We investigate the minimization of the age of information (AoI) of an AI-powered application that requires timely processing of data generated by a multitude of users. We consider that sequences of inference tasks generated at individual terminals can either be processed locally with a tiny machine learning (ML) model or be offloaded to a more powerful ML model residing on an edge computing facility shared by all users. Since the local ML model is less powerful, its inferences may have low confidence. When this happens, the user is forced to repeat the inference with the more powerful edge ML model. The choice between local processing or offloading follows a randomized-alpha policy, where the local ML model, while less powerful, offers the advantage to alleviate congestion of the edge server. The AoI model follows the frameworks presented in the literature for multiple sources sharing the same queue. Local processing instead works as a single-server dedicated queue, but we account for the imperfections of the tiny ML model by including a failure probability in the local server. Tasks that are processed locally but eventually fail to achieve a minimum confidence level are offloaded to the edge server, resulting in a longer overall processing time. We derive a queueing model of the entire system based on some bounds from the literature. Our results show the trade-offs between processing latency, inference accuracy, and system congestion, highlighting the importance of optimizing task allocation strategies.

We consider a portion of a RAN where end-users access services that imply the issue of a request through their associated base station (BS), followed by a computation on one of the available in-network computing facilities, and finally by the return of the result of the computation to the end-user who issued the request. The result must be returned within a specified latency deadline in order to be useful. Since not all BSs are equipped with a computing facility, some end-users may be disadvantaged, because they are associated with a BS from which the delay for a service request to reach a computing facility and for the results of the computation to come back is longer. Aiming at uniform end-user satisfaction, network operators should strive to on the one hand reduce differences in achieved end-user performance, while on the other obtain an efficient use of network resources. With simple analytical models we investigate the effectiveness of light network management algorithms, consisting in carefully choosing the routing probabilities of service requests toward one of the available computing facilities. We argue that at least some of such light network management algorithms should be compatible with the very stringent European Network Neutrality rules, and we show that they allow a good trade-off between overall resource utilization and equal performance experienced by end-users.

We consider a radio access network slice serving mobile users whose requests imply computing requirements. Service is virtualized over either a powerful but distant cloud infrastructure or an edge computing host. The latter provides less computing and storage capacity with respect to the cloud, but can be reached with much lower delay. A tradeoff thus naturally arises between computing capacity and data transfer latency. We investigate the performance of this service model, discussing how service requests should be routed to edge or cloud servers. We look at the performance of various classes of online algorithms based on different levels of information about the system state. Our investigation is based on analytical models, simulations in OMNeT++, and a prototype implementation over operational cellular networks. First of all, we observe that distributing the load of service requests over edge and cloud is in general beneficial for performance, and simple to implement with a stateless online server selection policy that can be easily configured with near-optimal performance. Second, we shed light on the limited improvements that stateful polices can offer, notwithstanding they base their decisions on the knowledge of server congestion levels or round-trip latency conditions. Third, we unveil that stateful policies are dangerously prone to errors, which may make stateless policies preferable.

The immune system (IS) represents a complex network of cells and molecules devoted to the protection of individuals from external pathogens, and in terms of complexity, it is only second to the central nervous system. As our knowledge of the IS mechanisms has become more exhaustive, interest has grown in applying modeling and simulation techniques in this context. In particular, among these techniques, the Agent Based Models (ABMs) have been increasingly applied for the IS simulation. One of the major drawbacks of ABMs is represented by the lack of well-defined semantics, which may lead to inconsistent results in comparison to other stochastic approaches. In this paper, we make use of the well-defined semantics and the simulation algorithm for ABMs that we proposed in [1] to implement a few models of the Cancer-Immune System. Comparing ABMs and Gillespie’s Stochastic Simulation Algorithm results we show that our methodology brings coherence among the results of ABMs and SSA.

We consider a slice of a radio access network where human and machine users access services with either high throughput or low latency requirements. The slice offers both eMBB and URLLC service categories to serve HTC (Human-Type Communication) and MTC (Machine-Type Communication) traffic. We propose to use eMBB for both HTC and MTC, transferring machine traffic to URLLC only when eMBB is not able to meet the low latency requirements of MTC. We show that by so doing the slice is capable of providing very good performance to about one hundred MTC users under high HTC traffic conditions. Instead, running time-critical MTC over only eMBB is not doable at all, whereas using URLLC suffices for at most a few tens of devices. Therefore, our approach improves the number of users served by the slice by one order of magnitude, without requiring extra resources or compromising performance. To study system performance we develop a novel analytical model of uplink packet transmissions, which covers both legacy eMBB-or URLLC-based MTC, as well as our compound approach. Our model allows to tune slice parameters so as to achieve the desired balance between HTC and MTC service guarantees. We validate the model against detailed simulations using as an example an autonomous driving scenario.

In this paper we develop a simple, yet accurate, performance model to understand if and how evolutions of standard cellular network protocols can be exploited to allow large numbers of machine type devices to access transmission resources with short latency, and we apply our model to the performance analysis of smart factory radio access networks. The model results shed light on the problems resulting from the application of evolved standard access procedures and help understand how many devices can be served per base station with specified latency targets. In addition, considering the simultaneous presence of different traffic classes, we investigate the effectiveness of prioritised access, exploiting access class barring techniques. Our model shows that, even with the sub-millisecond time slots foreseen in LTE Advanced Pro and 5G, a base station can accommodate at most few thousand devices to guarantee access latency below 100 ms with high transmission success probability. Lower access latency, of the order of 10 ms, can be achieved only with base stations serving an unrealistically small numbers of devices. This calls for a rethinking of wireless access strategies to avoid excessive latency in ultra-dense cell deployments within smart factory's infrastructures.

In this article, we develop a modeling framework to describe the uplink behavior of radio access in a sliced cell, including most features of the standard 3GPP multiple access procedures. Our model allows evaluating throughput and latency of each slice, as a function of cell parameters, when resources are in part dedicated to individual slices and in part shared. The availability of an accurate model is extremely important for the automated run time management of the cell and for the correct setting of its parameters. Indeed, our model considers most details of the behavior of sliced 5G cells, including Access Class Barring (ACB) and Random Access CHannel (RACH) procedures, preamble decoding, Random Access Response (RAR), and Radio Resource Control (RRC) procedures. To cope with a number of slices devoted to serve various co-deployed tenants, we derive a multi-class queueing model of the network processor. We then present (i) an accurate and computationally efficient technique to derive the performance measures of interest using continuous-time Markov chains, which scales up to a few slices only, and (ii) tight performance bounds, which are useful to tackle the case of more than a fistful of slices. We prove the accuracy of the model by comparison against a detailed simulator. Eventually, with our performance evaluation study, we show that our model is very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices for services with different characteristics and performance requirements, such as machine type communications and human type communications.

BACKGROUND: Emerging and re-emerging infectious diseases such as Zika, SARS, ncovid19 and Pertussis, pose a compelling challenge for epidemiologists due to their significant impact on global public health. In this context, computational models and computer simulations are one of the available research tools that epidemiologists can exploit to better understand the spreading characteristics of these diseases and to decide on vaccination policies, human interaction controls, and other social measures to counter, mitigate or simply delay the spread of the infectious diseases. Nevertheless, the construction of mathematical models for these diseases and their solutions remain a challenging tasks due to the fact that little effort has been devoted to the definition of a general framework easily accessible even by researchers without advanced modelling and mathematical skills. RESULTS: In this paper we describe a new general modeling framework to study epidemiological systems, whose novelties and strengths are: (1) the use of a graphical formalism to simplify the model creation phase; (2) the implementation of an R package providing a friendly interface to access the analysis techniques implemented in the framework; (3) a high level of portability and reproducibility granted by the containerization of all analysis techniques implemented in the framework; (4) a well-defined schema and related infrastructure to allow users to easily integrate their own analysis workflow in the framework. Then, the effectiveness of this framework is showed through a case of study in which we investigate the pertussis epidemiology in Italy. CONCLUSIONS: We propose a new general modeling framework for the analysis of epidemiological systems, which exploits Petri Net graphical formalism, R environment, and Docker containerization to derive a tool easily accessible by any researcher even without advanced mathematical and computational skills. Moreover, the framework was implemented following the guidelines defined by Reproducible Bioinformatics Project so it guarantees reproducible analysis and makes simple the developed of new user-defined workflows.

Summary We consider fog computing scenarios where data generated by a set of IoT applications need to be processed locally by a set of fog nodes, belonging to distinct Fog Infrastructure Providers (FIPs) sharing the same co‐location facility, with the aim of increasing their profits. This is a challenging goal as it requires reducing costs and meeting QoS targets despite time‐varying workloads. We argue that these FIPs may find it profitable to cooperate by mutually sharing their workload and resources, and we show (by using a game‐theoretical framework) that this is indeed the case when stable coalitions can be formed. Based on these results, in this paper, we present (1) a mathematical model for maximizing the profit obtained for allocating IoT applications to a group of FIPs, and (2) a coalition formation algorithm that allows each FIP to decide with whom to cooperate so as to increment its profits. The efficacy of the devised algorithm is assessed by means of an experimental evaluation taking into account different workload intensities. The results from these experiments show the capability of the proposed algorithm to form coalitions of FIPs that are profitable and stable in all the scenarios we take into consideration.

In this paper we investigate the allocation of resources to slices on the radio interface of one cell. In particular, we develop a detailed stochastic model of the behaviour of the sliced cell radio access, including most features of the standard access procedures. Our model allows the computation of the throughput achieved by each slice, as well as the distribution of delays for each slice. The availability of a model capable of accurately predicting the performance achieved by services using different slices as a function of the cell parameters is extremely important for the automated run time management of the cell and for the correct setting of its parameters.Specifically, while our model can cope with a number of slices, we focus on the case of one cell comprising one slice for human type communications and one slice for machine type communications, and we discuss relevant emerging behaviours in the slices performance, as functions of the cell parameters.We validate the analytical predictions by comparison against the estimates of a detailed simulator, proving the accuracy of the model. Our model turns out to be very effective in providing insight and guidelines for allocation and management of resources in cells hosting slices carrying traffic derived from services with different characteristics and performance requirements.

In this paper we study the queuing system that describes the operations of data services in cellular networks, e.g., UMTS, LTE/LTE-A, and most likely the forthcoming 5G standard. The main characteristic of all these systems is that after service access, resources remain allocated to the end user for some time before release, so that if the same user requests access to service again, before a system timeout, the same resources are still available. For the resulting queuing model, we express the blocking probability in closed form, and we also provide recursive expressions in the number of connections that can be handled by the base station. Closed form expressions are also derived for other useful performance metrics, i.e., throughput and network service time. Analytical results are validated against results of a detailed simulation model, and compared to traditional queueing models results, such as the Erlang B formula iteratively applied to the resources that are not blocked by potentially returning users. Our analysis complements the performance evaluation of the other key mechanism used to access data services in cellular networks, namely the random access, which precedes the resource allocation and utilization phase studied in this paper.

In this paper we develop a simple, yet accurate, performance model to understand if and how evolutions of traditional cellular network protocols can be exploited to allow large numbers of devices to gain control of transmission resources in smart factory radio access networks. The model results shed light on the applicability of evolved access procedures and help understand how many devices can be served per base station. In addition, considering the simultaneous presence of different traffic classes, we investigate the effectiveness of prioritised access, exploiting access class barring techniques. Our model shows that, even with the sub-millisecond time slots foreseen in LTE Advanced Pro and 5G, a base station can accommodate at most few thousand devices to guarantee access latencies below 100 ms with high transmission success probabilities. This calls for a rethinking of wireless access strategies to avoid ultra-dense cell deployments within smart factory infrastructures.

An experience common to smartphone users is the difficulty in accessing services in crowded scenarios, such as a rock concert or a football match. In these cases, to (partially) mitigate frustration, users generically claim that network congestion is occurring, and try again and again to access the network with their smartphones: the result is that user frustration and network congestion reinforce each other! This paper investigates the root causes of poor performance of cellular networks in crowded environments and shows that the commonly adopted random access procedure can prevent full utilization of wireless resources. We develop a simple yet accurate analytical model to analyze why attempting random access to wireless resources can become a problem even when access congestion avoidance is enforced, e.g., with the Access Class Barring technique. The model we propose suggests that cluster-based network access, leveraging device-to-device communications, significantly alleviates access problems. Moreover, it sheds light on scalability laws that govern network utilization and quality of experience, in terms of cell capacity, number of access channels, and cluster size.

The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time fine-grained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts etc., using systematic methologies from complex networks and graph spectra analysis. However, a misuse of these methods may result in privacy-intrusive and discriminatory actions that may threaten citizens' autonomy and put their life under surveillance. This paper studies highly sparse temporal networks that model social interactions such as the physical proximity of participants in conferences. When citizens can self-determine the anonymized proximity data they wish to share via privacy-preserving platforms, temporal networks may turn out to be highly sparse and have low quality. This paper shows that even in this challenging scenario of privacy-by-design, significant information can be mined from temporal networks such as the correlation of events happening during a conference or stable groups interacting over time. The findings of this paper contribute to the introduction of privacy-preserving data analytics in temporal networks and their applications.

In this paper device-to-device (D2D) communication is proposed as a tool for enhancing the services provided by mobile cellular networks. The technique we discuss relies on the cooperation among the mobile users that participate in the service delivery process, under the control of the base station. In particular, the paper proposes an incentive mechanism encouraging terminals to organize into an optimal number of clusters from the point of view of both bandwidth capacity and power saving. The aspects inducing users to collaborate are analyzed and modelled, e.g., the amount of mobile battery power drained during collaboration, to better design incentives to switch to D2D. The proposed technique allows the base station to estimate the incentives to grant to the mobile terminals to optimize its cost. Through both analysis and simulations, we show that our scheme achieves a significant gain in terms of costs while increasing the bandwidth capacity of the whole cell.