This work addresses the challenge of forecasting temporal metrics that characterize cellular traffic behavior. The ultimate goal is to provide network operators with a valuable tool for modeling mobile network traffic and optimizing connected resources. The idea is to estimate beforehand the temporal evolution of some Quality-of-Experience (QoE) and Quality-of-Service (QoS) metrics, which is helpful for accurately tuning the allocation of network resources. Remarkably, these metrics (expressed as time series) are typically correlated, and changes in one time series can affect others in a variety of ways and to different extents. For example, high network delay (a QoS-related metric) is associated with degradation in voice quality over time (a QoE-related metric). Accordingly, we address the problem of cellular traffic forecasting with correlated time series, proposing three innovative hybrid learning strategies designed by combining the advantages of two approaches: (i) a statistical approach, implemented through the Vector Autoregressive (VAR) model, which encodes each metric as a combination of past values of the same metric along with a combination of values of other related metrics, resulting in a multivariate structure; and (ii) an approach based on deep learning techniques (specifically, CNN, LSTM, and GRU) which operate on such a multivariate structure to perform the forecasting. The resulting performance demonstrates the benefits of the proposed hybrid schemes (VAR-CNN, VAR-LSTM, VAR-GRU) over their pure counterparts, with a significant reduction in forecasting errors. The network metrics were gathered in a real urban cellular environment, where the presence of exogenous factors (e.g., interferences, weather conditions, etc.) makes the forecasting assessment particularly challenging.

Predicting the behavior of real-time traffic (e.g., VoIP) in mobility scenarios could help the operators to better plan their network infrastructures and to optimize the allocation of resources. Accordingly, in this work the authors propose a forecasting analysis of crucial QoS/QoE descriptors (some of which neglected in the technical literature) of VoIP traffic in a real mobile environment. The problem is formulated in terms of a multivariate time series analysis. Such a formalization allows to discover and model the temporal relationships among various descriptors and to forecast their behaviors for future periods. Techniques such as Vector Autoregressive models and machine learning (deep-based and tree-based) approaches are employed and compared in terms of performance and time complexity, by reframing the multivariate time series problem into a supervised learning one. Moreover, a series of auxiliary analyses (stationarity, orthogonal impulse responses, etc.) are performed to discover the analytical structure of the time series and to provide deep insights about their relationships. The whole theoretical analysis has an experimental counterpart since a set of trials across a real-world LTE-Advanced environment has been performed to collect, post-process and analyze about 600,000 voice packets, organized per flow and differentiated per codec.

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.

Service provisioning mechanisms implemented across 5G infrastructures take broadly into use the network service chain concept. Typically, it is coupled with Network Function Virtualization (NFV) paradigm, and consists in defining a pre-determined path traversed by a set of softwarized network nodes to provide specific services. A well known chain-like framework is the IP Multimedia Subsystem (IMS), a key infrastructure of 5G networks, that we characterize both by a performance and an availability perspective. Precisely, supported by a designed from scratch testbed realized throughClearwaterplatform, we perform a stochastic assessment of a softwarized IMS (softIMS) architecture where two main stages stand out: i) a performance analysis, where, exploiting the queueing network decomposition method, we formalize an optimization problem of resource allocation by modeling each softIMS node as an$M/G/c$system; ii) an availability assessment, where, adopting the Stochastic Reward Net methodology, we are able to characterize the behavior of softIMS in terms of failure/repair events, and to derive a set of optimal configurations satisfying a given availability requirement (e.g., five nines) while minimizing deployment costs. Two routines dubbedOptCNTandOptSearchChainhave been devised to govern the performance and availability analyses, respectively.

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 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.

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has taken a prominent role in the network security management field, due to the substantial number of sophisticated attacks that often pass undetected through classic IDSs. These are typically aimed at recognizing attacks based on a specific signature, or at detecting anomalous events. However, deterministic, rule-based methods often fail to differentiate particular (rarer) network conditions (as in peak traffic during specific network situations) from actual cyber attacks. In this article we provide an experimental-based review of neural-based methods applied to intrusion detection issues. Specifically, we i) offer a complete view of the most prominent neural-based techniques relevant to intrusion detection, including deep-based approaches or weightless neural networks, which feature surprising outcomes; ii) evaluate novel datasets (updated w.r.t. the obsolete KDD99 set) through a designed-from-scratch Python-based routine; iii) perform experimental analyses including time complexity and performance (accuracy and F-measure), considering both single-class and multi-class problems, and identifying trade-offs between resource consumption and performance. Our evaluation quantifies the value of neural networks, particularly when state-of-the-art datasets are used to train the models. This leads to interesting guidelines for security managers and computer network practitioners who are looking at the incorporation of neural-based ML into IDS.

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.