Fabio Postiglione

Virtualized networks are built on the principle of replacing bulky and rather static hardware-based functions with software-based, virtualized instances of those functions, enabling more agile and cost-effective communication infrastructures. However, this shift brings new challenges for ensuring reliability and availability due to increased dependencies among system components introduced by virtualization technologies. Reliability, i.e., the ability of a system to perform regularly under specified conditions, and availability, i.e., the probability of a system of being ready to use, are critical requirements that must be guaranteed to maintain seamless network operations. Accurate modeling of these aspects is crucial for designing robust, fault-tolerant virtualized systems that can withstand service disruptions, ensuring continuous user access. Accordingly, this survey focuses on reliability and availability attributes of virtualized networks from a modeling perspective. We first introduce the Network Function Virtualization (NFV) architecture and relevant definitions, followed by a review of the European Telecommunications Standards Institute (ETSI) standardization efforts. We then explore key modeling formalisms and illustrate their use in characterizing failure and repair behaviors. A survey of related literature and supporting software tools is provided, along with a discussion on lessons learned and open research challenges to guide future work in designing fault-tolerant NFV systems.

In this article, an informative Bayesian approach is proposed for the bounded transformed gamma process, a novel stochastic process recently proposed in the literature to describe bounded above, monotonic increasing, degradation phenomena. The proposed approach is used to analyze a set of real wear data of the cylinder liners of a Diesel engine. Several scenarios, which differ in terms of the quality of the available prior knowledge, are considered and suitable prior distributions are suggested for each of them. In addition, detailed instructions are provided to help potential users incorporate into the suggested prior distributions all and solely the pieces of prior information that are available and sound. In particular, weak prior distributions are also suggested for situations in which available information is poor and/or there is no prior information to exploit. The proposed approach is used to estimate the process parameters and some functions thereof, such as the mean degradation level, the residual reliability of a unit, and to predict the future degradation growth and the useful lifetime. Point estimation and prediction under the (asymmetric) general entropy loss function are also performed to properly deal with situations where overestimation is costlier than underestimation, or vice versa. Estimates and predictions are computed by using proper Markov Chain Monte Carlo algorithms. Results obtained by analyzing wear data of the liners are compared both with those provided by classical methods and with those obtained by using Bayesian approaches based on vague priors. Finally, a sensitivity analysis is developed to study the impact of different prior distributions on the estimates of the parameters.

Hydrogen-based technologies are growing, thanks to recent advancements in systems such as fuel cells and electrolyzers. The present work aims to develop a methodology for the definition of a fused health indicator to monitor the operating and health conditions of a solid oxide fuel cell system. A suitable degradation model was built to yield four trendable output indicators, which were subsequently merged to create the fused health indicator. Subsequently, the assessment of off-design conditions and two realistic scenarios (leakage and constant excess of air working regime) was carried out. The health indicator has proved suitable for fault detection, prognostic applications, control strategy improvement, and health management. In particular, the methodology has underlined the necessity of making the control strategy adaptive with respect to degradation. Through this approach, it is observed that reducing the solid oxide fuel cell temperature difference by 10 °C can result in a 1.2% increase in lifetime. In contrast, the leakage simulation reveals a decrease of about 10.5% in the health state after 100 h, resulting in about a 21% lower end-of-life.

Network Function Virtualization (NFV) converts legacy telecommunication systems into modular software appliances, known as service chains, running on the cloud. To address potential software aging-related issues, rejuvenation is often employed to clean up their state and maximize performance and availability. In this work, we propose a framework to model the performability of service chains with rejuvenation. Performance modeling uses queueing theory, specifically adopting an M/G/m model with the Allen-Cunneen approximation, to capture real-world aspects related to service times. Availability modeling is addressed through the Multidimensional Universal Generating Function (MUGF), a recent technique that achieves computational efficiency when dealing with systems with many sub-elements, particularly useful for multi-provider service chains. Additionally, we deploy an experimental testbed based on the Open5GS service chain, to estimate key performance and availability parameters. Supported by experimental results, we evaluate the impact of rejuvenation on the performability of the Open5GS service chain. The numerical analysis shows that i) the configuration of replicas across nodes is important to meet availability goals; ii) rejuvenation can bring one additional “nine” of availability, depending on the time to recovery; and iii) MUGF can significantly reduce computational complexity through straightforward algebraic manipulations.

This work proposes a stochastic characterization of resilient 5G architectures, where attributes such as performance and availability play a crucial role. As regards performance, we focus on the delay associated with the Packet Data Unit session establishment, a 5G procedure recognized as critical for its impact on the Quality of Service and Experience of end-users. To formally characterize this aspect, we employ the non-product-form queueing networks framework where: i) main nodes of a 5G architecture have been realistically modeled as G/G/m queues which do not admit analytical solutions; ii) the decomposition method useful to catch subtle quantities involved in the chain of 5G interconnected nodes has been conveniently customized. The results of performance characterization constitute the input of the availability modeling, where we design a hierarchical scheme to characterize the probabilistic failure/repair behavior of 5G nodes combining two formalisms: i) the Reliability Block Diagrams, useful to capture the high-level interconnections between nodes; ii) the Stochastic Reward Networks to model the internal structure of each node. The final result is an optimal resilient 5G setting that fulfills both a performance constraint (e.g., a temporal threshold) and an availability constraint (e.g., the so-called five nines) at the minimum cost, namely, with the smallest number of redundant elements. The theoretical part is complemented by an empirical assessment carried out through Open5GS, a 5G testbed that we have deployed to realistically estimate main performance and availability metrics.

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.

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.