This paper presents DroneOPERA, an ns-3 simulation module to model the energy consumption of on-board computing in energy-constrained unmanned aerial vehicles (UAVs) operating in Internet of Drones (IoD) networks. As drone networks are increasingly integrated with IoT and cloud services, energy-efficient processing becomes a key challenge, especially in applications requiring real-time analytics under strict power budgets. DroneOPERA extends ns-3 with a configurable model that couples UAV mobility, battery dynamics, and CPU workloads associated with on-board tasks. By processing data locally instead of offloading raw information to external servers, DroneOPERA enables the study of privacy-preserving mission designs, where communication is limited to compact, processed results. The module exposes APIs to define mission profiles, processing workloads, and hardware configurations, and computes the resulting energy expenditure at fine time granularity. We demonstrate the capabilities of DroneOPERA through a representative use case, in an agriculture scenario, highlighting the trade-offs between task complexity, flight time, and privacy-preserving on-board processing.

Modern Software-Defined Wide Area Network (SDWAN) deployments are required to manage traffic over heterogeneous underlay networks while meeting stringent Quality of Service (QoS) requirements. In scenarios where multiple branches share overlay resources, independent tunnel selection decisions often lead to congestion and degraded performance. Existing approaches lack coordination mechanisms to handle the dynamic interactions between agents competing for shared resources. This paper presents a Multi-Agent Reinforcement Learning (MARL) framework for distributed overlay selection in SD-WANs. Each branch is modeled as an autonomous agent that learns routing policies through interaction with the network environment. To account for the mutual impact of decisions across branches, we adopt the Centralized Training with Decentralized Execution (CTDE) paradigm, enabling agents to learn globally consistent behaviors while preserving scalability at inference. To encourage cooperative policies, we introduce a $\lambda$-weighted reward shaping mechanism that balances local QoS goals with global resource fairness. We evaluate our approach using both PPO and DQN algorithms in a simulated SD-WAN environment. The findings highlight the necessity of MARL in addressing resource contention and ensuring equitable shared overlay utilization.

In rural areas such as national parks, forests, and mountains, recognizing and classifying wildlife is crucial for monitoring animals that may pose risks to crops and human safety. Deep learning provides the most accurate approach due to its dynamic adaptability. However, it is also highly energy intensive. Given the constraints of rural environments, including the use of extreme edge devices and limited power availability, this study employs distributed computing to maximize battery life while enabling the execution of DL tasks. The core concept is Split Computing, specifically applied to the YOLOv8m and YOLOv10 models, which have been provided by researchers as a highly effective solution for wildlife classification. This approach dynamically reallocates different model components across available boards, specifically the NVIDIA Jetson Orin Nano, to optimize energy consumption. This research investigates various model distribution configurations across the head, backbone, and neck components, assessing whether video stream compression between the camera and the computing boards affects energy consumption and network load. Our findings suggest that the choice of model splitting configuration significantly impacts energy efficiency, bandwidth consumption, and computational load distribution.

The integration of machine learning on microcontrollers at the extreme edge of the network is gaining significance for various Internet of Things applications. This paradigm shift enables real-time data processing and decisionmaking directly on the devices, thereby reducing latency and reliance on cloud connectivity. Nevertheless, due to the computational and memory limitations of microcontrollers, training and deploying a neural network using existing libraries such as TinyML remains impractical for some devices. This paper proposes an approach where the model is trained in the cloud, with only the forward phase directly implemented on the microcontroller. We leverage the LittleBits platform for its user-friendly interface and suitability for educational purposes, aiming to introduce machine learning concepts to non-expert users. Our case study focuses on temperature prediction in smart greenhouses, demonstrating the practical utility of machine learning in timely interventions for plant disease prevention and optimal growth conditions. This work not only illustrates the feasibility of deploying machine learning on resource-constrained devices but also emphasizes the potential of LittleBits in making novel technologies accessible and comprehensible to a broader audience.

The digital divide remains one of the biggest challenges of the modern era, limiting access to information and global connectivity for populations in remote or rural regions. The doctoral research goes in the direction of an integrated approach to address the divide in the view of 6G. In particular, the integration of Non-Terrestrial Networks (NTN) and Terrestrial Networks (TN) is envisioned as a key part of the future 6G systems. Exploiting the Software-Defined Wide Area Network (SD-WAN) technology, we aim to propose an architecture for an adaptive and resilient network that can integrate satellite technologies such as Low Earth Orbit (LEO) satellite networks. Moreover, we present some results of using Reinforcement Learning (RL) in the tunnel selection problem for SD-WAN. In order to assess the performance of the LEO satellite, we are also collecting and analyzing real measurements using a Starlink connection. Finally, we propose some open questions in the field of integrating NTN/TN with SD-WAN.

Smart agriculture is experiencing rapid transformation through innovative Agritech solutions. Robotic Arms (RAs) and Sensor-equipped Wearable Gloves (SWGs) are critical tools for performing high-precision tasks in controlled environments like hydroponic greenhouses. Digital Twin (DT) technology further enhances these systems, enabling real-time monitoring, dynamic adaptation, and predictive maintenance, thus ensuring optimal efficiency and cost savings. This paper investigates the integration of DT technology into a hydroponic greenhouse to facilitate remote control of a RA using a SWG. We have developed a DT framework that synchronizes physical and digital environments for real-time motion planning and monitoring. Our system architecture aims to demonstrate the potential of DTs in agriculture, allowing tasks to be performed in three modes: Real-to-Digital (updating the DT with real-world data), Digital-to-Real (executing commands from the digital model in the physical world), and Digital-to-Digital (simulating scenarios to optimize performance). Experimental validation highlights the system's performance, focusing on communication and computing delays and their impact on real-time operations.

Climate change poses significant challenges, particularly in agriculture, where extreme weather events demand more efficient and resilient systems, such as smart greenhouses. These controlled environments require predictive solutions to optimize conditions such as temperature and humidity, which are critical for crop growth. While neural networks are widely employed for climate prediction, their complexity presents a barrier to implementation on edge devices, which are characterized by limited computational resources. In this work, we propose Fuzzy-Augmented Neural Network (FANN), a novel approach based on fuzzy sets, applied in cascade to regressive neural network models, to reduce complexity and energy consumption in greenhouse microclimate classification and prediction. The methodology was tested on four edge devices, including microcontrollers and microprocessors. We compare our FANN approach with standard models (FFNN, BNN, SimpleRNN, GRU, LSTM), highlighting significant reductions in inference time, energy consumption, and memory usage. FANN also offers practical advantages, such as the ability to adapt classification by modifying fuzzification parameters without retraining the model, and the potential to parallelize computations for simultaneously classifying the microclimate of multiple crops. These features make the system flexible and optimal for practical applications in dynamic agricultural contexts.

Networked Music Performance (NMP) is one of the most challenging real-time applications in which musicians can play together using the internet without being physically together. Nowadays, the connection technology can have performance that guarantees an adequate Quality of Experience (QoE) for NMP. On the other hand, there are millions of musicians who live in remote and rural areas without access to high-speed network connections. The promising LEO satellite internet technology could fill this gap and democratize NMP in remote places. This paper aims to provide an analysis of Starlink’s capability to meet the stringent requirements of NMP. We present an analysis of network metrics that are relevant to NMP applications and assess the NMP software JackTrip in order to test a real NMP application with LEO satellite internet. The evaluation of the RTT, One-way delay, Packet loss, and jitter in different scenarios poses some challenges for the NMP scenario. We present the results of two live NMP jamming sessions with musicians, proposing an adapted QoE model that incorporates both objective and subjective metrics. With a tailored buffer strategy and the ability of a musician, we were able to obtain an adequate QoE, overcoming the challenges introduced by LEO satellite internet.

The agricultural sector is undergoing a significant transformation driven by technological advancements led by Agritech solutions. In this context, our study introduces the architecture of smart Hydroponic greenhouses. This work focuses on developing and testing a 3D sensing glove designed to empower users in remote control. Additionally, we developed a 3D model of the human hand to test the movement accuracy of the sensing glove. Latency measurements on serial, Bluetooth 4.0 interfaces and a proposed Network Architecture are conducted to assess real-time control performance. Our architectural proposal aims to control agricultural machinery seamlessly within hydroponic greenhouses, enhancing precision and user experience. The study lays the foundation for future advancements in remote agricultural operations and smart greenhouse management.

The continuous growth of advanced services integrating processing, storage, real-time data exchange, and transmission capacities highlights the importance of Quality of Service (QoS) and Quality of Experience (QoE). In particular, it is very relevant for enterprises that rely on Wide Area Networks (WANs) for reliable and efficient communication between their headquarters and branches. With the evolution of new applications with stringent requirements, ensuring high-performing WANs is a critical priority for businesses looking to remain competitive and provide a seamless customer experience. This paper explores using Rein-forcement Learning (RL) algorithms on Software-Defined Wide Area Network (SD-WAN) to improve QoS and reduce costs. SD-WAN allows for the dynamic reconfiguration of network devices in real-time, better meeting network measurements and service requirements. By leveraging self-learning techniques such as RL, which exploits feedback mechanisms, we can improve network availability by automatically routing traffic over existing network technologies. We also compare two SD-WAN topology scenarios, including direct WAN connections between Customer Premises Equipment (CPEs) within enterprise premises and CPEs used as peering points for traffic routing. Our approach shows promising results regarding network performance and cost-effectiveness, which can benefit businesses looking to improve their network infrastructure.

Recent impressive growth of AI applications in the most diversified heterogeneous domains is largely motivated by the availability of hardware accelerators used from the backstage of data centers (such as TPU, Tensor Processing Units, or VPUs, Visual Processing Units) to the far edge of embedded devices equipped with DPUs and Deep Learning Processing Units. High level toolchains for a more friendly usability of these platform had similar relevance in the process. In this paper we considered edge devices that provide an essential contribution for the deployment of “distributed intelligence” and are used typically at the gateway, CPE or Edge computing level. One of the typical assumptions is that Field Programmable Gate Array (FPGA) are far more expensive - with respect to power consumption - than legacy SBCs (single board computers). The main contribution of the paper is a fair comparison (at the same clock frequency and with the same main CPU) of processing time and power consumption of two different boards used for deep neural network classification. We will highlight the relevance of classification speed with respect to common KPIs adopted to compare the performances of automatic classification such as Loss, Precision, Recall, etc. This will be particularly relevant in the challenging domains of hardware accelerated real time control loops to provide distributed intelligence at the application level but also at the inner functions of emerging networking architectures.

Quality of Service (QoS) and Quality of Experience (QoE) are the most relevant requirements for new advanced services comprising integrated cooperation between processing, storage, sensing, and transmission capabilities. As reliance on technology grows, businesses increasingly turn to Wide Area Networks (WANs) connectivity solutions to ensure reliable and efficient communication between their locations and cloud data centers. With new applications with stringent requirements, QoS and QoE have become critical priorities for companies looking to remain competitive and provide a seamless customer experience. As a result, many organizations require reliable and high-performing WANs to effectively transmit critical data between their branches and cloud data centers. We examine Multi-Protocol Label Switching (MPLS technology), which is the historical choice for WANs but has significant disadvantages in cost and performance. Then, we analyze the novel Software-Defined Wide Area Network (SD-WAN) technology that allows for real-time flexible, dynamic reconfiguration of network devices to meet network measurements and service requirements. Furthermore, we introduce a first work of my Ph. D. that examines the use of a Reinforcement Learning algorithm in a new SD-WAN topology scenario in which we consider both direct WAN connections between Customer Premises Equipment (CPEs) located within enterprise premises and CPEs used as peering points for traffic routing. This paper presents our initial analysis of these technologies and the main ideas guiding me in my Ph. D. program.

Time-slotted channel hopping (TSCH) mode in the IEEE 802.15.4-2015 standard provides ultrahigh reliability and ultralow power consumption to sensor devices. The key feature of TSCH is the scheduling of time slots and frequencies, which falls outside the current standards. In this paper, we focus on throughput maximizing and max-min fair scheduling problems in a centralized TSCH networks. At first, a polynomial time algorithm for the throughput maximizing scheduling problem is proposed. We proceed to investigate and deliberate on some instances of the problem with their combinatorial properties. Second, a novel auction-based scheduling algorithm that uses a first-price sealed-bid auction mechanism is presented for the throughput maximizing problem. Simulation results show that the proposed algorithm obtains a close throughput performance to the optimal one obtained through CPLEX with a much lower complexity. Moreover, we propose a novel heuristic for the max-min fair scheduling problem and demonstrate its performance through extensive simulations in terms of the total throughput and fairness varying the number of nodes, frequencies, and antennas. Simulation results indicate the effectiveness of the proposed algorithm and its close performance to the optimal solution.

Due to the impressive demand for communication-intensive applications deployed in the cloud, cross-layer orchestration solutions for Cloud data centers (DCs) are essential for the effective usage of both network and cloud resources while addressing service requirements and user expectations. In this regard, the software-defined networking (SDN) can play a key role thanks to programmable network control operations and to a more effective inter-working between network controllers and cloud management platforms. On the other hand, due to the relevant size of Cloud DCs, a significant evaluation of SDN-based orchestration solutions requires testing environments with proper scales, and significant level of fidelity. This paper discusses the opportunities of using virtual testbeds to carry out Cloud-related experimentations while addressing with significant scale requirements of DC infrastructures. First, we present a set of results of a test campaign we carried out using the Fed4FIRE experimental facility aiming at a comprehensive performance evaluation of an SDN-based orchestration solution for Cloud DCs. Second, we provide a comparison between virtual testbeds and other testing environments, i.e., laboratory testbeds, simulators and emulators, in terms of offered advantages, and effectiveness in experimentations. Moreover, we discuss the appropriateness and advisability of using virtualized testbeds for research on novel SDN and Cloud orchestration solutions at scale, by highlighting advantages of virtual testbeds in comparison with laboratory, simulation and emulation testing facilities in terms of effectiveness of experimentations and accuracy of results.

We present an SDN-based orchestration system that enables automated and coordinated arrangement of both computing and network resources in virtualized DC environments. The orchestrator is validated through a set of simulations, as well as through experiments carried out in a real laboratory. Results show that the proposed orchestration system improves the service requests acceptance rate and the utilization of network resources, while minimizing degradations of the user experience. Thus, DC resources can be effectively exploited, and different management objectives (e.g., load balancing, energy saving) can be reached.

In the last years, Software Defined Networking has emerged as a promising paradigm to foster network innovation and address the issues coming from the ossification of the TCP/IP architecture. The clean separation between control and data plane, the definition of northbound and southbound interfaces are key features of the Software Defined Networking paradigm. Moreover, a centralised control plane allows network operators to deploy advanced control and management strategies. Effective traffic engineering and resources management policies allow to achieve a better utilisation of network resources and improve end-to-end service performance. This paper deals with the architectural design and experimental validation of a control application that enables differentiated routing for traffic flows belonging to different service classes. The new control application makes routing decisions leveraging on OpenFlow network statistics, i.e., taking advantage of real-time network status information. Moreover, a Deep Packet Inspection module has been developed and integrated in the control application to detect VoIP traffic with Session Initiation Protocol signalling, enforcing this way policies for a differentiated treatment of VoIP traffic. Finally, a functional validation is performed in emulated environment.

The constant growth in IT as well as the increase of users and services requirements are making communication networking more and more complex to manage. This is why there has been the need to look for new approaches. One very promising solution is Software Defined Networking (SDN) that decouples the data and control planes, having one centralized controller for the network. This gives chances to control and manage the network as desired, thus opening many new possibilities. In this work we chose to exploit SDN to enable QoS control, starting from existing tools (Floodlight) and making modifications in order to better exploit their potential. A new network control application for end-to-end QoS provisioning is built on top of Floodlight and validated in an emulated SDN environment.

Nowadays, Data Centers are the primary infrastructures for Cloud Computing services provisioning. In this challenging scenario, Data Centers have to deal with highly dynamic workloads, thus not only should they adopt advanced software virtualization solutions, but also network control capabilities. Elastic, agile network connectivity control should be integrated with the computing management infrastructure to make it achieve its goals. This paper presents an OF-based controller, called OFVN, that enables novel virtualization-aware networking functions in Cloud DCs. Virtual Machines allocations are performed considering the availability of computational resources on physical server, but also selecting a forwarding path for VMs data flows based on network links utilization. The focus of the paper is on the implementation and assessment of the controller in an experimental testbed. The behaviour of different allocation strategies is also investigated.

Oversubscription of intra-Data Center network links and high volatility of VM deployments require a flexible and agile control of Data Center network infrastructures, also integrated with computing and storage resources. In this scenario, the Software-Defined Network paradigm and, specifically, the OpenFlow protocol, opens up new opportunities for the design of innovative resource management platforms that enable dynamic and fine-grain control of DC networks through traffic engineering algorithms. This paper investigates the performance of two different sets of cloud-fluent traffic engineering algorithms. Conceived to work during cloud service deployments, the main target of such algorithms is to achieve a better utilization of network resources by exploiting OpenFlow capabilities for traffic-aware deployments of Virtual Machines. The effectiveness of the proposed solutions is evaluated in terms of network link utilization against VM requests acceptance ratio through simulations and experimental tests carried out by using an ad-hoc emulator.

This demo deals with a new orchestrator, the OFVN controller, specifically designed, according to the SDN principles, to deliver Cloud services with IT and network resources requirements.

Server virtualization enables dynamic workload management in data centers. However, some aspects of virtualization software technologies, like Virtual Machines (VMs) migration or communications between VMs and storage resources, can lead to huge and unbalanced utilization of the intra-data center network. In this paper, we investigate such issues in an experimental testbed, focusing on the measurement of the traffic overhead due to VMs migration in different operating conditions. Our measurement campaigns highlight that performance is strongly affected by several factors, such as VMs placement, VMs active memory, usage of Jumbo frames instead of Ethernet frames. We also analyze and compare the traffic exchanged between hosts on which VMs are placed, when two different storage systems (i.e., NFS and iSCSI) are used.

Today's e-Infrastructures are deployed to enable easy and secure access to heterogeneous resources (e.g., instrument, computing and storage elements) by exploiting cloud and grid technologies. Since e-Science applications are usually characterized by huge data transfers, user interactivity and high computational loads, the selection and allocation of cloud or grid resources dramatically affect their performance. Starting from a previous work by the same authors, this paper proposes a new distributed resource allocation algorithm, referred to as MRA3D-H, capable of handling multiple resource requirements for jobs/tasks that arrive to the computing environment of the e-Infrastructure. More specifically, MRA3D-H aims at finding a trade-off between execution time and cost of data-intensive applications by taking into account performance parameters at system and network levels as well as the economic cost of computational resources. Simulations have been carried out to compare the performance of MRA3D-H with other resource allocation algorithms in the DORII project environment.

The choice of the path computation algorithm is a key factor to design efficient traffic engineering strategies in multi-protocol label switching networks and different approaches have been proposed in the literature. The effectiveness of a path computation algorithm should be evaluated against its ability to optimise the utilisation of network resources as well as to satisfy both current and future label switched paths allocation requests. Although powerful and flexible simulation tools might be useful to assist a network manager in the selection of proper algorithms, state-of-the-art simulators and network planning tools do not currently offer a suitable support. This study deals with the design and performance evaluation of multi-constraints path computation algorithms. To this aim, ad hoc software modules have been developed and integrated within the MTENS simulator. New single-path and multi-path computation algorithms have been proposed and compared in terms of number of accepted requests, success probability, network resources utilisation and execution time. Finally, some guidelines and recommendations for the selection of path computation algorithms have also been provided.

Wavelength Routed Photonic (or all-optical) Networks (WRPNs) with an intelligent control plane will play a key role in providing the proper ground for future Internet applications. However, classic Routing and Wavelength Assignment (RWA) solutions are inadequate to be adopted by WRPNs. The main issues regard physical impairments and QoT, processing time and setup delay, lightpath (LP) survivability and minimization of resource allocation. In case of critical services provisioning, LPs must have tight QoT guarantees, even in case of network instability and failures. This work presents two new impairment-aware RWA (IA-RWA) algorithms, specifically tailored for the provisioning of critical services. These algorithms guarantee absolute bandwidth and Bit Error Rate (BER) for LPs, assuring survivability in case of single network failures. A high parallelizable, combined RWA engine minimizes resource utilization without increasing setup time. The performance of the proposed algorithms are evaluated with respect to a modified version of a previously proposed IA-RWA algorithm that takes into account QoT and survivability.

An e-Infrastructure allows end-user's applications to easily and securely access heterogeneous grid resources (e.g., computing and storage elements). Since e-Science applications are often characterized by huge data transfers and high computational loads, the selection and allocation of grid resources dramatically affect their performance. This paper proposes a distributed resource allocation algorithm, referred to as MRA3D, capable of handling multiple resource requirements for jobs/tasks submitted to the grid computing environment of the e-Infrastructure. More specifically, MRA3D aims at minimizing the execution time of data-intensive applications by taking into account performance metrics describing both system and connectivity status of the computational resources. Simulations have been carried out to compare the performance of MRA3D with other resource allocation algorithms in a realistic environment by using synthetic as well as real workload traces.

DiffServ-aware MPLS-TE (DS-TE) significantly enhances the MPLS-TE architecture by enabling bandwidth reservation on a per-class basis. More specifically, to this aim, the so-called Bandwidth Constraint Models (BCMs) are introduced as a key architectural element. This paper describes a new NS2 module that offers a complete support for the DS-TE architecture including the standard BCMs (MAM and RDM) and a new BCM (G-RDM), recently proposed by the authors. Moreover, the paper shows the results of simulations carried out to evaluate the behaviour of the above mentioned BCMs in a real network scenario.

Nowadays, many IP backbone networks adopt separate control and forwarding planes for unicast and multicast traffic flows. Indeed, while MPLS (multiprotocol label switching) is widely deployed for unicast traffic, IP multicast is the only available solution for the delivery of "one-to-many" traffic flows. With P2MP LSPs (point-to-multipoint label switched paths) support, a unified control and forwarding plane may be devised. Such a reduction in the number of protocols used in the core of the network as well as in the number of encapsulations in the data plane, results in simplified network operations. The paper discusses the design and the development of the control and data planes extensions needed to provide P2MP LSP support in an MPLS node. In particular, such extensions, concerning the P2MP LSPs path computation, the RSVP-TE signalling protocol, and the forwarding mechanism, have been implemented as new software modules for an ad-hoc developed simulator, based on NS2. Finally, tests have been performed to assess the behaviour of the new functionalities introduced in the simulator.

In the last years, broadband satellite networks have emerged as a flexible technology offering high-speed Internet access to large communities of users at relatively low-cost. At the same time, the large widespread of VoIP and Multimedia over IP (MoIP) applications, has pointed out the need of investigating their performance over heterogeneous networks and, more specifically, on wireless and satellite systems not offering Quality of Service (QoS) guarantee. The paper presents the results of an experimental study aimed at analyzing the behaviour of MoIP applications over a two-way satellite system. Starting from the most relevant performance metrics of a satellite network, the paper is focused on the evaluation of the Perceived Quality of Service (PQoS) when VoIP and MoIP applications are deployed over a satellite network. Control and data plane issues that may deteriorate the performance of such applications are highlighted.

In the last years, grid computing has emerged as a valuable service to solve complex computational problems in many scientific and industrial domains. Quality of Service (QoS) provision for these applications is therefore a key challenge for high speed Next Generation Networks and cross-layer mechanisms, enabling the development of network-aware grids, should be introduced. This paper takes into account, as a case study, an application for the simulation of particles coagulation phenomena. A parallel direct simulation Monte Carlo algorithm has been implemented and extensive computations have been performed in a cluster of computing nodes to evaluate the execution time of the application in different operating conditions. Then, data collected from these experiments, have been used as input for the design and deployment of a network-aware grid infrastructure. Since this application is characterized by frequent data exchanges, the impact of the allocated bandwidth on the overall application's performance has been investigated.

We propose a traffic engineering (TE) architecture to supply wireless mesh networks (WMNs) with an efficient support of diverse applications with different quality of service (QoS) requirements. The proposed TE-WMN architecture is based on the MPLS technology. Its main goal is to provide TE capabilities so that a heterogeneous infrastructure, made of wired networks and WMNs, can be built and managed in a seamless manner. In the TE-WMN architecture, the path computation element (PCE) is in charge of finding a path that satisfies the QoS requirements. This operation often requires solving a multi-constrained problem, which is known to be NP-complete. So far many approximated and heuristic solutions have been developed for and applied to wired networks. However, the different features of WMNs make it impractical to reuse those solutions. Hence we have devised two heuristic path computation algorithms (PCAs) with the goal of keeping the complexity very low and yet improving the performance of simple existing algorithms. An extensive set of tests has proved the good performance of the proposed PCAs.

Originally designed to improve the efficiency of packets forwarding, MPLS provides support for Traffic Engineering and network resilience. Constrained-based path computation is a key building block for Traffic Engineering in MPLS networks, since it allows to set-up LSPs along paths that satisfy QoS constraints. This paper deals with on-line and offline path computation algorithms and, more specifically, introduces and compares the performance of three novel offline path computation algorithms which aim at improving the performance of their standard version by means of some heuristics. All these algorithms have been developed in NS2 as an extension of OSPF-TE\ns and integrated with RSVP-TE\ns.

Global Grid Computing goal is to connect heterogeneous computational resources belonging to the same Virtual Organization (VO) through the Internet to form a single, more powerful virtual computer. However, to fulfill this goal it is necessary to develop services that provide the virtual computer with the same functionalities of individual end systems, such as security, interprocess communication, and resource management.

MPLS traffic engineering (TE) allows the creation of end-to-end paths across the network with bandwidth reservations. The main drawback of the basic MPLS-TE model is that it operates at an aggregate level and so it is unaware of traffic classes. DiffServ-aware MPLS-TE (DS- TE) refines the MPLS-TE model by allowing bandwidth reservations to be carried out on a per-class basis. The result is the ability to give strict QoS guarantees while optimizing the use of network resources. Bandwidth constraints models play a key role in the DS-TE architecture, since they establish how bandwidth is distributed among different classes. In this paper, we first present a new bandwidth constraints model, called G-RDM. Then, we compare the performance of G-RDM with respect to MAM and RDM in different scenarios by means of an analytical model based on Markov-chains. The results show that G-RDM, joining the best features of MAM and RDM, allows to improve their performance.

Originally conceived as a fast forwarding technique, MPLS provides support for traffic engineering and network survivability. Constrained-based path computation is a key building block for traffic engineering in MPLS networks, since it allows to select a path that satisfies assigned QoS requirements. In this paper, we introduce a novel path computation procedure which aims at improving the performance of the well-known Wang-Crowcroft algorithm by means of some heuristics. Moreover, the two algorithms have been developed in NS2 as an extension of OSPF-TE\ns and integrated with RSVP-TE\ns. Finally, the paper shows how the developed software module can be used to satisfy a set of LSP allocation requests with multiple QoS constraints.

In a multi-service IP network, it is a key challenge to provide QoS to end-user applications while effectively using network resources. Traffic Engineering support in MPLS networks allows operators to carry traffic with strict QoS requirements by means of advanced network capabilities, such as resource reservation, fault-tolerance and optimization of transmission resources. To deliver a reliable service, MPLS exploits a set of procedures that protect the traffic flows carried on different paths. Therefore, Label Switching Routers have to support mechanisms for fault detection, notification and recovery, whereas MPLS has to enable the configuration of different recovery techniques. In this paper, we present the design and the development of some software modules for the simulation of different MPLS-based recovery strategies in the discrete event simulator NS2. Moreover, the paper discusses some simulation results that show a performance comparison between path protection and path restoration strategies in a simulation scenario matching the Geant network.

In operational networks, services are delivered according to specific service level agreements (SLAs). To ensure the SLAs stipulated with the users, providers make use of traffic monitoring and measurement tools controlling their highspeed IP networks. In order to improve the scalability of the measurement systems, techniques which allow to reduce the total amount of data to be collected and processed are currently being standardized in the IETF packet sampling (PSAMP) Working Group. Among packet sampling techniques, hash-based one is the only capable of providing a feasible framework to monitor one-way delays (OWDs). This paper is focused on the experimental validation of SLAs conformance in operational networks using hash-based packet sampling techniques. The results presented hereafter are obtained from the analysis of traffic traces collected at two different points of operational networks. Such results are fundamental to validate the whole hash-based packet sampling framework, because they complement the already available results on the uniformity of distribution, computation speed and statistical properties of the hash functions calculated by using single point traces.

In a multi-service IP network, it is a key challenge to provide quality of service (QoS) to end-user applications while effectively using network resources. The DiffServ architecture has emerged as a scalable solution to provide a network with multiple service classes. The multiprotocol label switching (MPLS) traffic engineering (TE), instead, enables resource reservation, fault-tolerance and optimization of transmission resources. MPLS DiffServ-aware TE combines the advantages of these solutions and allows network operators to provide services that require strict QoS performance guarantees. The choice of the signalling protocol to exchange DiffServ, MPLS and TE-related information is a key point in this scenario. Different solutions have been proposed, such as RSVP-TE and CR-LDP. Since the first one seems to be the most attractive for router manufacturers, in this paper, the design and the development of a new software module to simulate the RSVP-TE protocol in the Network Simulator 2 (NS2) is presented. This module could be useful to speed-up the design, development and deployment of DiffServ-aware MPLS networks

This paper presents the results of an experimental activity concerning the implementation and validation of an EF-PHB service in a high-speed metropolitan optical network with a differentiated services architecture. As EF-PHB can be used to create low loss, low latency and assured bandwidth services, real-time traffic flows have been aggregated and classified as EF. Measurements have been performed in different operating conditions to evaluate how the setting of the EF class of service parameters affects the time metrics of each traffic flow belonging to the aggregate. The transfer delay and the delay variation values, as specified by the ITU-T recommendation Y.1541 for real-time, jitter sensitive applications, have been taken as QoS performance objectives to satisfy.

In recent years, the development of a multiservice network, using integrated multiplexing and switching strategies, has converged to an IP-based infrastructure, reducing the relevance of other network solutions, such as a B-ISDN based on ATM. However, to develop an IP-based commercial network, the issues related to quality of service (QoS) support must be addressed. These problems are particularly relevant when a satellite access solution is considered, since the network resources can be limited and expensive. At the same time, demand assignment multiple access (DAMA) protocols permit better exploitation of satellite resources by dynamically allocating system bandwidth in response to user requests. The paper discusses the application to a DAMA satellite access network of different QoS architectures proposed by the IETF and the scientific community. The main novelty of the paper consists in selecting from the alternatives the most suitable solution to be applied to a satellite access network. Furthermore, the advantages and the drawbacks of the proposed architectures are analyzed in detail; the interaction between the control plane of the MAC layer and the resource management at the IP layer is highlighted.

The paper presents an experimental approach to provide a guaranteed quality of service (QoS) over a satellite network based on the Internet protocol (IP). The results obtained represent part of the experimental activity carried out during the second year of the project "Integration of multimedia services on heterogeneous satellite networks", called "ASI-CNIT project". Both subjective metrics as mean opinion score (MOS) and objective metrics, as video and voice packet loss, jitter and transmission rate have been used to investigate the topic and to get proper configurations able to guarantee an high quality of service perceived by the users (PQoS-perceived quality of service). The integrated services approach along with the ReSerVation Protocol (RSVP) has been chosen to reserve the network resources. The measures reported have been obtained by real operative sessions.