.

In the last few years, the ever increasing attention to the eco-sustainability of Internet has pointed out the need of providing advanced power management schemes in network devices, in order to be able to reduce energy consumption according to the traffic load. In this context, starting from the observation that most of the traffic of today's Internet is carried by TCP, we focused on the impact that the dynamics of the TCP congestion control may have on energy efficiency of end hosts. In this paper we demonstrate that great energy consumption is related to network congestion conditions. Moreover, we propose to use a technique, called Active Window Management (AWM), with the aim of driving TCP sending rate according to the network available bandwidth, so improving congestion control and providing high TCP performance. AWM requires no changes to any TCP version. We demonstrate that the TCP traffic shaping performed by AWM allows a great reduction of the energy consumption in end hosts, with respect to the case where only TCP is used as congestion control.

One of the most weighty matter in the Internet today is the waste of energy due to the fact that consumption of network nodes is not tuned with the input traffic. For this reason, the implementation of rate adaptation facilities in the routers constitutes a challenging problem to make the network energy efficient. Rate adaptation in the green routers is usually achieved by scaling the processing power according to the data rate the router has to manage; at this purpose the clock frequency driving the router processes can be modified according to the input data rate. In this context this paper, starting from a measurement study of the Reference Router implemented on the NetFPGA platform, defines a model of the consumed power as a function of both the managed input traffic and the available clock frequencies. As demonstrated in the paper, the model can be applied by router designers to choose the main router platform parameters, i.e. the number of clock frequencies and the clock frequency switching time, while respecting a given tradeoff between the percentage of energy saved and the maximum tolerated loss probability due to frequency switch.

The reference router implementation on the Net FPGA platform has been changed in order to hijack the incoming packets according to rules specified by the user through Net FPGA registers. This means that we are able to change any field of any incoming packets, of course, depending on whether we are changing TCP or IP header fields, we need to recompute the TCP or IP checksum and store them back into the packets. It might be useful to change information of the IP or TCP header or the data itself. For example, we could crypto the data, change the URL address in order to point to the desired web site or for URL filtering, change the priority information of a bunch of data, and much more. Our implementation works at user data path level and modifies packet fields if certain conditions defined by the user through Net FPGA registers are satisfied. The project has been implemented as a fully open-source project and serves as an exemplar project on how to build and distribute Net FPGA applications. All the code (Verilog, system software, graphical user interface, verification scripts, make files, and support tools) can be freely downloaded from the Net FPGA.org website.

A traffic monitor system has been implemented on the NetFPGA. The NetFPGA is an open networking platform accelerator that enables researchers and instructors to build working prototypes of high-speed, hardware-accelerated networking systems. The traffic monitor application allows network packets to be captured and analyzed from up to all four of the Gigabit Ethernet ports. A graphical user interface showing the traffic of any port has been implemented on top of it. The project has been implemented as a fully open-source project and serves as an exemplar project on how to build and distribute NetFPGA applications. All the code (Verilog, hardware, system software, verification scripts, makefiles, and support tools) can be freely downloaded from the NetFPGA.org website. System performance has been compared with other two implementations: one using the same NetFPGA architecture but implementing the reference router with port mirroring, and the other being a software implementation built on top of the Click Modular Router.

Improving video streaming performance is a main target today for the development of services and related business opportunities in the Internet. For this reason the definition of an efficient video transport paradigm is yet a challenging task. The most common protocol for data transmission in the Internet is TCP but it has been widely believed not suitable for multimedia streaming because of bandwidth instability and retransmissions. However, the dominance of TCP makes desirable the possibility to use it without any modifications. The main idea of this paper is to improve the performance of video streaming by using TCP, by combining it with Active Window Management (AWM), an algorithm to be implemented by video content Providers in their Internet access router. Through an extensive analysis the paper demonstrates that thanks to AWM, packet losses are avoided while per-packet delivery delay is maintained almost constant, strongly decreasing transmission jitter; consequently the required video playout buffer can be substantially reduced with respect to the classical approaches.

New service providers, completely separated from the Internet Service Providers (ISP), are growing today in the Internet scenario to create new multimedia services through the P2P approach. These providers are manifesting great interest not only for classical multimedia applications, like video streaming and video on demand, but also for other applications, like for example video-conferencing, multi-party games, private video-chat rooms, and real-time video teaching for e-learning, that usually contain a small number of group members, and the groups are often created and destroyed relatively dynamically. For this kind of applications, use of the P2P approach constitutes a challenging research activity in the immediate future. In addition, new business models are needed to support the economical growth of these kinds of providers. An idea at the basis of the paper is that users are divided in two classes, according to the way they pay the service: in bandwidth (cheap-tariff peers), or with some money (full-tariff peers). The target of this paper is to define a revenue model for providers of services of a new P2P-based platform for multipoint communications for small groups of high-interactive users.

Addressing performance degradations in end-to-end congestion control has been one of the most active research areas in the last decade. A huge number of techniques have been proposed in the past, but some of them miss the challenging target of both minimizing network delay and keeping goodput close to the network capacity (e.g., active queue management (AQM) techniques); others require changes in both network routers and hosts that make them difficult to deploy [e.g., eXplicit Control Protocol (XCP)]. In a previous paper, a novel mechanism, called Active Window Management (AWM), has been proposed by the same authors with the aim of controlling the queue length in network routers, maintaining it almost constant to achieve no loss, while maximizing network utilization. The target of this paper is twofold: 1) carrying out a deep analysis of the AWM algorithm stability to evaluate the impact of some system parameters on the performance of the whole system; 2) demonstrating that AWM can be gradually introduced, maintaining the compatibility with the current Internet. An extensive numerical analysis is proposed to demonstrate that AWM stabilizes the queue around a target value with small oscillations.

E-learning is a general term used to refer to computer-enhanced learning. The most notable advantages of e-learning are flexibility, convenience and the ability to work at our own pace. Other advantages include the ability to communicate with fellow classmates from around the country. However, a common problem of usual e-learning approach is the lack of face-to-face interaction with a teacher. The most obvious solution to overcome this problem is to include a real-time multipoint audio-video channel to provide synchronous communications between teacher and students. This paper starts from an implementation experience of an e-learning platform for synchronous interaction between the actors, keeping "low-cost" as the main requirement of the deployment, to give the possibility to any user to participate at home using a domestic Internet access and a base-level personal computer. This paper proposes a software-based platform to provide multipoint communications in a heterogeneous unicast/multicast environment. It is constituted by a number of S-MCUs (software MCUs). Each S-MCU runs on a low-cost computer and is implemented like a software router by using click. Design, implementation and performance issues are discussed by a comparative numerical analysis.

Performance analysis frameworks for P2P networks are in great demand by Internet service providers (ISP) and telecommunications companies in order to re-design their systems so as to support the new paradigm. The target of this paper is to propose an analytical model of a real-time video transmission system using P2P to achieve multipoint communication on the current Internet. The system considered is defined in such a way as to maximize the number of users, while guaranteeing a given quality of service level in terms of the maximum number of hops connecting them with the video source.

Peer-to-peer (P2P) networks are emerging as an interesting alternative to media streaming delivery with respect to content delivery networks (CDN). Although several P2P protocols have been proposed to support video streaming with an adequate degree of quality of service (QoS), a critical point today is how to evaluate them and optimize their parameters. The difficulty lies in the fact that the behavior of the P2P overlay network and the one of the underlying packet network are closely related and need to be analyzed simultaneously. Ad-hoc simulators for each P2P architecture have been proposed but they neglect the interactions between peers and the underlying network. This paper proposes a new approach to P2P video streaming performance analysis and system design which provides a very accurate cross-layer simulation of the real behavior of P2P clients over TCP/IP networks. A fluid-flow approach has been adopted to simulate the behavior of the network elements supporting an emulated overlay network consisting of real implementations of P2P clients. The P2P SplitStream video streaming protocol is considered as a case study to demonstrate that the tool is able to capture performance parameters at both the overlay (e.g. inter description synchronization) and packet network levels (e.g. traffic source round-trip time and router queue behavior).

Many techniques have been proposed in the last few years to address performance degradations in end-to-end congestion control. Although these techniques require parameter tuning to operate in different congestion scenarios, they miss the challenging target of both minimizing network delay and keeping goodput close to the network capacity. In this paper we propose a new mechanism, called Active Window Management (AWM), which addresses these targets by stabilizing the queue length in the network gateways. AWM acts on the Advertised Window parameter in the TCP segment carrying the acknowledge, but it does not affect the TCP protocol. The proposed technique is implemented in the network access gateways, that is, in the gateways through which both the incoming and outgoing packets related to a given TCP connection are forced to go, whatever the routing strategy used in the network. We show that when the access gateways implementing AWM are the bottleneck in the networks, TCP performance is very close to that of a pseudo constant bit rate protocol providing no loss, while network utilization is close to one.

UMTS is designed to offer high bandwidth radio access with QoS assurances for multimedia communications. In particular, real-time video communications services are expected to become a successful experience under UMTS networks. In this context, a video transmission service can be designed over the basis that UMTS can provide either a constant bit rate data channel or a dynamic variable bit rate data channel adapted to load conditions. In this latter approach, which is more efficient for both the user and the service provider, multimedia sources have to be timely designed in order to adapt their output rate to the instantaneous allowed channel rate. The target of this paper is to define an analytical model of adaptive real-time video sources in a UMTS network where system resources are dynamically shared among active users.

In this paper we propose a Markovian model of BitTorrent. Unlike already developed works which capture demographic dynamics, it focuses on the behavior of individual peers. To this end, we center our attention on a generic peer, called tagged peer (TP); for each possible logical state of a BT peer-to-peer connection maintained by the TP, we consider a stochastic process which counts the number of such links, and characterize them according to their state. Validation is carried out and steady-state analysis is performed in order to illustrate how performance evaluation can be extracted from our model

In recent years sensor networks have gained a lot of interest for industrial applications. The target of this paper is to define an analytical framework to evaluate the transient behavior of a sensor network in which sensors implement the energy-saving technique of idle/active states. The model captures the behavior of the battery lifetime of each sensor, and is able to evaluate the distribution of the lifetime of the whole network

In the current Internet scenario increasing interest is being shown in quality of service (QoS) issues, so simulation and parameter tuning are becoming very hot topics. In this paper we present a user-friendly, easily expandable and time-saving tool, which is able to perform network simulation through fluid-flow models and parameter tuning with parallel genetic algorithms. All the network elements involved (TCP sources, links, routers and so on) are modeled with the fluid-flow approach as independent systems: their outputs only depend on their inputs and internal state variables. Network elements can be connected to each other by a powerful graphical interface. Subsequently the user can select network parameters to be optimized in order to achieve prearranged QoS guarantees. During this phase the tool adopts a parallel implementation of a genetic algorithm (GA) which reduces the execution time required for optimization. A case study is also presented.

With the advent of multimedia traffic over the Internet several TCP-friendly protocols have been developed to improve UDP with congestion control mechanisms. The most promising TCP-friendly protocol is the TFRC. This paper addresses a novel analytical model for TFRC traffic sources. The model is able to capture TFRC behavior and its dependence on the network congestion state. Moreover, the model includes the TFRC loss probability estimation algorithm performed by the receiver. In order to keep the model manageable, whatever the number of sources and network routers, a fluid-flow approach is used.

BitTorrent is today one of the largest P2P systems which allows file sharing for Internet users. Very little effort has been dedicated to this target up to now. The goal of this paper is to develop an analytical model of a free-rider in a BitTorrent network. Unlike previous analytical models which capture the behavior of the network as a whole, the proposed model is able to analyze the performance from the user perspective. The model is applied to a case study to evaluate performance in a real case, and to obtain some insights into the influence of BitTorrent parameters on system performance.

Adaptive rate video encoding is required to maximize efficiency when wireless links are involved in the communication. In fact, wireless channels are characterized by high, time-varying bit error rates. To cope efficiently with this problem adaptive forward error correction schemes have been proposed. These schemes introduce an amount of redundancy dependent on the channel conditions. Accordingly, the bandwidth available at the application layer changes: it increases when channel conditions improve, and decreases when channel conditions worsen. Obviously, the encoding parameters must be tuned to adapt the video source transmission rate to the available bandwidth. This adaptation is achieved by means of appropriate feedback laws, which are relationships between the encoding parameters to be used and other variables representing the state of the system. An analytical framework is introduced which can be used for the design of the feedback laws. To this purpose both the channel and the video source are modeled by means of Markov models. The resulting model of the whole system is denoted as SBBP/SBBP/1/K. Analysis is derived which allows to evaluate the most significant performance measures and, therefore, to design optimal feedback laws.

Wireless channels are characterized by high time-varying bit-error rates (BERs). To cope with this problem, several adaptive forward-error-correction (AFEC) schemes have been proposed in the literature. They work locally at the wireless link, adding a variable amount of redundancy to the transmitted data in order to maintain the packet error rate below an acceptable level. However, when such schemes are utilized, the bandwidth offered to the applications changes when channel conditions change. In this paper, the effects of these bandwidth variations are investigated in the case of real-time Motion Picture Experts Group (MPEG) video transmission. The MPEG encoder is controlled in order to adapt its emission rate to the current bandwidth offered by the wireless link. To this end, the encoding quality is diminished by the source rate controller when the transmission rate has to be decreased due to an increase in the channel BER, whereas it is improved when the transmission rate can be increased due to a decrease in the channel BER. A Markov-based model, denoted as SBBP/SBBP/1/K, has been introduced to model the scenario being considered. The analytical framework allows evaluation of the performance of the system and can be used to optimize the design of a video transmission system for wireless channels, providing the instruments to derive the tradeoff between information corruption in the wireless channel and MPEG video encoding quality.

One of the challenges today for wireless network operators is to find techniques which will make it possible to introduce multimedia capabilities into mobile communications. The application of a wireless network structure made up of pico-cells to solve this problem will cause an increase in the handover rate. Therefore, a major requirement in a wireless environment is to design an effective call admission control (CAC) strategy to minimize the handover drop probability. Another important requirement is to maximize network utilization in terms of the mean number of channels used. The goal of this paper is to define a resource management strategy for heterogeneous adaptive-rate traffic. The proposed strategy is actually a combination of two: a CAC management strategy and a bandwidth management one. The CAC management strategy extends the Guard Channel strategy to obtain priority-based CAC management for adaptive-rate sources; the bandwidth management strategy allows channels to be assigned proportionally to the throughput window declared by users. In order to assess the effectiveness of the proposed CAC strategy, two Markov models of the system are introduced: one, from the network point of view, to evaluate global system performance, and another, from the user point of view, to evaluate the performance each user is provided with by the network. Numerical examples conclude the paper to analyze which factors most affect performance when the source rate is adaptive and the proposed admission strategy is applied.

In this paper, a hierarchical paradigm modeling both the intramedia and intermedia time relationships of multimedia sources is derived. Each multimedia source is considered as being made up of a master monomedia source, which is independent, and slave monomedia sources, which are dependent on the master. The paradigm takes different timescale statistics into account and is used to characterize intermedia relationships in terms of the second-order statistics of the multimedia source. Finally, the impact of intermedia relationships on the performance of each monomedia source making up a multimedia source is analyzed when a number of multimedia sources are multiplexed together. The error introduced in both loss and jitter performance analysis when intermedia relationships are not modeled is evaluated through a case study. Numerical results show that this error may be dramatically significant in many common scenarios.

This paper demonstrates that higher network resource efficiency can be achieved by using resource management protocols which consider service disciplines based on service curves together with statistical traffic modeling. To this end, an appropriate analytical framework is introduced which allows calculation of the performance statistically guaranteed to any flow out of an aggregate. This feature enables the analytical framework to be applied to the elements of the core network where aggregates of traffic are considered instead of single flows in order to avoid scalability problems. Given that flows are modeled in the analytical framework through switched batch Bernoulli processes (SBBPs), the whole queueing system is denoted as SBBP/Sc/1/K. The performance is calculated in terms of loss probability and delay distribution. The proposed framework is applied in a significant multinode case study.

Promoting the evolution of the Internet from a simple data network to a true multiservice network constitutes a challenging task. To this end, the Internet Engineering Task Force (IETF) has set up the Integrated Services (IntServ) and Differentiated Services (DiffServ) Working Groups, with the goal of defining a next-generation Internet, in which traditional best-effort datagram delivery and additional enhanced quality of service delivery classes coexist. The IntServ framework, in particular, is designed to be used in the access network, and requires a traffic source with the capability of matching the traffic characteristics declared to the network, TSpec. We propose to use the rate control facility, usually implemented in current MPEG encoders to provide a constant bit rate, to shape the output traffic according to the declared TSpec while maintaining an acceptable perceived image quality. In order to assess this scenario, we introduce an SBBP/D/N/K queueing system, where the SBBP (switched batch Bernoulli process) emission is varied according to the quantizer scale parameter chosen by the addressed rate control mechanism. The analytical framework allows us: 1) to evaluate system performance in terms of both the marking probability of nonconforming output traffic and the quantization distortion introduced by the encoder; 2) to choose the TSpec parameters to be declared such that given performance parameters are respected.

In the near future, congestion control should be introduced for multimedia UDP-based traffic, in such a way that this traffic becomes "TCP-friendly". To this end, several TCP-friendly algorithms have been proposed in the literature. However, although these algorithms were introduced to support real-time applications on the Internet, the only target in optimizing them, until now, was to achieve fairness with TCP flows in the network. No attention has been paid to the quality of service (QoS) perceived by their users. The target of this paper is to analyze the problem of transmitting voice over IP when voice sources use two of the most promising TCP-friendly algorithms, RAP (rate adaptation protocol) and TFRC (TCP-friendly rate control). With this aim, a VoIP system architecture is introduced and the characteristics of each of its elements are discussed. To optimize the system, a voice multirate encoder, which is able to work over a TCP layer, is used, and a modification of both RAP and TFRC is proposed. Finally, in order to analyze the performance of the proposed system architecture and to compare the modified RAP and TFRC with the original algorithms, the sources have been modeled with an arrival process modulated by a Markov chain, and the model has been used to generate traffic in a simulation study performed with the ns-2 network simulator.

All real-time applications at present transmit via the UDP transport protocol. This creates a problem of fairness between TCP and UDP flows: in the presence of a high network load and congestion, TCP sources reduce their output traffic while UDP sources continue to transmit in the same way, as if the network were underloaded. In this way, UDP sources monopolize the available bandwidth, strongly penalizing TCP sources. A solution to this problem consists of introducing a congestion control algorithm for UDP sources as well; one of the best known TCP-friendly rate-control algorithms is the rate adaptation protocol (RAP). The target of this paper is twofold: firstly it investigates fairness between TCP and RAP protocols in a real Internet scenario where routers apply a random early detection (RED) active queue management policy; secondly, a set of guidelines for design of RED router parameters in such a way as to optimize fairness is provided.

The MPEG standard is the most popular technique in video and audio encoding. However, when MPEG encoding is used to transmit video information, one intrinsic drawback is its high burstiness. A large number of proposed solutions to this problem implement encoding control schemes based mainly on on-line adjusting of the encoder quantizer level through the quantization parameter. However, the choice of the feedback law is still a challenge to make MPEG encoding suitable for transmission over the forthcoming guaranteed service scenarios, where a negotiated bandwidth profile is available for video transmission. Up to now, this problem has only been approached by using simulation; no analytical framework exists yet which allows designers to study the most effective feedback law for transmitting video in the network environment to be used. This paper introduces an analytical paradigm modeling a rate-controlled MPEG encoding system in which a feedback makes the quantizer scale parameter dependent on both the state of the output counter/buffer in the controller and the activity level of the frame to be encoded. To this end, an SBBP/D/N/K queuing system is used, where the SBBP (switched batch Bernoulli process) arrival process is varied according to the quantizer scale parameter chosen by the addressed feedback law at the frame level. The analytical framework allows us to evaluate system performance in terms of both the distortion introduced by the encoding system and the resulting output process statistics; it is general enough to be applied whatever the feedback law is, provided that the parameter to be varied is the quantizer scale parameter. A case study is introduced to assess the proposed framework.

Guarantees of quality-of-service (QoS) in the real-time transmission of stored video on the Internet is a challenging task for the success of many video on demand (VoD) applications. Two QoS classes have been specified by the IETF Integrated Services (intserv) Working Group: Guaranteed Services and Controlled-Load Services. For both of them, it is necessary to provide traffic sources with the capability of calculating the traffic characteristics to be declared to the network, Tspec, on the basis of a limited set of parameters statistically characterizing the traffic and the required level of QoS. The target of this paper is to develop an algorithm for the evaluation of the Tspec parameters which characterize the video stream when a given QoS is required. To this end an analytical framework modeling an MPEG stored-video server and the access network node is introduced. The video emission process is modeled with a switched batch Bernoulli process (SBBP), and performance in the video-server smoother is analytically evaluated. Then the token bucket at the network access point, loaded by the output traffic of the video-server smoother, is modeled to calculate the probability of marking nonconforming data packets.

Design of network devices requires accurate models for network traffic. The main difficulty lies in determination of the traffic statistics which effectively influence the network performance, and in construction of a tractable and easy analytical model capturing these statistics. This paper demonstrates that, in order to evaluate performance of a multiplexer in a discrete-time environment, it is necessary to define a model which is able to capture the probability density function, autocovariance function, and the overload probability of the input traffic, simultaneously. The paper also provide an algorithm for the construction of an SBBP model matching these statistics.

The "new Internet" will allow individual application sessions to request different QoS parameters to the network. Two QoS classes have been specified by the IETF: guaranteed services and controlled-load services. For both of them, it is necessary to provide users with the capability of calculating the traffic characteristics, Tspec, to be declared to the network, on the basis of a limited set of parameters statistically characterizing the data source and the required level of QoS. The target of this paper is to realize an analytical framework modeling the source and the access network nodes, with the purpose of calculating the Tspec parameters for MPEG video traffic. To this end the video source is modeled with a switched batch Bernoulli process (SBBP), and performance in the source smoother is analytically evaluated. Then the token bucket at the network access point, loaded by the output traffic of the smoother, is modeled to calculate the probability of marking nonconforming data packets.

The enormous increase in wireless communications has led to the need to configure wireless access networks with cellular structures based on the use of micro- and picocells. A key issue in connection with this is the rerouting of the data flow directed to a user making a handover. The paper considers one of the most widely-used connectionless protocols, the signaling network layer (SNL), which is based on a tree-structured routing architecture. Through an analytical model based on a queueing network, its performance is analyzed in terms of the distribution of the rerouting delay. The proposed model is then applied to a case study to demonstrate its versatility and manageability and to get important insights to the dimensioning of the routing architecture.

Multimode coders are able to exploit the different characteristics of the speech waveform and to take into account the different peculiarities of background noise, thus allowing improvements in both signal reconstruction and network-offered load. In this context the variable rate code excited linear prediction (VR-CELP) coding, that is, a multimode variable bit rate (VBR) coding based on the CELP technique, has been introduced in the literature and is currently being considered for use in various applications, especially in the third-generation UMTS cellular systems. The target of the paper is to introduce an efficient and accurate framework allowing a network designer to analyze the impact of multimode VBR speech coding on the quality of service (QoS) provided by a wireless/wired ATM network. In order to capture the coder output characteristics, we propose to model a VR-CELP voice source by using a switched batch Bernoulli process (SBBP). More specifically, three models are introduced and compared in terms of accuracy and simplicity in determining network performance. As a result of the comparison, a four-state model has been chosen as the best tradeoff. The model is then used to analytically derive the loss probability and the jitter probability density function of an ATM multiplexer loaded by a number of VR-CELP sources. Finally, the proposed paradigm has been assessed in a case study where we demonstrate that, for a given output ATM link capacity and for a number of telecommunication services involving voice transmission, VR-CELP coding performs better than traditional on-off coding.

One of the most important quality-of-service parameters in a multimedia environment is skew, defined as the difference between the delays suffered by the monomedia flows belonging to the same multimedia stream. An analytical paradigm is proposed to evaluate the skew affecting a multimedia traffic stream in an asynchronous transfer mode multiplexer. For this purpose, the emission process of each multimedia source loading the multiplexer is defined as the superposition of heterogeneous correlated emission processes, each of which models one monomedia source as a switched batch Bernoulli process (SBBP). In order to model the intermedia relationships, the transition probabilities in the Markov chain underlying each SBBP are functions of the state of the other monomedia sources. The model is applied to a case study and the dependence of skew performance on some of the source characteristics, such as intermedia correlation, and some of the environment characteristics, such as buffer size, output capacity, and buffer utilization is analyzed and discussed.

Thanks to the ever growing diffusion of distributed multimedia applications, VBR compressed video traffic is expected to constitute the majority of the B-ISDN load in the immediate future. At the moment, the MPEG coding scheme is widely used for many types of applications. Therefore, it is crucial to obtain a model for the generation of sequences with the same statistical characteristics of real MPEG traces. In this paper, after demonstrating that intra-GoP (group of pictures) correlation has to be taken into account, an accurate model allowing the generation of an MPEG-like trace is proposed. Moreover, as the MPEG traffic has been demonstrated to be self-similar, the proposed algorithm allows one to generate a trace, fitting the short-range dependence (SRD) and long-range dependence (LRD) behavior and the probability density function of any given MPEG sequence.

Flow theory is a rich, effective theory introduced in order to study real-time network protocols. It is based on discrete mathematics in which a flow of data is represented by an infinite sequence of numbers and is characterized by its upper bounds. Unfortunately up to now flow theory has used a traffic characterization that in some cases can be too loose, thus causing an inefficient network utilization. In this paper we enhance flow theory by providing it with a more accurate traffic characterization and with all the tools necessary to deal with it. As we will see, this enhancement provide a more accurate performance evaluation and thus better network utilization.

An accurate and treatable Markov-based model for MPEG video traffic is proposed. Before reaching a definition of the model, a large number of MPEG video sequences are analyzed and their statistical characteristics are highlighted. Besides the well-known autocorrelation function, testifying the pseudo-periodicity of the sequence in the short term, and the gamma-shaped probability density function the correlation between different frames belonging to the same group of pictures (GOP) is also accurately studied. Then a structured model is proposed. Thanks to its two-level structure it is able to capture both the inter-GOP and the intra-GOP correlation. In order to obtain the first level of the model, the well-known inverse eigenvalue problem is solved in the discrete-time domain. Finally, the model is used to evaluate the loss probability in an ATM multiplexer loaded by an MPEG video source and an aggregate of external traffic. The accuracy of the model is evaluated by comparing the analytically obtained loss probability with the loss probability calculated by simulating the system using real traffic sequences as driven traces.

We propose an analytical model for the study of two different routing strategies in an ATM network supporting multimedia traffic flow: the multimedia virtual circuit (MVC) and the independent virtual circuit (IVC). The first strategy consists of assigning a single virtual channel to all of the monomedia streams which make up the multimedia flows; the second strategy consists of multiplexing homogeneous monomedia streams belonging to different multimedia sources on the same virtual channel. With this aim, a multimedia source is modeled as an arrival process defined as the superposition of heterogeneous correlated arrival processes, each of which models one monomedia source. In order to take into account the intermedia relationships which exist in a multimedia stream, each monomedia source is modeled as an interrupted Bernoulli process in which the transition and the arrival probabilities are functions of the states of the other monomedia sources. A finite-buffer discrete-time approach is used in order to compare MVC and IVC performance when an aggregate of N heterogeneous multimedia sources loads the network. Performance is evaluated for each monomedia source in terms of loss probability and jitter probability density function. To assess the proposed paradigm, a case study is shown.

The skew affecting a multimedia traffic stream when N multimedia streams are mixed in an ATM multiplexer is evaluated. To this purpose a multimedia source is modeled by an arrival process defined as the superposition of heterogeneous correlated arrival processes, each of which models one monomedia source by means of an Markov modulated Bernoulli process (MMBP) process whose transition probabilities are functions of the state of the other monomedia sources.