The rapid development and scaling of mobile telecommunications networks, together with related domains such as the edge-cloud continuum have raised significant concerns regarding energy consumption and environmental sustainability. Addressing these concerns requires a focus on CPU energy consumption, as CPUs are among the largest energy consumers in these systems. This paper investigates existing techniques, with a focus on CPU idle states (C-states), performance states (P-states), and frequency scaling governors implemented at both hardware and software levels. These mechanisms enable the dynamic adjustment of CPU parameters, providing opportunities to optimize power consumption, frequency, voltage, and overall system performance. In this regard, three CPUs with different architectures from well-known manufacturers, Intel® and AMD®, are thoroughly examined. A comprehensive dataset, collected under three load scenarios (idle, medium, and high), is used to support the analysis, reflect realistic runtime conditions, and enable a comparison of the technological differences in how these parameters are exposed and utilized.

In order to satisfy the ethical requirements that are expected from upcoming 6G technologies, the knowledge of the power consumption ascribable to the applications and network functions is crucial to enforce a sense of responsibility and joint efforts towards value-driven sustainability. In this respect, this paper presents a modular, energy-focused prototype of the Management Data Analytics Function (MDAF), that represents the cornerstone of the Observability framework recently developed by the 6Green Project. Its main goal is to provide management data to upper layers (i.e., network and end-users/verticals). A 5G network was tested on both the User-Plane and Control-Plane; at the same time, said network was monitored by both the proposed MDAF and a common power monitoring solution: Scaphandre. Results show that the MDAF measures a higher power consumption than Scaphandre; the difference lies between 50% (when idle) and 4% (at the maximum offered load). This difference corresponds to the ”indirect” power consumption that the MDAF is able to ascribe to the containers/Virtual Machines (VMs) (i.e., the Network Functions (NFs)). The more accurate power consumption measurements of the single NFs is a first step towards the need to spread energy/carbon awareness to all the involved stakeholders.

The virtualization of UltraSound (US) medical imaging systems through cloud-edge computing offers a transformative approach to healthcare, overcoming limitations of traditional on-premises systems. By dematerializing US system functions and migrating them to the cloud, barriers related to hardware and localization are eliminated, enabling a more agile and scalable approach to medical imaging. This paradigm shift leverages virtual objects and composite virtual objects to intelligently manage and orchestrate physical and virtual components, optimizing system performance and meeting service requirements.

The emergence of 5G systems and beyond, along with state-of-the-art cloud and software engineering solutions, have brought to a radical shift of networking technologies with respect to the previous generations. However, the increased performance and flexibility have come at the cost of reduced sustainability, not only from an environmental standpoint but, in turn, affecting economic and societal sustainability as well. Infrastructure owners can introduce power management solutions in their datacenters, but the improvement is negligible unless tenant operating on top of the physical infrastructure are enabled and incentivized to customize their slices and their applications towards more energy efficient and carbon neutral profiles. For these reasons, the $\mathbf{6 G r e e n}$ project has designed an Observability framework for estimating the energy consumption and the carbon footprint of an entire network slice, or of edge-computing applications attached to it. This paper highlights the rationale behind the 6 Green approach and provides a high-level description of the observability framework.

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.

Digital services and digital service chains are the heart beating of the modern economy. Their composition involves several players, i.e., processes, software, devices, and many kinds of data exchanged among them. In such a scenario, it is important to guarantee data confidentiality, integrity, as well as authentication and authorization procedures between the communicating parties of a service chain. Cyber-security frameworks are explicitly designed for this purpose. They rely on the integration of different software modules, mutually interfaced to accomplish complex security tasks. Nevertheless, it is important to guarantee a high level of protection during data exchange among the modules. Currently, standardized authentication and authorization mechanisms are implemented through proprietary “As-a-Service” products, but the deployment of a mature on-premise solution is still missing. To bridge this gap, this contribution proposes an authentication and authorization module that automatically protects the information flowing among the modules of cyber-security frameworks. It guarantees resource availability only to authenticated subjects. Thus, their operations are confined in what actions they are authorized for. The proposed module has been implemented and tested in a real cyber-security framework under development into the H2020 GUARD project. Experimental tests show that the proposed module enables authentication and authorization procedure delegation among GUARD modules, which eases their implementation, while maximizing the flexibility of the set of access control policies and an efficient protection of the services.

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.

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.

Today’s networks and services undoubtedly require a high level of protection from cyber threats and attacks. State-of-the-art solutions that implement Machine Learning (ML) have shown to improve the accuracy and confidence in threat detection compared to previous approaches, making it suitable for detecting today’s sophisticated attacks such as Distributed Denial of Service (DDoS). However, in real-world deployments, input data streams take large bandwidth and processing capacity, especially for Deep Learning (DL) solutions that require extensive input data. On the other hand, deployment environments usually have limited bandwidth and computing resources, such as in the Internet of Things (IoT). Thus, a lightweight detection solution that satisfies such constraints is needed. In this paper, we utilize a feature reduction approach for our DL-based DDoS detector based on the Analysis of Variance (ANOVA), which is used to identify important data features and reduce the data inputs needed for detection. Our result shows that we can reduce the data input needed by up to 84.21% while only reducing 0.1% detection accuracy. We also provide a detailed analysis of the characteristics of DDoS attacks using ANOVA and compared our work with recent DL-based DDoS detection systems to demonstrate that our results are comparable to existing approaches.

Distributed Denial of Service (DDoS) attacks disrupt global network services by mainly overwhelming the victim host with requests originating from multiple traffic sources. DDoS attacks are currently on the rise due to the ease of execution and rental of distributed architectures such as the Internet of Things (IoT) and cloud infrastructures, which could potentially result in substantial revenue losses. Therefore, the detection and prevention of DDoS attacks are currently topics of high interest. In this study, we use traffic flow information to determine if a specific flow is associated with a DDoS attack. We used traditional Machine Learning (ML) methods in developing our DDoS detector and applied an exhaustive hyperparameter search to optimize their detection capability. Using lightweight approaches is suitable for resource-constrained environments such as IoT to reduce computing overhead. Our evaluation shows that most algorithms provide satisfactory results, with Random Forests achieving as high as 99% of detection accuracy, which is similar to the performance of current deep learning solutions for DDoS detection.

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.

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.

The idea of proxying network connectivity has been proposed as an efficient mechanism to maintain network presence on behalf of idle devices, so that they can "sleep". The concept has been around for many years; alternative architectural solutions have been proposed to implement it, which lead to different considerations about capability, effectiveness and energy efficiency. However, there is neither a clear understanding of the potential for energy saving nor a detailed performance comparison among the different proxy architectures. In this paper, we estimate the potential energy saving achievable by different architectural solutions for proxying network connectivity. Our work considers the tradeoff between the saving achievable by putting idle devices to sleep and the additional power consumption to run the proxy. Our analysis encompasses a broad range of alternatives, taking into consideration both implementations already available in the market and prototypes built for research purposes. We remark that the main value of our work is the estimation under realistic conditions, taking into consideration power measurements, usage profiles, and proxying capabilities.

The objective of this paper is to determine how the incoming and outgoing traffic patterns provided by the TCP impact the energy efficiency of a networked device under realistic scenarios. We set up a complex test-bed that allowed us to perform a large number of measurements of energy- and network-performance indexes on a general-purpose computing system with power management capabilities, and the Linux operating system. The performed measurements gave us a chance not only 1) to provide a complete energy profiling of TCP connections, but also 2) to analyze the role of underlying network protocols in detail, and 3) to identify specific situations where current network protocols may trigger high inefficiencies in networked hosts. Based on the obtained results, we also conducted further experiments for evaluating techniques for reducing the energy consumption induced by TCP data transfers under different scenarios.

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.

Personal communication devices are extensively used for mobile computing thanks to ever increasing wireless coverage and processing capability. However, they are constrained in terms of both human interaction (screen size and resolution, keyboard) and lifetime; hence mobile computing often relies on the possibility to migrate applications and communication sessions among several (fixed or mobile) devices. Session migration is a challenging feature, especially when communication with other hosts is involved, and the current Internet architecture does not support natively this paradigm. In this paper, we address seamless and transparent session migration for the Transmission Control Protocol (TCP) that maintains compatibility with current Internet; hence, our work keeps the remote TCP peer unaware of the migration process. We describe the latest features of the Linux kernel that allow the migration of a TCP session and a couple of alternatives to divert network packets towards the new destination.

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.

Multi-core processors optimized for networking applications typically combine general-purpose cores with offloading engines to relieve the processor cores of specialized packet processing tasks, such as parsing, classification, and security. Unfortunately, modern embedded operating systems still lack an effective and advanced hardware abstraction to exploit these aspects optimally. Based on these considerations, this paper proposes a novel framework, OpenFlow in the Small (OFiS), specifically designed to provide a flexible hardware abstraction layer for heterogeneous multi-core systems with advanced hardware accelerators for network offloading. OFiS represents such accelerators as standard OpenFlow switches inside the processor, moving the edge of the OpenFlow network management to the computational resources inside the end-boxes. As indicated in the experimental evaluation, OFiS exploits hardware parallelism and consolidates the software tasks at finer granularities.

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.

This paper deeply and experimentally analyzes the efficiency of low power idle techniques when applied to packet processing engines of network devices. To this purpose, we set up a complex testbed that allowed us to perform several measurements on energy- and network-performance indexes. The reference device platforms that we selected for this evaluation are new generation software routers based on component-off-the-shelf hardware, since they already include advanced power management capabilities, and can be considered as a significant example for next-generation green network devices. The results collected in the measurement campaign allowed us not only (i) to provide an in-depth energy consumption profiling of SR data-plane, but also (ii) to clearly outline energy costs due to the use of low power idle techniques. Among other interesting aspects, we completely characterized the energy consumption due to wakeup transitions, which may cause instantaneous consumption spikes higher than 4 times the power energy requirement when active.

Multi-core processors for networking applications typically combine general-purpose cores with off-loading engines, to relieve processor cores of specialized packet processing tasks, such as parsing, classification, and security. Unfortunately, modern embedded operating systems still lack of an effective support and hardware abstraction for optimally exploiting the above mentioned aspects. Starting from these considerations, this paper proposes a novel framework, “OpenFlow in the Small” (OFiS), specifically designed to provide a flexible hardware abstraction for a wide set of heterogeneous multi-core processors with advanced network off-loading capabilities. The OFiS framework allows SW services/applications, or only the operating system, activating and customizing off-loading operations, and distributing them to processor cores on a per-flow basis.

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.

In this paper, we consider energy-aware network devices (e.g. routers, switches, etc.) able to trade their energy consumption for packet forwarding performance by means of DVFS techniques. We focus on state-of-the-art packet processing engines, which generally represent the most energy-starving 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, in order to optimize the trade-off between energy consumption and network performance indexes. With this aim, we propose and analyze a constrained optimization policy, which tries to find the best trade-off between power consumption and packet latency times. In order to deeply understand the impact of such policy, a number of tests have been performed by using real-world traffic traces.

Advanced power management capabilities have been proposed to be included into next-generation green network devices in order to modulate their energy requirements according to the workload. The clear side effect of enabling these new capabilities consists in a performance level reduction of network devices. Starting from some existing benchmarking standards for evaluating energy-efficiency, namely ECR and ATIS-060015, this contribution is devoted to determining a set of parameters, and methodologies that can be applied to correctly and precisely evaluate the tradeoff between energy consumption and network performance. Some experimental results obtained with the proposed indexes and methodologies, as well as a green SW router prototype have been provided.

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.

In this work, our main objective is to explore how Software Routers (SRs) can deploy advanced and flexible paradigms for supporting novel control plane functionalities and applications. To this purpose, we investigate and study a new open-source SW framework, namely Distributed SW ROuter Project (DROP), which aims at developing and enabling a novel distributed paradigm for IP-router control and management. DROP is partially based on the main guidelines of the IETF ForCES standard, and it allows building logical network nodes through the aggregation of multiple SRs, which can be devoted to packet forwarding or control operations. In addition to the original ForCES design, DROP will aim at extending router distribution and aggregation concepts by moving them at a network-wide scale in order to enable and to support value-added services for next-generation networks.

Pervasive communications are greatly affected by mobility issues. Users often move and that implies different devices and networks to become available dynamically. Terminal handovers and session migrations could negatively impact the enjoyment of multimedia content in such scenarios. In this paper, we introduce the concept of personal address and proposed a mobility framework around it, which accounts for all aspects of mobility. Moreover, we describe an implementation of this framework for interactive voice-over-IP and for streaming applications; finally, we report the results from our experimental testbed.

We consider a new generation of COTS software routers (SRs), able to effectively exploit multi-Core/CPU HW platforms. Our main objective is to evaluate and to model the impact of power saving mechanisms, generally included in today's COTS processors, on the SR networking performance and behavior. To this purpose, we separately characterized the roles of both HW and SW layers through a large set of internal and external experimental measurements, obtained with a heterogeneous set of HW platforms and SR setups. Starting from this detailed measure analysis, we propose a simple model, able to represent the SR performance with a high accuracy level in terms of packet throughput and related power consumption. The proposed model can be effectively applied inside "green optimization" mechanisms in order to minimize power consumption, while maintaining a certain SR performance target.

Current trends in smart homes suggest that several multimedia services will soon converge towards common standards and platforms. However this rapid evolution gives rise to several issues related to the management of a large number of multimedia streams in the home communication infrastructure. An issue of particular relevance is how a context acquisition system can be used to support the management of such a large number of streams with respect to the Quality of Service (QoS), to their adaptation to the available bandwidth or to the capacity of the involved devices, and to their migration and adaptation driven by the users’ needs that are implicitly or explicitly notified to the system. Under this scenario this paper describes the experience of the INTERMEDIA project in the exploitation of context information to support QoS, migration, and adaptation of multimedia streams.

Over the last few years, peer-to-peer (P2P) file sharing applications have evolved to become a major traffic source in the Internet. The ability to quantify their impact on the network, as a consequence of both signaling and download traffic, is fundamental to a number of network operations, including traffic engineering, capacity planning, quality of service, forecasting for long-term provisioning, etc. We present here a measurement study on the characteristics of the traffic associated with different P2P applications. Our aim is to offer useful insight into the nature of P2P traffic, which we consider a step toward building P2P traffic aggregates generators in simulative environments. We show that P2P traffic can be divided into two distinguished behavioral profiles, which, independently of the application protocol, present significant differences in the average and standard deviation of four measurements: arrival times, durations, volumes and average packet sizes ofP2P conversations. These profiles well represent the typical behavior of signaling and download traffic. Based on our findings, we argue that, if such distinction is not taken into account, the statistical measurements needed to model P2P traffic aggregates would result biased, and potentially bring to misleading results.

Scarceness of bandwidth is a common problem to all radio networks. The cellular structure with frequency reuse has been the solution for many years in the past; however, decreasing cell sizes results in increasing handoff requests and this could be critical in high mobility environments. Moreover, the cellular structure cannot ensure the best utilization of resources when the distribution of the users inside the system is not constant in time. In this paper we focus on handoff protection and efficient dynamic bandwidth allocation for multi-service networks in high- mobility environments, in presence of very simple strategies for the call admission control, in order to assure scalability. We propose to find an optimal solution by using a new simple heuristic which exploits the prediction of the system behaviour made by some suitable network model; moreover, a more traditional approach based on a stochastic algorithm is derived from the Monte Carlo methods for comparison. Simulation results show that the proposed heuristic approach performs better, especially in critical situations.

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.

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 event-driven simulation tool aimed at testing access control mechanisms in an asynchronous transfer mode (ATM) environment is presented. It is dedicated to the behavioural description of an ATM virtual circuit (VC) switch. The purpose is to test resource allocation and call admission control (CAC) strategies; so, only the relevant events for this objective are considered and some important functions (e.g., the switching element) for a complete description of an ATM switch are not explicitly modelled. Along with the description of the simulation tools; some resource allocation and CAC are also reported, and several simulation results are discussed, in order to assess their performance.

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.>