The evolution of mobile communication technologies to Beyond 5G (B5G) and Sixth-Generation (6 G) introduces significant advancements but also new cybersecurity vulnerabilities. This paper details the National Inter-university Consortium for Telecommunications (CNIT) National Laboratory of Smart and Secure Networks (S2N) testbed in Genoa, Italy, a state-of-the-art facility for research and development in B5G/6G technologies with a focus on cybersecurity. The testbed’s modular architecture, including isolated “islands” and advanced HardWare/SoftWare (HW/SW), enables the simulation and analysis of cyber threats like Application Programming Interface (API) exposure, and Distributed Denial of Service (DDoS) attacks. It supports the testing of security measures, including Artificial Intelligence (AI)/Machine Learning (ML)-based solutions, and facilitates comprehensive vulnerability assessments and penetration testing for next-generation networks.

A Packet delay emulator for high bandwidth traffic is a specialized tool designed to simulate network delays and measure the impact on high-speed data transmission. Simulating network delays and measuring the impact on data transmission allows network administrators and developers to test their systems in a controlled environment, mimicking real-world scenarios with different network conditions. This paper discusses the limitations of traditional packet latency emulators and the technical con-siderations required to test high-speed networks, and proposes a specialized packet latency emulator to replicate real-world network conditions. Results compare the proposed emulator with the best tools that are currently available and show a performance in line with the reference, with a significant accuracy improvement in the presence of higher latencies.

The availability of networks able to perform control and management operations at unprecedented speeds is a crucial achievement for supporting next-generation Industry 4.0 applications. In particular, the presence of humans interacting with moving objects calls for decisions to be performed at time scales such as to ensure achieving the specific requirements identified by standardization bodies for the industrial sector in Beyond-5G environments. Among these specific Key Performance Indicators (KPIs), network reliability must be carefully pondered to guarantee the safety of the human operators at any time while investing the proper level of resources. For this reason, this paper presents an optimization problem that finds the best set of redundant radio bearers for each User Equipment (UE) in the factory area while accounting for the resource usage. Such problem extends the current specification on redundant transmissions. Several heuristics have been designed to achieve the desired time scales that could not be fulfilled with an exhaustive search. Results show that the optimization algorithm and the heuristics provide the same outcomes in terms of selected bearers and loss rates, but the latter do so at significantly more stringent time scales.

Beside increasing flexibility and programmability, the current network “softwarization” trend is believed to be beneficial also in respect of energy efficiency, owing to the consolidation of resources made possible by virtualized networking components. However, the widespread use of general-purpose hardware may jeopardize energy saving, unless proper control strategies are put in operation. In this context, the paper addresses a “smart sleeping” control problem, where computing resources in multi-core processors executing network functions are modelled as multi-server queues, and the number of active processing units (either physical or virtual) can be dynamically adjusted by parametric control over a time scale compatible with the long-term dynamics of the traffic flows that require processing. We show that, on average, up to 25% of processing capacity of a network node can be turned off in the presence of bursty traffic with low load without significantly affecting packet latency.

In order to fulfill the stringent requirements of 5G applications, measuring the performance of the VNFs composing the network slices is crucial to identify potential bottlenecks of the networks. However, since the VNF behavior is time varying and strongly depends on the infrastructure and hosting execution environment, the traditional traffic generators are not suited for the evaluation as their overhead, both in terms of deployment time and code complexity, may affect the results to the point of corruption. In order to overcome this issue, this paper presents a software traffic generator, based on TRex and executed in a VNF, which leverages on an automation framework to provide zero-touch as-a-Service active monitoring. Results show that the impact of this solution on the measured performance is negligible in terms of deployment time as well as required input lines.

Network softwarization has paved the way for 5G technologies, and a wide-range of (radically new) verticals. As the telecommunications infrastructure evolves into a sort of distributed datacenter, multiple tenants such as vertical industries and network service providers share its aggregate pool of resources (e.g., networking, computing, etc.) in a layered “as-a-Service” approach exposed as slice abstractions. The challenge remains in the coordination of various stakeholders’ assets in realizing end-to-end network slices and supporting the multi-site deployment and chaining of the micro-service components needed to implement cloud-native vertical applications (vApps). In this context, particular care must be taken to ensure that the required resources are identified, made available and managed in a way that satisfies the vApp requirements, allows for a fair share of resources and has a reasonable impact on the overall vApp deployment time. With these challenges in mind, this paper presents the Resource Selection Optimizer (RSO)– a software-service in the MATILDA Operations Support System (OSS), whose main goal is to select the most appropriate network and computing resources (according to some criterion) among a list of options provided by the Wide-area Infrastructure Manager (WIM). It consists of three submodules that respectively handle: (i) the aggregation of vApp components based on affinities, (ii) the forecasting of (micro-) datacenter resources utilization, (iii) and the multi-site placement of the (aggregated) vApp micro-service components. The RSO’s performance is mainly evaluated in terms of the execution times of its submodules while varying their respective input parameters, and additionally, three selection policies are also compared. Experimental results aim to highlight the RSO behavior in both execution times and deployment costs, as well as the RSO interactions with other OSS submodules and network platform components, not only for multi-site vApp deployment but also for other network/services management operations.

Multi-access Edge Computing (MEC) is expected to play an essential role in enabling 5G (and beyond) technologies and services. This has driven numerous micro-datacenter (μDC) deployment studies in the literature, with a common goal of addressing the optimal μDC placement and dimensioning problems. Along this line, this paper aims at clustering subareas with similar network activity dynamics, to find a good hotspots' representation over the urban area. Leveraging common Machine Learning (ML) and statistics principles, the main contribution of this paper is two-fold: (i) the definition and selection of dynamicity features based on real telecommunications datasets; and (ii) the granular cluster evaluation and analysis based on agglomerative hierarchical clustering. Three feature sets (containing 20, 12 and 8 features, respectively) are evaluated at varying precision levels, showing interesting trends on the number of clusters, heatmaps and intra-cluster correlation. These could potentially provide some valuable indications on the placement and dimensioning of the$\mu$DCs.

This paper aims to showcase the ability of the MATILDA Platform to enable vertical applications fully utilizing the capabilities offered by the fifth generation of mobile networks (5G). Although 5G, powered by network slicing and edge computing, promises to flexibly support radically new and extremely heterogeneous vertical applications, vertical stakeholders generally lack both the skills to exploit the full potentials of 5G networks and the vision of the underlying resources, owned by Telecom providers that are reluctant to expose them in an unmediated way. The MATILDA platform bridges the gap between the vertical application and the network service domains. This paper presents the case of an Industry 4.0 application, and highlights the role played by the MATILDA solution in its successful deployment and orchestration.

Since the adoption of virtualization paradigms is seen as a viable way to fulfil the requirements of next-generation applications in a sustainable way, this paper examines the virtualization of the Radio Access Network (RAN), focusing on the C-RAN architecture, in order to better understand the impact of NFV technologies on power consumption and costs in real networks. The evaluation compares the power consumption obtained by deploying the Base Band Unit (BBU) using commercial devices or pools of Virtual Network Functions (VNFs). Publicly available datasets describing the traffic and the eNodeBs have been used for the evaluation, as well as datasheets for both the commercial devices and the VNF pools. Results show that the usage of the virtualized BBU causes consumptions around 250% higher with respect to the commercial deployment, and operation and capital costs over 66% higher, contradicting the common belief of NFV being a “green” technology. Further estimates conducted in this paper, however, highlight how the deployment of VNFs alongside specialized hardware solutions can represent a successful approach for telecom providers, with energy savings up to 20% and costs in line with the ones of dedicated hardware deployments.

While the integration of fourth-generation (4G) networks with Edge Computing technologies would anticipate the improvements foreseen by the coming of 5G, as well as smoothen the transition to the new technology, 4G does not natively support Edge Computing. Therefore, specific functionalities for user-plane integration and isolation of tenant spaces are required for effectively deploying Edge Computing in 4G networks. This paper describes the design of the end-point between the mobile and edge environments that has been integrated in the telecom layer platform of the MATILDA Project. Such end-point, designed in a Virtual Network Function (VNF), allows intercepting and forwarding data and control traffic towards external Data Networks. Instances of this VNF can be horizontally scaled according to a decision policy, which determines the minimum number of instances required for the current load. Results show that the latency ascribable to the VNF processing is sufficiently low to satisfy the delay budget for all 5G use cases up to 10 ms and that the decision policy based on the QoS Class Identifiers (QCIs) allows scaling with the traffic load, while still fulfilling the performance requirements of each application.

HTTPS enhances end-user privacy and is often preferred or enforced by over-the-top content providers, but renders inoperable all intermediate network functions operating above the transport layer, including caching, content/protocol optimization, and security filtering tools. These functions are crucial for the optimization of integrated satellite-terrestrial networks. Additionally, due to the use of end-to-end and per- session encryption keys, the advantages of a satellite's wide- area broadcasting capabilities are limited or even negated completely. This paper investigates two solutions for authorized TLS interception that involve TLS splitting. We present how these solutions can be incorporated into integrated satellite- terrestrial networks and we discuss their trade-offs in terms of deployment, performance, and privacy. Furthermore, we design a solution that leverages satellite broadcast transmission even in the presence of TLS (i.e. with the use of HTTPS) by exploiting application layer encryption in the path between the satellite terminal and the TLS server. Our findings indicate that even if no other operation than TLS splitting is performed, TLS handshake time, which involves roundtrips through possibly a Geosynchronous satellite, can be reduced by up to 94%. Moreover, by combining an application layer encryption solution with TLS splitting, broadcast transmissions can be exploited as well as proactive caching, content pushing, request aggregation, and other optimizations.

Fifth generation (5G) mobile networks will lead to a deep integration between networks and applications. Through novel paradigms like network functions virtualization and edge computing, the new classes of heterogeneous application services will be enabled to run close to the mobile end-user devices with zero-perceived latency and fully-cognitive dynamic reconfiguration capabilities. Such “vertical” applications exhibit diverse performance/scalability requirements, and will rely on highly distributed, extremely virtualized, multi-tenant, and software-defined infrastructures. In such a context, handling the required operations in a scalable and dynamic fashion will be of paramount importance. A specific aspect, addressed by software-defined networking (SDN), regards the provision of suitable communication channels, once resource allocation mechanisms have performed the most efficient deployment of virtual network function instances, and VNF chaining needs to be implemented to enable network services. In this respect, this paper introduces the multi-cluster overlay (MCO) network paradigm: a tunnel-less SDN scheme for scalable realization of virtual tenant networks across the 5G distributed infrastructure, able to support (bulk) migrations of software instances among geo-distributed computing resources in a seamless and effective fashion. The numerical simulation and experimental results show that the MCO achieves up to over one order of magnitude smaller number of forwarding rules than the other state-of-the-art SDN mechanisms, while also assuring high performance during reconfiguration operations.

The upcoming Cloud-Fog interplay is expected to grant service providers more degrees of freedom in the implementation and management of their service portfolios. However, recent advances in Mobile Internet have developed a growing need to support user mobility - as users move, the Fog counterparts of services that require close proximity may call for migration(s) to meet the desired quality of service (QoS). With the state-of-the-art virtualization technologies, next-generation Cloud/Fog services are being implemented in modular software (i.e., as a graph/chain of portable virtual objects (VOs)) that can be migrated around the Telco infrastructure, and yet scalability is still an open issue, especially with the inter-datacenter bulk live migration of VOs. In this perspective, a VO clustering and migration policy that jointly considers user proximity and inter-VO affinity is proposed to scalably support user mobility, while allowing service differentiation among users. Results confirm that introducing migrations improve the QoS to always meet or exceed the requirements, as compared to static service placement, and considering VO clusters as aggregate entities will initiate around 40% less migrations, on average - an improvement that increases with inter-VO affinity and could potentially simplify service management when supporting user mobility.

Within a 5G mobile network scenario, this paper adopts Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV) technological frameworks to evaluate the gain in terms of user Quality of Experience (QoE) that can be achieved when contents and server of a video delivery service move from the cloud at the edge of the Radio Access Network. The application scenario we set up envisions a video streaming service geared a small group of individuals who have strong social ties and who move in an urban space. The scaling up of this emergent type of social interaction is set to become in the near future highly demanding for the resources of mobile operators. The paper recreates the network infrastructure of a mobile operator in the city of Milano. We model network conditions through Mininet, and manage (computing, network and storage) resources by means of an orchestrator named OpenVolcano. The paper shows that, compared to the traditional cloud approach, a MEC one provides better QoE to group members simply taking advantage both of the proximity and distribution of mini data centers and of proper resource orchestration.

The Network Functions Virtualization (NFV) paradigm is undoubtedly a key technological advancement in the Information and Communication Technology (ICT) community, especially for the upcoming 5G network design. While most of its promise is quite straightforward, the implied reduction of the power consumption/carbon footprint is still debatable, and not in line with the energy efficiency perspective forecasted by the ETSI NFV working group (WG). In this paper, we provide an estimate of the possible future requirements of this upcoming technology when deployed according to the virtual Evolved Packet Core (vEPC) use case specified by the ETSI NFV WG. Our estimation is based on real performance levels, certified by independent third-party laboratories, and datasheet values provided by existing commercial products for both the legacy and NFV network architectures, under different deployment scenarios. Obtained results show that a massive deployment of the current NFV technologies in the EPC may lead to a minimum increase of 106 % in the carbon footprint/energy consumption with respect to the Business As Usual (BAU) network solutions. Moreover, these values tend to increase at a very high pace when the most suitable software/hardware combination is not applied, or when packet processing latency is taken into account.

By bringing Cloud-like services much closer to endusers and their devices, Fog computing is foreseen to expand the scope of overlay networks, encompassing different heterogeneity dimensions (i.e., size, geographical distribution, devices/virtual objects (VOs) involved, quality of service (QoS) requirements, etc.). Although highly flexible solutions like Software-Defined Networking (SDN) have been conceived to handle such heterogeneity in future networks, scalability is still an open issue, especially with respect to Fog computing requirements. In this paper, we propose an SDN-based network slicing scheme for supporting multi-domain Fog/Cloud services, which offers high scalability, among other aspects, over legacy ones. Results show that the number of unicast forwarding rules needed to be installed in an overlay drops by up to over one order of magnitude and 4 times compared to the "fully-meshed" and OpenStack cases, respectively, at the cost of possible path sub-optimality, albeit knowledge on the datacenter topology can be used for VO placement optimization.

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.

The design of High Throughput Satellite (HTS) systems builds on the concept of Smart Gateway Diversity (SGD) to exploit the spatial diversity of gateways in case of feeder link outage, occurring because of atmospheric impairments introduced in Extremely High Frequency (EHF) frequency bands. The gateway handover procedure requires precise prediction algorithms and coordination among different network elements. This paper presents novel outage prediction algorithms based on machine learning concepts, integrated in the framework of SDN architectures, to efficiently orchestrate the gateway handover operations. Simulation campaigns prove the validity of the proposed concept and shed light on the potentials of the SDN architecture in future HTS systems.

Free-to-play models, streaming of games and eSports are reasons for online gaming to grow in popularity recently. On the forefront are multiplayer online battle arenas, which gain high popularity by introducing a competitive format that is easy to access and requires cooperation and team play. These games highly rely on fast reaction of the players, which makes latency the key performance indicator of such applications. To obtain low latency, this paper proposes moving game servers close to players towards the edge of the network. The performance of such mechanism highly depends on the geographic distribution of players. By analyzing match histories and statistics, we develop models for the arrival process and location of game requests. This allows us to evaluate the performance of edge server resource migration policies in an event based simulation. Our results show that a high number of edge servers is preferable compared to few larger edge servers to reduce the latency of players. This supports approaches that allow deploying virtual server instances in the back-haul.

An estimation setting is considered, where a number of sensors transmit their observations of a physical phenomenon, described by one or more random variables, to a sink over noisy communication channels. The goal is to minimize a quadratic distortion measure (Minimum Mean Square Error - MMSE) under a global power constraint on the sensors' transmissions. Linear MMSE encoders and decoders, parametrically optimized in encoders' gains, Shannon-Kotel'nikov mappings, and nonlinear parametric functional approximators (neural networks) are investigated and numerically compared, highlighting subtle differences in sensitivity and achievable performance.

The constant evolution and expansion of the Internet and Internet-related technologies has exposed the limitations of the current networking infrastructures, which are represented by the unsustainable power consumption and low level of scalability. In fact, these infrastructures are still based on the typical, ossified architecture of the TCP/IP paradigm. In order to cope with the Future Internet requirements, recent contributions envisage an evolution towards more programmable and efficient paradigms. In this respect, this paper describes the basic issues, the technical approaches, and the methodologies for the implementation of power management primitives in the context of the emerging Software Defined Networking. In detail, we propose to extend one of the most prominent solutions aimed at increasing networking flexibility, the OpenFlow Protocol, to integrate the energy-aware capabilities offered by the Green Abstraction Layer (GAL). However, the mere introduction of node-level solutions would be of little or no use in the absence of a network-wide management scheme to guarantee inter-operability and effectiveness of the proposed architecture. In this respect, this work also proposes an analytical model for the management of a network with these capabilities. The results will show how our solutions are well suited to provide a scalable and efficient network architecture able to manage the orchestration and consolidation of the available resources.

We propose an analytical model able to accurately estimate both power consumption and network performance indexes of Energy Efficient Ethernet (EEE) links working at the three available speeds under various traffic load patterns and packet size distributions. The model is sufficiently flexible and accurate to consider different traffic parameters; among others, the packet size distribution, the average burst inter-arrival rate, the burst size distribution, etc. With relatively low complexity, since it addresses stationary queue behavior, the analysis allows obtaining the average energy consumption of the link and, unlike previous works, the mean latency time experienced by incoming packets in closed form, without any upper or lower bound approximations. This aspect makes the model suitable to be adopted in optimization frameworks for network design and control purposes. The numerical results of the model are validated against measurements on a real test bench.

With the aim of controlling power consumption in metro/transport and core networks, we consider energy-aware devices able to reduce their energy requirements by adapting their performance. In particular, we focus on state-of-the-art packet processing engines, which generally represent the most energy-consuming components of network devices, and which are often composed of a number of parallel pipelines to “divide and conquer” the incoming traffic load. Our goal is to control both the power configuration of pipelines and the way to distribute traffic flows among them. We propose an analytical model to accurately represent the impact of green network technologies (i.e., low power idle and adaptive rate) on network- and energy-aware performance indexes. The model has been validated with experimental results, performed by using energy-aware software routers loaded by real-world traffic traces. The achieved results demonstrate how the proposed model can effectively represent energy- and network-aware performance indexes. On this basis, we propose a constrained optimization policy, which seeks the best tradeoff between power consumption and packet latency times. The procedure aims at dynamically adapting the energy-aware device configuration to minimize energy consumption while coping with incoming traffic volumes and meeting network performance constraints. In order to deeply understand the impact of such policy, a number of tests have been performed by using experimental data from software router architectures and real-world traffic traces.

The energy management of computer hardware mainly relies on primitives defined in the ACPI industrial standard. Network devices can borrow the same concepts as a basis for their energy-efficient design; however, to effectively tailor network processors for their specific applications, the set of energy-aware states defined by the ACPI and their parameters should be carefully chosen. In particular when designing network hardware, one should address the problem of optimizing parameter values (most often chosen in a discrete set) to trade-off energy efficiency and network performance. In this paper, we provide a methodology for such choice, by investigating this trade-off over a continuous spectrum of parameter values. This approach will allow us to gain insight in the behavior of the optimal solutions that achieve different desired tradeoffs.

The authors consider the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables (r.v.'s) that are correlated with the source output. The acquired values are transmitted over a wireless channel to a sink, where an estimation of the source has to be constructed, according to a given distortion criterion. In the presence of Gaussian random variables (r.v.'s) and a Gaussian vector channel, the authors are seeking optimum real-time joint source-channel encoder–decoder pairs that achieve a distortion sufficiently close to the theoretically optimal one, under a global resource constraint, by activating only a subset of the sensors. The problem is posed in a team decision theoretic framework, and the optimal strategies are approximated by means of neural networks. The analysis investigates the generalisation capabilities of the proposed approach, by showing insights into the structure of the problem. The surprising outcome is that a quasi-static application of the approach reveals to be sufficient to maintain quasi-optimal performance under a dynamic environment (e.g. with respect to nodes' positions).

This study aims at summarising the work done in the SatNEx project for the development and the performance evaluation of a new access scheme for multimedia data transmissions in digital video broadcasting-return channel via satellite (DVB-RCS)/satellite second generation (S2), applied to a mobile environment. The work has been stimulating because, as far as the authors know, this research topic has not yet been fully addressed in the literature. The authors started thinking about a realistic scenario of mobility, where novel services could be offered; having defined the scenario, the authors developed a satellite access scheme suitable for transmission control protocol (TCP) sessions and video streams in mobility. In this study, the authors present the reference architecture, the access scheme, the choice of the most suitable transmission parameters and the simulation results that are obtained when transmitting the two different types of data.

The real-time control of multiple queues handling traffic of different nature is obtaining increasing relevance in both the uplink and downlink of wireless telecommunication networks, characterised by the presence of a central access point. Such is the case of satellite networks, with either on-board processing or double-hop configuration, besides a number of terrestrial local and metropolitan wireless networks. Given a certain amount of available bandwidth, the problem is that of deciding, within a certain time frame, the allocation of bandwidth partitions for each traffic queue, whose packets are awaiting transmission; eventually, this determines the transmission rates to be passed to the scheduler and to the physical layer adaptive coding and modulation devices. In a satellite network, where this task is accomplished by a master station, residing at the access point, it is possible to take such decisions by means of a centralised controller, based on real-time instantaneous (in the downstream direction) or delayed (in the upstream) information on the queues' state. The study derives a control law to be used in this task, by adopting an approach based on optimal linear quadratic regulation. Both cases of un-delayed and delayed information are considered. The control laws are tested in a geo-stationary satellite scenario of digital video broadcasting – return channel via satellite (DVB-RCS), and the queues are considered at the medium access control level. Simulation results under real-traffic traces are also presented to highlight the effectiveness of the control and to compare alternative solutions.

In this contribution, we explore and evaluate the feasibility and the impact of power management policies, able to well suit a heterogeneous set of highly modular architectures, generally used for developing today's network equipment. The proposed policies aim at optimizing the power consumption of each device component with respect to its expected network performance. In order to provide an experimental evaluation of the proposed ideas, we applied such power management policies to a new generation SW router based on multi-core COTS platform, and we evaluated it with real traffic traces.

Telecommunications networks are composed of functional layers acting in cascade. Quality of service (QoS) derives from the action of each layer that must assure a specific level of quality to the upper layer in terms of performance parameters. The action is called vertical QoS mapping and is provided through algorithms that compute the bandwidth necessary so to assure the requested QoS when information is transferred from one layer to the next one below. This paper proposes a scheme that adapts the bandwidth to be allocated to a buffer which conveys heterogeneous traffic (both concerning traffic sources and QoS requirements) in a layer-in-cascade model. The proposed algorithm is based only on measures and does not use closed-form expressions, a-priori information about traffic statistical properties, and assumptions about buffer dimension. It is called WI-RCBC (wireless interface reference chaser bandwidth control).

We consider the acquisition of measurements from a source, representing a physical phenomenon, by means of sensors deployed at different distances, and measuring random variables that are correlated with the source output. The acquired values are transmitted to a sink, where an estimation of the source has to be constructed, according to a given distortion criterion. In the presence of Gaussian random variables and a Gaussian vector channel, we are seeking optimum real-time joint source-channel encoder-decoder pairs that achieve a distortion sufficiently close to the theoretically optimal one, under a global power constraint, by activating only a subset of the sensors. The problem is posed in a team decision theoretic framework, and the optimal strategies are approximated by means of neural networks. We compare the solution with the results obtained by heuristically choosing a subset of the sensors on the basis of successive simulations under a fixed topology.

The Extended RItz Method (ERIM) can be used to face optimal decision and control problems when finding the global solution is hard, because the problem is ill-conditioned or we can only compute the solution via numerical approximations. It consists in constraining the control functions to take on a fixed structure with a certain number of free parameters to be optimized. We will show the use of such method for the solution of a communication problem in a mixed (analog/digital) transmission environment. A noisy channel is used to convey information from a limited-energy analog device to a sink; in the presence of a binary link, how can we reduce the energy spent for transmission without renouncing reconstruction capability and real-time encoding?

The problem of pricing for a telecommunication network is investigated with respect to the users' sensitivity to the pricing structure. A functional optimization problem is formulated, in order to compute price reallocations as functions of data collected in real time during the network evolution. No a-priori knowledge about the users' utility functions and the traffic demands is required, since adaptive reactions to the network conditions are sought in real time. To this aim, a neural approximation technique is studied to exploit an optimal pricing control law, able to counteract traffic changes with a small online computational effort.

The goodput of long-lived TCP connections can be maximized in error-prone channels by trading bandwidth for Bit Error Rate (BER). This fact can be exploited in the bandwidth and transmission parameters' assignment, by means of Adaptive Coding and Modulation (ACM), in satellite systems; in such a case, multiple source- destination (SD) pairs actually "see" channels with diverse characteristics, owing to unequal weather conditions. However, when dealing with multiple SD assignments, both aspects of goodput-maximization and fairness must be dealt with. Improving the effectiveness of TCP may be relatively easy, but maintaining the effectiveness while maintaining the fairness is a challenge. The paper numerically analyzes the fairness behavior of two bandwidth allocation methods to TCP connections, experiencing diverse fading conditions.

This paper deals with a NP-hard resource allocation problem for a satellite network. An approach based on the estimation of the gradient of a cost function, obtained through a "relaxed continuous extension" of the discrete constraint set, is proposed. Since neither closed forms of the performance measure, nor additional feedbacks on the statistical properties of the traffic sources are requested, the proposed approach reveals to be suitable for optimizing the resource allocation in real life case studies, where the application of specific certainty equivalent assumptions is impractical

In this paper, a novel call admission control (CAC) problem is investigated in relation to the pricing structure of a telecommunication network, in which both guaranteed performance (GP) and best effort (BE) services are offered. The user's sensitivity to the prices is described through utility functions. An original decision making process is studied to decentralize the proposed CAC mechanism. To this aim, a neural approximation technique is investigated to exploit different decision makers, distributed in the network and performing the CAC decisions. Simulation results show how sub-optimal CAC decisions are obtained in a decentralized fashion and with a small on-line computational effort.

A resource allocation problem for a satellite network is considered, where variations of fading conditions are added to those of traffic load. Since the capacity of the system is finite and divided in finite discrete portions, the resource allocation problem reveals to be a discrete stochastic programming one, which is typically NP-Hard. In practice, a good approximation of the optimal solution could be obtained through the adoption of a closed-form expression of the performance measure in steady-state conditions. Once we have summarized the drawbacks of such optimization strategy, we address two novel optimization approaches. The first one derives from Gokbayrak and Cassandras and is based on the minimization over the discrete constraint set using an estimate of the gradient, obtained through a "relaxed continuous extension" of the performance measure. The computation of the gradient estimation is based on infinitesimal perturbation analysis (IPA). Neither closed forms of the performance measures, nor additional feedbacks concerning the state of the system and very mild assumptions about the stochastic environment are requested. The second one is the main contribution of the present work, and is based on an open-loop feedback control (OLFC) strategy, aimed at providing optimal reallocation strategies as functions of the state of the network. The optimization approach leads us to a functional optimization problem, and we investigate the adoption of a neural network-based technique, in order to approximate its solution. As is shown in the simulation results, we obtain near-optimal reallocation strategies with a small real time computational effort and avoid the suboptimal transient periods introduced by the IPA gradient descent algorithm.

There are several QoS technologies available for an Internet service provider to manage guaranteed performance services in a telecommunication network. The study of the possible interactions between the different QoS technologies is currently an open area of research often called, in the literature, QoS mapping. A QoS mapping problem arises when different encapsulation formats are employed to support a fixed QoS with different transport technologies (e.g., ATM, IP). We face this problem in terms of bandwidth management. We adopt a novel control scheme that does not need any closed-form formula for the performance metric and that is able to react to traffic changes.

Video-coding techniques that are widely used in videoconferencing and streaming applications over the Internet may face degradation in performance when traversing satellite channels. The aim of the present paper is to provide objective measurements of the quality of video-encoded sequences for two of the most popular video coders in this setting [namely, H.261 and moving picture experts group (MPEG)-2], after transmission over faded satellite channels. In particular, the emphasis is on the Ka band, which is becoming an attractive alternative for commercial applications but is more prone to quality degradation than the currently widely used Ku band. In order to test the behavior of the coders in repeatable experimental conditions and over different data link platforms, two transmission chains have been implemented in the laboratory by careful emulation of the required environment. The data link layer has been based on HDLC-like and direct video broadcasting protocols, respectively. The experiments have been performed in the presence of additive white Gaussian noise (AWGN) and by using fading patterns derived by real-world data. The results obtained highlight some often neglected aspects in the behavior of the coders under examination, both in relation to their comparative performance and to their adaptability to different underlying data link protocols.

The paper proposes two alternative bandwidth allocation schemes suited for the Ka-band satellite environment. The aim is to provide a control mechanism to compensate rain fade. The main idea is to consider the effect of the attenuation as a reduction of the bandwidth 'seen' by a single land station and using a supplementary portion of bandwidth to guarantee a rain margin. One of the earth stations involved plays the role of master. It monitors the available resources and contains the centralized network control center, which manages the bandwidth allocation. Both schemes are organized in two hierarchical levels. The upper level, located in the master station, assigns a portion of the overall bandwidth to the earth stations. At each of these, the lower level shares the received portion between guaranteed and non-guaranteed traffic. The allocation mechanisms are aimed at keeping the call blocking probability of the guaranteed traffic below a given threshold and at reducing the packet dropping probability of the non-guaranteed, best-effort traffic. An extensive performance analysis has allowed investigation of the behavior of the two strategies showing the differences between them.

The paper proposes a bandwidth allocation scheme adapted for satellite networks. The traffic considered is divided into two categories: guaranteed traffic and non-guaranteed best-effort traffic. The control scheme is aimed at keeping the call blocking probability of the guaranteed traffic below a given threshold and at minimising the packet discarding probability of the best-effort portion in case of degradation of the satellite channel. In that way a minimum grade of service should be guaranteed for both traffic. The allocation scheme is designed as a two-level hierarchical mechanism: the upper level allocates a portion of the overall bandwidth to each station, the lower level shares the received portion between guaranteed and non-guaranteed traffic. The results show the behaviour of the algorithm and its performance.

Mobile communications are widespread in a large part of industrialized countries and cellular networks, by which mobile radio-communications are supported, can give directly or potentially a huge amount of frequently updated information on the position of their users. This information can be used to estimate on-line the traffic conditions of important roads and highways, by exploiting the presence of mobile phones on-board a good deal of vehicles. This paper analyzed this possibility and proposes a mechanism, which gives the capability to estimate traffic parameters in the cells along a road with a partial presence of active cellular phones in the vehicles. The proposal has been tested by using an integrated vehicle and communication traffic simulator and different situations have been verified. The results are presented in the paper and they show a good level of accuracy and a satisfactory behavior of the proposed technique.

A cellular wireless network is considered, where multi-class call admission control is exerted locally in a number of cells. Cells are in turn organized into clusters, which play the role of a higher hierarchical control level. The whole capacity (in number of channels) available in a cluster is distributed among its cells, according to variations in traffic parameters and user mobility, with the goal of minimizing the average blocking probability for the whole system. At the same time, on the basis of a temporarily assigned capacity, cell controllers regulate the admission of new calls and the handoff flow, by setting thresholds on the maximum number of new calls that prevent the blocking probability of the handoff calls to exceed a given upper bound. The overall resource allocation mechanism is described and numerical results are presented and discussed, in relation with different user mobility patterns.

In an access node to a multiservice network [e.g., a base station in an integrated services cellular wireless network or the optical line terminal (OLT) in a broad-band passive optical network (PON)], the output link bandwidth is adaptively assigned to different users and dynamically shared between isochronous (guaranteed bandwidth) and asynchronous traffic types. The bandwidth allocation is effected by an admission controller, whose goal is to minimize the refusal rate of connection requests as well as the loss probability of cells queued in a finite buffer. Optimal admission control strategies are approximated by means of backpropagation feedforward neural networks, acting on the embedded Markov chain of the connection dynamics; the neural networks operate in conjunction with a higher level bandwidth allocation controller which performs a stochastic optimization algorithm. The case of unknown, slowly varying input rates is explicitly considered. Numerical results are presented that evaluate the approximation and the ability to adapt to parameter variations.

We consider the problem of multimedia service distribution over an integrated services broadband access network, based on the transport of fixed-size traffic units (ATM cells). In general, the presence of distributed access multiplexers is assumed, which are both geographically dispersed and hierarchically structured. Such multiplexers are intelligent devices with decision making capabilities that operate jointly, in order to make the best possible use of the transport capacity of the access network and to maintain the quality of service (QoS) requirements of different users and service classes. Following the physical system organization, a hierarchical control structure is defined, where each service class is served by an independent controller, parametrized by the bandwidth allocated to it, and a central agent, playing the role of a coordinator in the hierarchical control scheme, decides upon the bandwidth partitions. This decision aims at minimizing a global cost that captures QoS requirements both at the call-level (call blocking probability) for QoS-aware, connection-oriented services and at the cell-level (cell loss probability) for connectionless, best-effort, ones. The control architecture also reflects the multilayer hierarchy introduced by the presence of multiple teletraffic time scales, by essentially decoupling the above problem from that of ensuring QoS at the cell-level for services of the first type. The cost functions used in the optimization and the control strategies are described, and numerical results are reported, which indicate the effectiveness of the method.

In an access node to a hybrid-switching network (e.g., a base station handling the downlink in a cellular wireless network), the output link bandwidth is dynamically shared between isochronous (guaranteed bandwidth) and asynchronous traffic types. The bandwidth allocation is effected by an admission controller, whose goal is to minimize the refusal rate of connection requests as well as the loss probability of packets queued in a finite buffer. Optimal admission control strategies are approximated by means of backpropagation feedforward neural networks, acting on the embedded Markov chain of the connection dynamics. The case of unknown, slowly varying, input rates is explicitly considered. Numerical results are presented, comparing the approximation with the optimal solution obtained by dynamic programming.

An access multiplexer is considered, serving a TDM output channel with a hybrid structure, characterized by the presence of both isochronous (circuit-switched) and asynchronous (packet-switched) traffic. The isochronous traffic is, in turn, subdivided into several classes, which are distinguished according to their speed, in order to model a multirate, multitraffic environment. The aim of the paper is to define a control scheme for the allocation of the output link bandwidth, in order to realize two objectives: 1) minimize call blocking probability for the isochronous traffic and packet loss probability, and 2) meet quality of service requirements for both traffic types as closely as possible. The proposed scheme is based on a hierarchical control structure, where the description of the dynamics of the isochronous traffic is given in terms of continuous-time Markov chains and the packet rejection rate is approximated by means of the stationary distribution of an M/Pareto/1/K queue by exploiting the large difference in time scales between the isochronous flows and the asynchronous one. There are two control levels: 1) a "fast" one acting on the admission of isochronous calls by means of distributed decisional agents (one for each traffic class) which operate with the time scale of the connection request process dynamics, and 2) a slower one, playing the role of a coordinator in the hierarchical scheme, which periodically recomputes a set of parameters (related to the bandwidth allocation) by numerically solving a parametric optimization problem, where real-time information as well as traffic statistics are taken into account. Numerical results are provided, to investigate the performance of the control architecture.

The paper presents a scheme for admission control at the call set-up in an ATM node, aimed at minimising the probability of blocking a call and at balancing this probability between traffic classes. The traffic is considered to be divided into traffic classes, characterised by statistical parameters like peak and average bandwidth and by quality of service (QoS) requirements that allow to define a feasibility region: where requirements are guaranteed A model to describe the 'call admission' is proposed: two different cost functions, based on the blocking probability, are defined and minimised, taking into account the 'feasibility region' constraint: the first function is intended to obtain the minimum overall blocking probability, whereas the second is aimed to balance the blocking probability among classes. The maximum number of acceptable calls yielded by each cost function is obtained for each class, by minimising it under stationary traffic conditions. The efficiency of the proposed strategy is tested by simulations and verified by comparing it with other admission schemes.

Management and control of multimedia traffic is considered within a scenario characterized by the presence of different network platforms, exhibiting possibly very different features in terms of quality of service (QoS). In this framework, the needs for control strategies, acting in the user plane at protocol layers above the transport is examined and their goals, possible actions, information bases and location (centralized/decentralized) are discussed. Two specific strategies for QoS control of a multimedia connection involving real time traffic (voice and video) are then introduced; one is based on a backoff procedure at the transmitter, while the other acts upon feedback from the receiver to change some features of the coding algorithm. The joint operation of both control strategies is examined in an experimental environment based on Ethernet networks, interconnected by IP routers.

A multiple access protocol based on a reservation random access (RRA) scheme is derived, and its performance is evaluated and compared by simulation to that of other protocols operating within the same environment. Given a TDMA framed channel and a cellular structure, the aim of the protocol is that of maximizing the one-step throughput over an entire frame. This is achieved by deciding on the access rights at the base station, which then broadcasts this information at the beginning of the frame. The decision is made on the basis of binary channel feedback information (collision/no collision) over the previous frames, as well as of long term averages of packet generation rates at the mobile stations, assuming independence in the presence of packets at the latter. The scheme is analysed by simulation in the presence of voice and data traffic. Several comparisons show a relevant performance improvement (in terms of data delay and maximum number of voice stations acceptable within a cell) over other protocols that use ALOHA as a reservation mechanism (RRA-ALOHA or PRMA schemes).

A combined multiple access protocol is investigated, based on the ISA algorithm, which was first introduced for a slotted unframed channel, and later modified to fit a cellular wireless network with distributed handoff. ISA manages the access rights at the beginning of each slot in order to maximize the success probability (one step throughput), based on an independence assumption on the presence of packets at the stations. We extend this concept in the context of a framed channel that has the same basic structure as packet reservation multiple access (PRMA). In this case, it is possible to see the frame as a whole bunch of "parallel channels", over which the overall success probability must be maximized at the beginning of the frame for all free slots. The combined PRMA-ISA algorithm has been developed and investigated by means of simulations, using models previously proposed for data and voice (phone calls) traffic.>

Dynamic bandwidth allocation among traffic classes with different performance requirements sharing an asynchronous transfer mode (ATM) link is considered as an integrated control problem with a multilevel structure. At the lower level, call admission control rules are applied that maintain a certain grade of service, in terms of cell loss probability and cell delay, given the buffer space and bandwidth assigned to each class; unlike those used in previous works, these rules are derived on the basis of homogeneous (based on similar quantities) measures of the performance requirements. At the higher level, bandwidth shares are periodically recomputed online by an allocation controller, whose goals reflect overall cell loss and refused traffic, as well as overall average delay. These goals are expressed by an optimization problem that is solved by numerical techniques. The whole control system should provide a dynamic feedback controller, capable of reacting in real time to changes in the traffic patterns. Simulation results are presented and are discussed in regard to the efficiency of the admission controllers, the performance of the overall scheme, and the capability of reacting to sudden changes in the load of some traffic class.>

The authors introduce a control strategy for bandwidth management in asynchronous transfer mode (ATM) networks. A two-level hierarchy is defined, where one level performs fixed class-selective call admission control strategies that are periodically dynamically coordinated by a higher-level bandwidth allocation controller. Each admission controller decides to accept or refuse an incoming call on the basis of a class-selective rule designed to maintain a certain quality of service. A decision is taken according to the virtual capacity share that is assigned to the various service classes by the allocation controller. Bandwidth shares are periodically recomputed online by means of the constrained minimization of a cost function that takes cell loss probability and refused traffic into account. The control structure, the strategies, and the optimization algorithm used are described as well as the assumptions underlying the choices made. Initial simulation results are reported.>

The joint problem of admission control of isochronous traffic and bandwidth sharing between isochronous and asynchronous traffic is addressed, in a MAN environment of the DQDB type. Local access controllers at the users' sites independently decide on the acceptance of each new isochronous call be applying a randomized decision rule. A central controller periodically reassigns bandwidth partitions (in slots/frame) between isochronous and asynchronous traffic, as well as upper bounds on the isochronous slots assigned on a per-user basis. This assignment is obtained by means of a parametric optimization that acts on a number of parameters characterizing the local decision rules, in order to minimize a suitable cost function, and depends on the overall system's state at the intervention time. The problem is formalized, and the application of a gradient projection method is considered, with cost functions spanning a finite or even infinite future time horizon.>

A solution for the connection of personal computers or workstations to a central station (represented by a mainframe or a server attached to a local area network) by means of a shared radio channel is presented. Issues regarding the multiple-access protocol are discussed, and an access scheme that is based on the independent stations algorithm (ISA) is presented. The necessary modifications of the basic ISA are discussed, and the overall algorithm is analyzed and tested by simulation. An implementation that is currently being developed is briefly described, focusing on the choices made for the logical link control protocol, the transmission techniques, and the interface software.>

In the class of multiple-access schemes utilizing channel feedback information, one has generally to face a multistep control problem. In order to simplify the structure of the access strategies, a single-step horizon can be considered, so that the goal becomes the maximization of the probability of transmission at each slot. However, even in this case the optimal solution turns out to be intractable for a large number of stations. To avoid this intractability some simplifying assumptions can be made at the price of a certain degree of suboptimality. Our approach is based on an independence assumption and yields a scheme which can be termed an independent stations approach (ISA).