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.

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.

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.

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.

Nowadays, most telecommunication services adhere to the Service Function Chain (SFC) paradigm, where network functions are implemented via software. In particular, container virtualization is becoming a popular approach to deploy network functions and to enable resource slicing among several tenants. The resulting infrastructure is a complex system composed by a huge amount of containers implementing different SFC functionalities, along with different tenants sharing the same chain. The complexity of such a scenario lead us to evaluate two critical metrics: the steady-state availability (the probability that a system is functioning in long runs) and the latency (the time between a service request and the pertinent response). Consequently, we propose a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs). We adopt a multi-state system to model single CNFs and the queueing formalism to characterize the service latency. To efficiently compute the availability, we develop a modified version of the Multidimensional Universal Generating Function (MUGF) technique. Finally, we solve an optimization problem to minimize the SFC cost under an availability constraint. As a relevant example of SFC, we consider a containerized version of IP Multimedia Subsystem, whose parameters have been estimated through fault injection techniques and load tests.

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.

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 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.

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.

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.