This paper presents a stochastic framework for managing the performability (performance and availability) of 5G-based service function chains (SFCs). By integrating an$M / G / m$queueing model for latency estimation and Stochastic Reward Networks (SRNs) for availability assessment, we evaluate the impact of software rejuvenation on 5 G network performability. The final goal is to derive the optimal 5G setting that meets both performance (e.g., delay threshold) and availability (e.g., the “five nines”). Our testbed, based on Open5GS, validates the model and provides insights into optimal 5G settings that balance performance, availability, and resource utilization.

In modern telecommunication networks, services are provided through Service Function Chains (SFC), where network resources are implemented by leveraging virtualization and containerization technologies. In particular, the possibility of easily adding or removing network resources has prompted service providers to redefine some concepts including performance and availability. In line with this new trend, we propose a performability study of a multi-provider containerized IP Multimedia Subsystem (cIMS), an SFC-like infrastructure used in the core part of 4G/5G networks to handle multimedia sessions. On the one hand, performance issues are tackled by modeling each cIMS node in terms of a G/G/m queueing system to derive the Call Setup Delay (CSD), a performance metric related to the user-end experience in multimedia communications. On the other hand, availability issues are addressed through the Multi-State System (MSS) formalism, to take into account different performance rates of the system. Then, we devise an algorithm called PE-MUGF (Performability Evaluation through Multidimensional Universal Generating Function) to identify the minimum-redundancy cIMS configuration which meets given performance and availability targets at the same time. Finally, an extensive experimental analysis based on Clearwater, a containerized IMS testbed, allows us to estimate most of system parameters whose robustness is evaluated through a sensitivity analysis.

We advance a performability assessment of a multi- tenant containerized IP Multimedia Subsystem (cIMS), i.e.: one and the same infrastructure is shared among different providers (or tenants). Specifically, we: i) model each cIMS node (a.k.a. Containerized Network Function - CNF) through the Multi-State System (MSS) formalism to capture the dimensionality of the multi-tenant arrangement, and characterize each tenant through queueing theory attributes to catch latency-dependent performance aspects; ii) afford an availability analysis of cIMS by means of an extended version of the Universal Generating Function (UGF) technique, dubbed Multidimensional UGF (MUGF); iii) solve an optimization problem to retrieve the cIMS deployment minimizing costs while guaranteeing high availability requirements. The whole assessment is supported by an experiment based on the containerized IMS platform Clearwater which we deploy to derive some realistic system parameters by means of fault injection techniques.

As a case study of a novel approach to characterize service chains in terms of performance and availability, we consider a containerized IP Multimedia Subsystem (cIMS) infrastructure. The performance analysis is carried out by exploiting the queueing network decomposition method useful to model each cIMS node as a realistic M/G/c system, jointly with the solution of a convex optimization problem for containers allocation. Such a solution is used to feed the availability analysis (faced through the Stochastic Reward Network technique) amenable to derive a set of configurations guaranteeing a given availability target at minimum cost. The whole analysis is supported by a testbed based on the Clearwater platform used to derive some experimental parameters values.

The failure of a single network element composing a Service Function Chain (SFC) unavoidably leads to some degradation in terms of availability (ability of guaranteeing working conditions), and/or performance (ability of sustaining a certain workload) for the whole SFC. By considering both of these aspects, we propose, as a case study, a joint analysis of availability and performance (a.k.a. performability) of IP Multimedia Subsystem, an SFC infrastructure which plays a key role in the all-IP convergence of telecommunication services, especially as per prospects of 5G . We refer to an implementation of IMS based on container technology (containerized IMS, or cIMS) which allows to decouple the application layer from the underlying hardware infrastructure more efficiently than classic virtualization schemes. We model the probabilistic behavior of a cIMS by means of Stochastic Reward Networks (SRN) and Reliability Block Diagram (RBD) formalisms to take into account failure and repair events. Then, with the assistance of a designed-from-scratch algorithm (OptChains+), we carry on a performability analysis: i) to evaluate and compare series/parallel cIMS configurations (or settings), and ii) to find settings with minimum cost and maximum availability, given a performance level. The proposed assessment lends itself to a sensitivity analysis, here demonstrated by examples, useful for robustness evaluation.

The Network Function Virtualization (NFV) paradigm has been devised as an enabler of next generation network infrastructures by speeding up the provisioning and the composition of novel network services. The latter are implemented via a chain of virtualized network functions, a process known as Service Function Chaining. In this paper, we evaluate the availability of multi-tenant SFC infrastructures, where every network function is modeled as a multi-state system and is shared among different and independent tenants. To this aim, we propose a Universal Generating Function (UGF) approach, suitably extended to handle performance vectors, that we call Multidimensional UGF. This novel methodology is validated in a realistic multi-tenant telecommunication network scenario, where the service chain is composed by the network elements of an IP Multimedia Subsystem implemented via NFV. A steady-state availability evaluation of such an exemplary system is presented and a redundancy optimization problem is solved, so providing the SFC infrastructure which minimizes deployment cost while respecting a given availability requirement.

Today, modern telco infrastructures are espousing softwarized paradigms (e.g. virtualization, containerization), which are necessary to implement the network slicing, and, consequently, to achieve a beneficial trade-off between service offered and costs. In particular, container-based technologies, when compared to classic virtualized frameworks, offer a lightweight environment to host novel network services. Inspired by these last trends, in this work we propose a statistical characterization of a containerized version of IP Multimedia Subsystem (cIMS), one of the crucial parts of 5G core network. Precisely, we: i) exploit the Queueing Networks (QN) formalism to model the chained behavior of a cIMS infrastructure; ii) perform a statistical assessment aimed at analyzing both the queueing dynamics in different scenarios (single/multi class), and at selecting the optimal cIMS deployment guaranteeing the minimum response time at a given cost; iii) carry on an experimental analysis through Clearwater platform to extract realistic estimates of system parameters.

The reputation of network providers strongly depends on their ability to guarantee high performance levels of virtualized infrastructures, and to maintain strict Quality-of-Service (QoS) requirements, thus, the concept of "five nines" or high availability (HA) is critical. It means that, on average, the continuity of a provided service cannot be violated for more than about five minutes per year. In this direction, we propose a framework useful to design and model, from a HA perspective, the Service Function Chains (SFCs) whose chained software logic is embodied in many softwarized telco infrastructures (e.g. IP Multimedia Subsystem elected here as a representative use case). The proposed framework interacts with TimeNET tool, and offers interesting functionalities such as: i) generating stochastic models of SFCs based on the SRN (Stochastic Reward Nets) formalism; ii) deploying network scenarios via drag-and-drop operations for basic users, or modifying the underlying SRN models for advanced users; iii) setting a variety of parameters (mean-time-to-failure/repair, software/hardware specs, redundancy, etc.); iv) presenting availability results in tabular and/or graphical forms.

IP Multimedia Subsystem (IMS) represents a crucial element for the convergence of telecommunication systems heading towards 5G solutions. Actually, IMS offers a standardized and vendor independent model allowing network providers to supply multimedia services such as video streaming or HD voice, with pressing QoS requirements. The IMS architecture can dramatically improve its flexibility when deployed within softwarized environments, in conjunction with virtual or container-based technologies. This latter, in particular, realizes an abstraction of software resources from the underlying hardware in a more efficient and resource saving manner than virtual machines. Inspired by this model, we propose a tool for the performability management of IMS architectures deployed in a containerized environment (dubbed cIMS). First, we model the stochastic behavior of a cIMS by means of two complementary formalisms: i) Reliability Block Diagram (RBD) amenable to model high level interconnections among cIMS nodes, and ii) Stochastic Reward Networks (SRN) useful to capture deeper details of a single node in terms of failure and repair events. Then, we develop an automated procedure aimed at supporting the performability management of cIMS deployments that must satisfy the optimal trade-off among high availability requirements, capacity load, and deployment costs.

Nowadays, telecom providers may benefit from the flexibility offered by Network Function Virtualization (NFV) paradigm that allows to decouple the service logic from the underlying hardware infrastructure. Thus, main functionalities of network nodes (e.g. routers, firewalls, load balancers etc.) can be deployed on a virtual machine (VM) with its own resources. On the other hand, deploying a whole VM (which hosts a single virtualized network service) can be expensive, since too many resources are uselessly wasted. A valuable alternative is offered by containers, namely, virtualized and lightweight processes that, differently from classical VMs, do not carry a whole operating system. In this work we consider a container-based version of IP Multimedia Subsystem (IMS) infrastructure, a crucial player within next generation telecommunication networks, a.k.a. 5G. More precisely, we offer an availability evaluation of a containerized IMS (cIMS) deployment through i) Reliability Block Diagram (RBD) formalism useful to model high-level interconnections among cIMS nodes, and ii) Stochastic Reward Networks (SRN) methodology which allows to analyze the evolution of the cIMS life cycle in presence of failure and repair events. Moreover, we perform a sensitivity analysis aimed at evaluating the whole cIMS robustness with respect to deviations of some critical parameters.

We consider the problem of mitigating the effect of malicious cyber-threats spreading across multiple subnets of a data network. Three fundamental issues arise: 1) providing a manageable model of threat propagation; 2) quantifying the danger associated to different subnets; and 3) optimizing the allocation of the countermeasures. We address these issues by providing the following novel contributions. First, a convenient mathematical abstraction of threat propagation is proposed, which employs the birth-and-death process with immigration pioneered by Kendall in his seminal work of 1948. Then, exploiting the notable properties of such a model, we show how to retrieve analytical solutions for optimal resource allocation across subnets, for the case where the parameters of the attack are perfectly known. Finally, the assumption of perfect knowledge is removed, and the unknown attack parameters are estimated using maximum-likelihood estimators.

Distributed Denial-of-Service (DDoS) attacks are usually launched through the botnet, an “army” of compromised nodes hidden in the network. Inferential tools for DDoS mitigation should accordingly enable an early and reliable discrimination of the normal users from the compromised ones. Unfortunately, the recent emergence of attacks performed at the application layer has multiplied the number of possibilities that a botnet can exploit to conceal its malicious activities. New challenges arise, which cannot be addressed by simply borrowing the tools that have been successfully applied so far to earlier DDoS paradigms. In this paper, we offer basically three contributions: 1) we introduce an abstract model for the aforementioned class of attacks, where the botnet emulates normal traffic by continually learning admissible patterns from the environment; 2) we devise an inference algorithm that is shown to provide a consistent (i.e., converging to the true solution as time elapses) estimate of the botnet possibly hidden in the network; and 3) we verify the validity of the proposed inferential strategy on a test-bed environment. Our tests show that, for several scenarios of implementation, the proposed botnet identification algorithm needs an observation time in the order of (or even less than) 1 min to identify correctly almost all bots, without affecting the normal users' activity.

Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique is assessed via numerical simulations based on SEVIRI data.

The detection of encrypted real-time traffic, both streaming and conversational, is an increasingly important issue for agencies in charge of lawful interception. Aside from well established technologies used in real-time communication (e.g. Skype, Facetime, Lync etc.) a new one is recently spreading: Web Real-Time Communication (WebRTC), which, with the support of a robust encryption method such as DTLS, offers capabilities for encrypted voice and video without the need of installing a specific application but using a common browser, like Chrome, Firefox or Opera. Encrypted WebRTC traffic cannot be recognized through methods of semantic recognition since it does not exhibit a discernible sequence of information pieces and hence statistical recognition methods are called for. In this paper we propose and evaluate a decision theory based system allowing to recognize encrypted WebRTC traffic by means of an open-source machine learning environment: Weka. Besides, a reasoned comparison among some of the most credited algorithms (J48, Simple Cart, Naive Bayes, Random Forests) in the field of decision systems has been carried out, indicating the prevalence of Random Forests.

In several human activities, such as agriculture and forest management, the monitoring of radiometric surface temperature is key. In particular both high spatial resolution and high acquisition rate are desirable but, due to the hardware limitations, these two characteristics are not met by the same sensor. The fusion of remotely sensed data acquired by sensors with different spatial and temporal resolution is a profitable choice to face this issue. When the real-time requirement is relaxed, the data sequence can be processed as a whole, allowing to improve the final result. Within this framework, we propose a novel batch sharpening strategy, relying on interpolation, data fusion and Bayesian smoothing techniques, and we assess its effectiveness on SEVIRI and MODIS thermal data.

A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms.

The continuous time monitoring of the radiometric surface temperature by means of high spatial resolution images is desirable in agricoltural applications, such as irrigation management. Since the requirement of high spatial and temporal resolutions can hardly be met by a single sensor, we resort to a fusion strategy of data from multiple sensors. Specifically we consider the Interacting Sequential Bayesian Estimation strategy, as it is able to deal with the sudden changes observed in the temperature dynamics. The method has been validated on SEVIRI TIR real data, properly spatially degraded in order to mimic sensors with different characteristics.

The paper presents an architectural proposal that integrates a vehicle parking system, which offers a series of specific services (billing, targeted marketing, etc.), into the 3GPP IP Multimedia Subsystem (IMS), with a specific focus on the localization of the vehicle in indoor areas. The vehicle is equipped with a device hosting an ad-hoc software client exploiting the Wi-Fi network card. The solution takes advantage of the widespread adoption of the 802.11x WLAN technology in order to provide a value added service using already installed infrastructure. The testing area is an indoor parking lot and the IT infrastructure includes several Wi-Fi Access Points appropriately located inside the parking area, some user equipments interacting with them, a Location Server able to provide an estimate of a vehicle's position and a Presence Server offering specific location-based services on behalf of the IMS infrastructure.

The design of multitarget tracking procedures includes, as the most time consuming steps, the definition of the objective class and the formulation of the detection criteria. In this paper we investigate a solution toward an intuitive way for implementing a detector for any ad-hoc application. We capitalize on the OSPA metric to discriminate between the semantic object class of interest and other look-alike classes starting from a short number of unlabeled markers. We propose an illustrative algorithm with a toy example, then we apply it to two real images, the first acquired by SEVIRI, the second by MERIS. In the first case we discriminate between lakes, sea and look-alike clouds, in the other between ground and sea ice. We show how semantic classes with very similar spectral properties can be separated even in the presence of uncertainties or errors in the ground truth.

Pansharpening algorithms aim to enhance low resolution multi-spectral images by means of high resolution panchromatic ones. Several approaches are based on the MultiResolution Analysis (MRA) achieved through the pyramidal decomposition of images. We focus here on the implementation based on Morphological Filters (MF) that are optimized through Genetic Algorithms (GA). The effectiveness of this algorithm is compared with other techniques, among which those based on Wavelet operators, through several quality indices on two different real scenarios.

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field MAP-MRF framework by adding a penalization term which is determined according to the hystory of cloud masses. Multi Target Tracking of clouds is accomplished by methods of Finite Set Statistics (FISS) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.

In order to evaluate performance of mobile networks, it is necessary to consider the occurrence of random failures causing performance degradation and the consequent repair actions. This approach is especially suitable for next generation networks based on the Third Generation Partnership Project (3GPP) IP Multimedia Subsystem (IMS), as a consequence of the very high Quality of Service levels required by telecommunication operators' subscribers. IMS core network signalling servers can be modeled as multi-state elements, where server states correspond to different performance levels. The number of sessions handled by a single server per time unit is one of the performance figure that can be considered. Given a demand profile, some redundancy techniques must be adopted to meet the typical requirements for a telecommunication network in terms of service availability and, in this paper, a redundancy optimization problem is solved by using a Universal Generating Function approach.

Wireless location of a User Equipment (UE) has received growing attention in recent years. The first step for the design of a wireless location system consists in choosing the system architecture and the localization algorithm that match the requirements of the working scenario. In this paper the area of interest is represented by an indoor parking lot, in which the variable occupancy of motor vehicles alters the electromagnetic propagation and causes large errors in vehicle location estimation. The proposed strategy to deal with this problem is the use of a server-based architecture, that ensures security and scalability and accounts for the system state in terms of number and positions of already present vehicles. This concept of state is shown to be useful to design suitable algorithms, based on simplified electromagnetic models, to improve the localization performance.

In this paper, the problem of tracking cooperative mobile nodes in wireless sensor networks is addressed. Aiming at an efficient resource solution, the research adopts a strategy of combining target tracking with node selection procedures in order to select informative sensors to minimize the energy consumption of the tracking task. We devise a cluster-based architecture to address the limitations in computational, battery power and communications of the sensor devices. To track mobile nodes two kinds of particle filters, bootstrap and unscented particle filter, are considered to estimate the actual position and predict future locations. The particle filters have been already used in tracking algorithms, but their energy efficiency has received less attention. To save energy, a node selection procedure is proposed. The node selection problem is formulated as a cross-layer optimization problem and it is solved using greedy algorithms.

This paper aims to review some reliability and availability methodologies suitable to characterize telecommunication networks behavior, in particular, in the presence random failures of network elements. This approach is becoming more and more relevant to assess the Quality of Service offered by a telecom operator to its subscribers, also known as performability. In order to clarify this approaches, we present some examples related to next generation networks based on the IP Multimedia Subsystem. Some notions about the emerging requirements of network survivability are given.

Multimedia communications over Internet should achieve adequate quality of service while maintaining 'fairness' in network resources allocation with respect to competing connections. To achieve these conflicting requirements the adopted transport protocol should provide an adequate average throughput and implement congestion control and flow control mechanisms designed to minimize packet loss, delay and throughput variations. The transport protocol proposed here is a modified version of the window-based datagram congestion control protocol, that implements a TCP-like congestion control mechanism wherein the multiplicative decrease of the congestion window is controlled by a non-linear function. Experimental results show that this modified congestion control algorithm, which is adjusted at run-time based on the estimated mean round-trip time, is good candidate toward the aforementioned requirements.

Coherence is the key factor in Synthetic Aperture Radar Interferometry. We study the baseline decorrelation due to antenna spatial diversity in order to take into account the effect of the surface statistic roughness in a more general case. As a model of surface roughness we use the fractional Brownian motion

The paper deals with the detection of signals with unknown parameters in impulsive noise, modeled as a spherically symmetric random process. The proposed model subsumes several interesting families of noise amplitude distributions: generalized Cauchy, generalized Laplace, generalized Gaussian, contaminated normal. It also allows handling of the case of correlated noise by a whitening approach. The generalized maximum likelihood decision strategy is adopted, resulting in a canonical detector, which is independent of the amplitude distribution of the noise. A general method for performance evaluation is outlined, and a comprehensive performance analysis is carried out for the case of M-ary equal-energy orthogonal signals under several distributional assumptions for the noise. The performance is contrasted with that of the maximum likelihood receiver for completely known signals, so as to assess the loss due to the a-priori uncertainty as to the signal parameters.>

In a decentralized hypothesis testing network, several peripheral nodes observe an environment and communicate their observations to a central node for the final decision. The presence of capacity constraints introduces theoretical and practical problems. The following problem is addressed: given that the peripheral encoders that satisfy these constraints are scalar quantizers, how should they be designed in order that the central test to be performed on their output indices is most powerful? The scheme is called cooperative design-separate encoding since the quantizers process separate observations but have a common goal; they seek to maximize a system-wide performance measure. The Bhattacharyya distance of the joint index space as such a criterion is suggested, and a design algorithm to optimize arbitrarily many quantizers cyclically is proposed. A simplified version of the algorithm, namely an independent design-separate encoding scheme, where the correlation is either absent or neglected for the sake of simplicity, is outlined. Performances are compared through worked examples.>

In some applications, estimation of probabilities of the order of 10/sup -6/ or lower is required. Extrapolative methods based on extreme value theory (EVT) and generalized extreme value theory (GEVT) are considered. They give a closed-form approximation of the probability tail in terms of two or three parameters, respectively. A Monte Carlo simulation study was carried out to assess EVT and GEVT estimators' performances with respect to several factors, such as estimation methods, initial distribution, sample size, and partition of the initial sample. It was found that the GEVT estimators are consistently more efficient, while achieving a substantial sample-size saving with respect to the conventional counting procedure. Some applications to estimation problems encountered in radar systems design are considered.>

The signal design problem for FSK communication via fading dispersive channels is considered. The channel is modeled as a linear filter whose time-varying impulse response is a sample function from a zero-mean Gaussian random field of arbitrary WSSUS type. The additive noise component in the received waveforms is supposed to be a zero-mean white Gaussian random process, and maximum likelihood demodulation is assumed. The signal design procedure here adopted consists of minimizing a known upper bound on the error probability, whereas the previous similar design method by Daly intended maximizing an upper bound on the detection probability for radar-astronomy targets. Though with slightly different optimal numerical values, here, as in Daly's problem, the signal design depends on a single parameter which is a simple functional of the channel timefrequency covariance function and of the signal envelope ambiguity function. A detailed example shows how the results of this concise paper can be used to optimize signal parameters and to predict the performance loss due to nonoptimal signal envelopes.