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

In teletraffic engineering, M/G/1 queues are pivotal for optimizing network performance and operational efficiency. Closed formulas like the mean-value Pollaczek-Khinchin (MV-PK) are highly valued by practitioners due to their simplicity, facilitating rapid and flexible system analyses. This formula aids in verifying the reliability of complex simulations concerning M/G/1 models, especially when applicable. The challenge intensifies when dimensioning M/G/1 systems with heavy-tailed service time distributions. Simulation of such queues becomes notably arduous, necessitating increased number of samples and longer simulation durations. Moreover, verification becomes more intricate as the MV-PK formula cannot be straightforwardly applied in such cases. This paper extends the MV-PK formula to heavy tailed service distributions with infinite/infinitesimal moments using Nonstandard Analysis. Additionally, it shows how recently introduced Bounded Algorithmic Numbers (BANs) enables numerical verification of the extended PK formula via discrete-event simulations of the M/G/1 queue, even when predicted delay values are infinite. The implemented approach tends to converge fast and exhibits remarkable numerical robustness. It adheres to the principle of “write once, run multiple times”, as the same source code for queue simulation handles both finite and infinite variance cases.

The Internet of Things (IoT) significantly extends the attack surface of the Internet, making the use of an Anomaly-based Intrusion Detection System of paramount importance. Despite in the last years big research efforts have focused on the application of Deep Learning techniques to attack detection, an ultimate real-time solution, able to provide a high detection rate with an acceptable false alarm rate while processing raw network traffic in real time, has still to be identified. For this reason, in this paper we propose an Intrusion Detection System that, leveraging on probabilistic data structures and Deep Learning, is able to process in real time the traffic collected in a backbone network, offering almost optimal detection performance and low false alarm rate. Indeed, the extensive experimental tests, run to validate and evaluate our system, confirm that, with a proper parameter setting, we can achieve about 90% of detection rate, with an accuracy of 0.871.

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

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

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

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

In the last years CUSUM based algorithms have emerged as a good basis to develop efficient systems able to detect anomalies and attacks in the network traffic. Nevertheless, such techniques are still far from providing an ideal solution to the problem, mainly because of the huge number of false alarms that characterizes such approaches. For this reason in this paper we propose a novel detection method based on the combined use of the CUSUM algorithm and the wavelet analysis. The introduction of the wavelet analysis, as shown by the obtained results, allows us to strongly reduce the false alarm rate of the “classical” methods, still retaining excellent performance in the detection of network anomalies.

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management and many detection techniques, able to promptly reveal and identify network attacks, mainly detecting Heavy Changes in the network traffic, have been proposed. Among these, one of the most promising approach is based on the use of the CUSUM (CUmulative SUM). Nonetheless, CUSUM performance is strongly affected by its sensitivity to the presence of seasonal trends in the considered data. For this reason, in this paper we propose a novel detection method based on the idea of performing a pre-processing stage of the data by means of wavelets, aimed at filtering out such trends, before applying the CUSUM algorithm. The performance analysis, presented in the paper, demonstrates the efficiency of the proposed method, focusing on the performance improvements due to the pre-processing stage.

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

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

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper we address the problem considering a method based on PCA for detecting network anomalies. In more detail, we present a new technique that extends the state of the art in PCA based anomaly detection. Indeed, by means of the Kullback-Leibler divergence we are able to obtain great improvements with respect to the performance of the "classical" approach. Moreover we also introduce a method for identifying the flows responsible for an anomaly detected at the aggregated level. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed method.

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper we present a novel method for network anomaly detection, based on the idea of discovering Heavy Change (HC) in the distribution of the Heavy Hitters in the network traffic. To assess the validity of the proposed method, we have performed an extensive experimental evaluation phase, during which our system performance have been compared to a more “classical” HC-based approach. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed method.

The increasing number of network attacks causes growing problems for network operators and users. Thus, detecting anomalous traffic is of primary interest in IP networks management. In this paper we present a novel method for network anomaly detection, based on the idea of discovering Heavy Change (HC) in the distribution of the Heavy Hitters in the network traffic. To assess the validity of the proposed method, we have performed an extensive experimental evaluation phase, during which our system performance have been compared to a more "classical" HC-based approach. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed method.

Recently, the power consumption of the home/access networks has stimulated several research efforts devoted to the study of new mechanisms to optimize the energy utilization. In particular, most of these works is based on the idea that Ethernet links are mostly underutilized, and is thus aimed at realizing mechanisms able to adapt device consumption to the link utilization. Given the few works presenting an experimental study of the current Ethernet network devices from an energy point of view, this paper aims at presenting an extensive measurements campaign for evaluating the power consumption of two COTS Ethernet NICs. The measurements campaign, carried out following the procedures provided by the ECR Initiative™ and the SNE Energy Star, shows the energy inefficiency of the current NICs. Finally, using the experimental results, the paper presents a qualitative estimation of the energy savings achievable by implementing a well-known energy savings mechanism (i.e., ALR) in the Ethernet NICs.

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

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

In the last few years, the number and impact of security attacks over the Internet have been continuously increasing. Since it seems impossible to guarantee complete protection to a system by means of the "classical" prevention mechanisms, the use of Intrusion Detection Systems has emerged as a key element in network security.

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

In the last few years, the number and impact of security attacks over the Internet have been continuously increasing. Since it seems impossible to guarantee complete protection to a system by means of the "classical" prevention mechanisms, the use of Intrusion Detection Systems has emerged as a key element in network security. In this paper we address the problem considering some techniques for detecting network anomalies. Our approach is based on the use of different compression algorithms for detecting anomalies in the network traffic running over TCP. In more detail we take into account the use of three different compression algorithms, based on distinct approaches, namely: Huffman coding, Dynamic Markov Coding, and Lempel-Ziv-Welch algorithm. The proposed methods are based on the consideration that the entropy represents a lower bound to the compression rate that we can obtain, and that the more redundant the data are and the better we can compress them. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed methods.

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

In the last few years the number and impact of security attacks over the Internet have been continuously increasing. Since it is impossible to guarantee complete protection to a system by means of the "classical" prevention mechanisms, the use of Intrusion Detection Systems (IDSs) has emerged as a key element in network security. In this paper we address the problem considering some techniques for detecting network anomalies, based on the use of co-occurrence matrices, to model the "normal" behavior of the TCP connections.

Abstract Computer security is a growing problem. Over the last years, the number and variety of security attacks in IP‐ based network infrastructures have increasingly grown, leading to the need of developing new security architectures. In this scenario, the use of intrusion detection systems (IDSs) has emerged as a key element, since it permits to tackle security threats by masquerader, misfeasor, and clandestine users. In this paper, we address the problem considering some new statistical techniques for detecting network anomalies. In more detail, the paper discusses the use of several statistical models to characterize the normal behavior of the network traffic running over TCP, so that anomalies can be revealed as significant deviations from such behavior. Namely, our proposal is based on the use of Markov chains, co‐ occurrence matrices, and compression algorithms, for modeling the TCP connections, in terms of statistical analysis of some of the packet header fields. The performance analysis, presented in this paper, demonstrates the effectiveness of the proposed methods. Copyright © 2009 John Wiley & Sons, Ltd.

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

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

The growth of the Internet in the last years has been pushed by increasing requirements in terms of capacity, security and reliability. Moreover, improvements in multimedia applications need mechanisms and architectures to accomplish quality of service (QoS) and differentiated services. Technology development has shown that the evolution of processing power cannot cope with the link capacity growth. Therefore a link capacity scheduler is no longer sufficient to assure efficient service differentiation to end-users, but a proper computing power allocation for packet processing must be adopted. In this paper a processing scheduling scheme for IntelregIXP2XXX Network Processors is proposed. A model of the architecture is defined and an ad-hoc simulator is developed to help the comprehension of the system and the re-design of the application. Finally experimental results show the performance of the proposed algorithm.

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

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

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

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

The paper deals with the applicability of fractional AutoRegressive Integrated Moving Average (f-ARIMA) processes to traffic modelling. This study is suggested by the ability of f-ARIMA processes in separately capturing both the long range dependence (LRD) and short range dependence (SRD) features of actual traffic. Indeed, the f-ARIMA models permit one to produce synthetic traces with a well-controlled LRD/SRD component ratio. The practical relevance of this feature is tested analytically, by means of discrete event simulations, the queueing performance in several working conditions for two different actual-traffic data sets, respectively related to a LAN-to-LAN interconnection and a videoconference service. In particular, the latter data set, characterised by a strong "low frequency" SRD component, emphasises the improvement on queueing performance evaluation introduced by a low-order f-ARIMA (p,d,0) model with respect to a pure LRD one. On the contrary, the inadequacy of the f-ARIMA model is highlighted when considering the LAN traffic characterised by a quite complex SRD component.

The paper presents joint measurements of IP and ATM traffic over an MPOA based campus network. The search for parsimonious realistic modeling of heterogeneous aggregated traffic has led to the generalization of on/off modeling schemes. Even a single on/off pattern with Pareto-geometric distributions of the sojourn times in each state determines a hyperbolic decay of the autocovariance function corresponding to long range dependence (LRIB). Hence, adequate on/off models can fit real traces corresponding to the multiplexing of several heterogeneous connections. In the paper, the on/off behavior of the traffic is evidenced at the ATM level. The relevant feature of the traces is that either the on and the off sojourn times could be well approximated by a light tailed distribution. The observed LRD behavior is due to the presence of correlation in the sequence of on (as well as off) periods lengths. In order to confirm this hypothesis, different synthetic traces were considered, verifying the LRD behavior by means of wavelet analysis. Implications of such state correlations on queueing behavior are then compared by means of discrete event simulations.

A key feature of integrated services networks is their ability to provide a variety of quality of service (QoS) guarantees to different applications. To this end scheduling systems can be employed. A primary QoS parameter is the cell loss probability (CLP), whose typical values are very small and difficult to estimate by means of standard simulation schemes. We propose an application of the importance sampling (IS) technique to efficiently estimate the CLP of an ideal two-queue generalized processor sharing (GPS) scheduling discipline. We subsequently apply this algorithm to simulate a realistic scheme, namely the packet-by-packet generalized processor sharing (PGPS). We model input traffic as Markov arrival processes (MAPs). The algorithm we present is based on large deviation results, which provide the asymptotic decay rate of per-session queue length tail distributions.