The on-demand activation of frequency bands in radio access networks can lead to a significant reduction of energy consumption, but risks to adversely impact performance. This approach to frequency band management can be applied either to a group of co-located base stations whose operators adopt a network sharing approach or to a single base station that uses multiple frequency bands. We develop a stochastic model based on the Matrix Analytic Method for the quantification of system performance and energy consumption in the case of coexisting streaming and elastic services. By computing numerical results in a specific setting, we show that the on-demand (de)activation, possibly combined with the adaptation of the data rate of streaming services, succeeds in greatly reducing energy consumption with respect to the case in which frequency bands are always active, with limited impact on the performance experienced by users. We also show that the introduction of a hysteresis in the frequency band activation/deactivation process allows the optimization of the energy/performance trade-off. Finally, we show that performance is not drastically altered by the burstiness of the elastic service request arrival process, and we prove that the separate analysis of streaming and elastic services provides quite optimistic results with respect to the joint analysis made possible by our model.

This paper addresses the optimization of the perception message periodicity in vehicular networks by minimizing a weighted sum of transmission and freshness costs. The expressions of the two contributions and the closed-form solution to the problem are obtained for the Dynamic Scheme of the New Radio Vehicle-to-Everything Side Link standard. Moreover, a model rooted in queueing theory is proposed to describe a connected automated vehicle as a source of messages about the detected objects. The model provides the statistical description of the number of objects in the vehicle viewing horizon, wherefrom the expression of its autocorrelation function is determined. Through the latter, the degree of similarity between the content of consecutive messages is understood. Lastly, an illustrative suburban scenario is investigated and realistically implemented in a simulator. The results show that increasing the detection range of the connected automated vehicle is beneficial, allowing higher values of the optimal update periodicity, larger message sizes, and lower correlation levels between consecutive messages. This choice leads to the best communication performance, guaranteeing a higher probability of successful packet delivery.

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.

Queuing networks with finite capacity are widely discussed in performance analysis literature. One approach to address the finite capacity of stations involves the implementation of a skip-over policy. Under this policy, when a customer arrives at a saturated station, service at that station is skipped, and the customer is rerouted based on the predefined network routing protocol. Skip-over networks have been extensively investigated, and they exhibit a product-form stationary distribution under the exponential assumptions of Jackson networks. However, a comprehensive understanding of the celebrated Arrival Theorem for this class of product-form models is still lacking and relies on certain conjectures. This paper makes three contributions: (i) it provides an in-depth comprehension of the Arrival Theorem for skip-over networks by offering a proof for the conjectures outlined in existing literature, (ii) it introduces a Mean Value Analysis (MVA) algorithm tailored for this type of queuing networks, and (iii) it explores the implications of these findings on the class of product-form queuing networks with fetching and repetitive service discipline.

In this paper, we present the first remote timing attack based on formal stochastic models. Our attack uses queuing models from the field of performance evaluation to estimate the service times of different classes of network requests. By using Bayesian statistics, we then identify opportunities for remote timing attacks by answering the following inverse question: what is the probability that a given network request belongs to a target class, given an estimate of its service time? Our experimental evaluation on popular web applications and websites shows that our investigation is not just a theoretical exercise, because our attack outperforms existing empirical approaches in terms of standard performance figures. We believe that the formal foundations put forward in this paper can be successfully applied to the creation of principled remote timing attacks which are more effective, because better equipped to deal with the complexity of the problem they are trying to solve.

We consider an access link carrying data generated by streaming and elastic services requested by fixed or mobile end users, and subjected to an admission control (AC) algorithm. For the performance analysis of such link we develop a new queuing model and we show that, with the considered AC, the queuing model admits a product form expression for the joint limiting probability distribution of the numbers of active services of the different types. In addition, we prove that, when mobility can be neglected, i.e., in the case of either fixed access or slow mobility, the queuing model is insensitive to the distribution of the amount of data to be transferred for the fulfilment of the different service requests. Numerical results show unexpected oscillating behaviors for several performance metrics, and provide interesting insight into the link performance.

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.

We consider an access link of a data network loaded with data flows generated by streaming and elastic services requested by fixed or mobile end users, and subjected to an admission control (AC) algorithm. For the performance analysis of such link we develop a new queuing model and we show that, with the considered AC, the queuing model admits a product form expression for the joint limiting probability distribution of the numbers of active services of the different types. Numerical results show unexpected oscillating behaviors for several performance metrics, and provide interesting insight into the link performance.

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 consider radio access network configurations comprising cells of different size (one macro cell and some small cells). Cells have overlapping coverage, and the corresponding base stations offer both elastic and inelastic services. The considered setting is modeled as a queuing network, and we examine the system performance for variable parameter values, showing that some of the emerging behaviors can be unexpected, and providing insight into the effective deployment of small cells within the coverage of one macro cell. In particular, we see that a large fraction of elastic traffic, together with an area of the small cell corresponding to a significant portion of the macro cell area are important aspects for the effective exploitation of the small cell capacity. These behaviors can significantly impact the deployment of small cells, which are expected to become increasingly popular because of the need to provide additional capacity through densification of the cell layout.

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.

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.

The role of vehicles has been rapidly expanding to become a different kind of utility, no longer just vehicles but nodes of the future Internet. The car producers and the research community are investing considerable time and resources in the design of new protocols and applications that meet customer demand, or that foster new forms of interaction between the moving customers and the rest of the world. Among the variety of new applications and business models, the spreading of advertisements is expected to play a crucial role. Indeed, advertising is already a significant source of revenue and it is currently used over many communication channels, such as the Internet and television. In this paper, we address the targeting of advertisements in vehicular networks, where advertisements are broadcasted by Access Points and then displayed to interested users. In particular, we describe the advertisement dissemination process by means of an optimization model aiming at maximizing the number of advertisements that are displayed to users within the advertisement target area and target time period. We then solve the optimization problem on an urban area, using realistic vehicular traffic traces. Our results highlight the importance of predicting vehicles mobility and the impact of the user interest distribution on the revenue that can be obtained from the advertisement service.

Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise an algorithm, based on cooperative game theory, that can be readily implemented in a distributed fashion, and that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation. We show that, by using our algorithm and the proposed CPs' utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Peer-to-peer (P2P) resource location techniques in grid systems have been recently investigated to obtain scalability, reliability, efficiency, fault-tolerance, security, and robustness. Query resolution for locating resources and update information on their own resource status in these systems can be abstracted as the problem of allowing one peer to obtain a local view of global information defined on all peers of a P2P unstructured network. In this paper, the system is represented as a set of nodes connected to form a P2P network where each node holds a piece of information that is required to be communicated to all the participants. Moreover, we assume that the information can dynamically change and that each peer periodically requires to access the values of the data of all other peers. A novel approach based on a continuous flow of control packets exchanged among the nodes using the random walk principle and rateless coding is proposed. An innovative rateless decoding mechanism that is able to cope with asynchronous information updates is also proposed. The performance of the proposed system is evaluated both analytically and experimentally by simulation. The analytical results show that the proposed strategy guarantees quick diffusion of the information and scales well to large networks. Simulations show that the technique is effective also in presence of network and information dynamics.

Rateless codes are designed to decode all the input symbols when a certain number of coded symbols have been received. However, it is possible to recover a subset of the input symbols from the actually received coded symbols: this process is called partial decoding and the number of recovered input symbols is termed the intermediate performance of rateless codes. In this paper we study the problem of the optimality of the partial decoding process: we say that a partial decoding algorithm is optimal if, given a rateless code, it is able to maximize the intermediate performance of the code, i.e. it is able to retreive the maximum number of input symbols when a certain number n of coded symbols have been received, for every n. We propose OPD, an optimal partial decoding algorithm for any rateless code, proving its optimality. The proposed algorithm is finally used to analyze the intermediate performance of LT codes.

In this paper, we propose a generalization of the basic flooding search strategy for decentralized unstructured peer-to-peer (P2P) networks. In our algorithm a peer forwards a query to one of its neighbors using a probability that is a function of the number of connections in the overlay network of both. Moreover, this probability may also depend on the distance from the query originator. To analyze the performance of the proposed search strategy in heterogeneous decentralized unstructured P2P networks, we develop a generalized random graph (GRG)-based model that takes into account the high variability in the number of application level connections that each peer establishes, and the nonuniform distribution of resources among peers. Furthermore, the model includes an analysis of peer availability, i.e., the capability of relaying queries of other peers, as a function of the query generation rate of each peer. Validation of the proposed model is carried out comparing the model predictions with simulations conducted on real overlay topologies obtained from crawling the popular file sharing application Gnutella.

In this paper we present ToroVerde, a novel push-based peer-to-peer (P2P) content distribution application exploiting the digital fountain concept through the use of rateless codes. We provide the protocol specification, then describe the simulator and the complete prototype we have developed for Planetlab deployment and testing. To this end, we consider flash crowd and steady arrival patterns as well as highly churning systems to perform a preliminary analysis of the potential advantages of introducing rateless codes. We present results from PlanetLab experiments compared against performance of BitTorrent, that is widely considered as the reference system for content distribution. We also present a few simulation results showing the behavior of ToroVerde as the number of peers in the systems increases. Our results suggest that ToroVerde has the potential of reducing the average download time for small-to-medium sized files in overlays composed of a few hundred peers with a small increase of the communication overhead.

In this paper we face the following problem: how to provide each peer local access to the full information (not just a summary) that is distributed over all \emph{edges} of an overlay network? How can this be done if local access is performed at a given rate? We focus on \emph{large and sparse} information and we propose to exploit the compressive sensing (CS) theory to efficiently collect and pro-actively disseminate this information across a large overlay network. We devise an approach based on random walks (RW) to spread CS random combinations to participants in a random peer-to-peer (P2P) overlay network. CS allows the peer to compress the RW payload in a distributed fashion: given a constraint on the RW size, e.g., the maximum UDP packet payload size, this amounts to being able to distribute larger information and to guarantee that a large fraction of the global information is obtained by each peer. We analyze the performance of the proposed method by means of a simple (yet accurate) analytical model describing the structure of the so called CS sensing matrix in presence of peer dynamics and communication link failures. We validate our model predictions against a simulator of the system at the peer and network level on different models of random overlay networks. The model we developed can be exploited to select the parameters of the RW and the criteria to build the sensing matrix in order to achieve successful information recovery. Finally, a prototype has been developed and deployed over the PlanetLab network to prove the feasibility of the proposed approach in a realistic environment. Our analysis reveals that the method we propose is feasible, accurate and robust to peer and information dynamics. We also argue that centralized and other distributed approaches, i.e., flooding and gossiping, are unfit in the context we consider.

This paper presents TURINstream, a novel P2P video streaming architecture designed to jointly achieve low delay, robustness to peer churning, limited protocol overhead, and quality-of-service differentiation based on peers cooperation. Separate control and video overlays are maintained by peers organized in clusters that represent sets of collaborating peers. Clusters are created by means of a distributed algorithm and permit the exploitation of the participant nodes upload capacity. The video is conveyed with a push mechanism by exploiting the advantages of multiple description coding. TURINstream design has been optimized through an event driven overlay simulator able to scale up to tens of thousands of peers. A complete prototype of TURINstream has been developed, deployed, and tested on PlanetLab. We tested our prototype under varying degree of peer churn, flash crowd arrivals, sudden massive departures, and limited upload bandwidth resources. TURINstream fulfills our initial design goals, showing low average connection, startup, and playback delays, high continuity index, low control overhead, and effective quality-of-service differentiation in all tested scenarios.

The goal of this paper is the development of network coding solutions able to improve the performance of video streaming applications over peer-to-peer overlays. Recent advances in P2P protocols have shown that rateless codes can be profitably applied to P2P video streaming with several advantages in terms of protocol efficiency and simplification, e.g. push based video delivery, no need of packet reconciliation at the decoder. In this paper existing and novel network coding techniques based on rateless codes are presented and compared, showing that rateless codes, besides simplifying the protocol design, can significantly reduce the startup and playback delays. The proposed protocol is evaluated on real topologies, obtained by crawling the widespread PPLive video streaming application. The reported experimental results show that the proposed protocol significantly reduces the startup and playback delay and allows one to increase the bitrate devoted to the video stream.

Are P2P systems and applications here to stay? Or are they a bright meteor whose destiny is to disappear soon? In this paper we try to give a positive answer to the first question, highlighting reasons why the P2P paradigm should become an integral part of computing and communication services and not only oddities for Cyber-geeks.

The P2P-IPTV is an emerging class of Internet applications that is becoming very popular. The growing popularity of these rather bandwidth demanding multimedia streaming applications has the potential to flood the Internet with a huge amount of traffic. In this paper we present an investigation of the popular P2P-IPTV application PPLive exploiting a measurement strategy that combines both active and passive measures. To this end, we use a crawler that allows the study of the topological characteristics of the overlay of one of the PPLive channels; concurrently, we perform passive measures on a PPlive client we run to join the crawled channel. We successively cross correlate information we obtained from the two measurements to assess the accuracy of the data captured by the crawler. Our results reveal the potentials and the limits of PPLive active measures strategies.

Abstract This paper develops a directed generalized random graphs based analytical modeling framework to compare several variations of the basic flooding search strategy in unstructured decentralized peer‐to‐peer networks. To validate the model predictions, we designed and implemented a distributed crawler architecture that is able to efficiently capture snapshots of the top‐level Gnutella overlay topology. The snapshots are used to obtain simulation results that are used to assess the accuracy of our model. The model predictions are then used to compute system‐oriented performance indexes (the average and the coefficient of variation of the number of query messages) as well as user‐oriented measures (the probability of finding at least one replica of a resource, the average search time). The trade‐off between the optimization of system‐oriented measures and the improvement of user‐oriented quality indexes is investigated for several variations of the basic flooding strategy suggesting that adding control parameters to the basic flooding mechanism might prove beneficial in this class of systems. Copyright © 2007 John Wiley & Sons, Ltd.

We focus on a vehicular network supporting safety applications, and we present an application and a channel access mechanism for efficient multihop broadcasting. We study the performance of the proposed solution by developing an analytical framework, which provides several metrics relevant to message dissemination. Analytical results are compared with the performance obtained through ns

This paper exploits a previously developed analytical modeling framework to compare several variations of the basic flooding search strategy in unstructured decentralized peer-to-peer (P2P) networks. The model predictions are used to compute system-oriented performance indexes (the average and the coefficient of variation of the number of query messages) as well as user-oriented measures (the probability of finding at least one replica of a resource, the average search time). The trade-off between the optimization of system-oriented measures and the improvement of user-oriented quality indexes is investigated for several variations of the basic flooding strategy suggesting that adding control parameters to the basic flooding mechanism might prove beneficial in this class of systems.

This paper presents an application of basic concepts of statistical physics to devise an approximate model describing the dynamics of large peer-to-peer networks, based on fluid-diffusive equations. The model we propose is quite general and highly modular, and allows to represent several effects related to resources distribution among peers, user behavior, resource localization algorithms and dynamic structure of the overlay topology. Since the complexity of the model is largely independent of the system size, it provides a viable alternative to Montecarlo approaches for the analysis of very large P2P systems.

The behavior of large-scale wireless sensor networks has been shown to be surprisingly complex and difficult to analyze, both by empirical experiment and simulation. In this paper we develop a new analytical model of the behavior of wireless sensor networks, based on a fluid approach, i.e., we represent the sensor network by a continuous fluid entity distributed on the network area. The model accounts for node energy consumption, channel contention, as well as traffic routing; thus, it is well suited for describing the properties of sensor networks and understanding their complex behavior.

In this paper a new algorithm for the transient solution of a sub-class of deterministic stochastic Petri nets (DSPN) is proposed. The technique can be applied to DSPNs comprising only deterministic and immediate transitions and such that in each tangible marking only one deterministic transition is enabled. The algorithm does not require any additional restriction on the deterministic transition delays that can have any positive real value. Most of the optimized algorithms presented in the literature are based on an efficient solution of the equations governing the stochastic process associated with the DSPN; the new algorithm we propose is based on an efficient combinatorial analysis of the paths within the state space underlying the DSPN, instead.

We present WALTy (Web application load-based testing tool), a set of tools that allows the performance analysis of Web applications by means of a scalable what-if analysis on the test bed. The proposed approach is based on a workload characterization generated from information extracted from log files. The workload is generated by using of customer behavior model graphs (CBMG), that are derived by extracting information from the Web application log files. In this manner the synthetic workload used to evaluate the Web application under test is representative of the real traffic that the Web application has to serve. One of the most common critics to this approach is that synthetic workload produced by Web stressing tools is far from being realistic. The use of the CBMGs might be useful to overcome this critic.

In this paper, we show the structural characteristics that a particular class of generalized stochastic Petri nets must exhibit in order for their stationary probabilities to have a product-form. Sufficient conditions for identifying such a class are derived and proven with the development of a series of transformations that can also be used to construct, for any GSPN of the class, an equivalent SPN. These resulting SPNs represent the structures that can be analyzed with standard methods for product-form SPNs to establish whether the original GSPNs have product-form solutions and to compute their performance indices with effective approaches based on computationally efficient algorithms that avoid the generation of their underlying state spaces.

Describes a method for the simulation of fluid stochastic Petri nets (FSPNs). The FSPNs are a promising formalism for modeling hybrid dynamic systems, i.e. systems having both discrete and continuous components that evolve over time. Unfortunately, an analytical evaluation of performance measures for such nets requires the solution of a complex system of integro-differential equations whose numerical analysis often becomes a formidable task. One possible way for computing such performance measures is the use of simulative approaches. Because of the mixed (discrete and continuous) state space, the simulation of FSPNs models poses some interesting challenges, which are addressed in this paper. The paper provides a simulative approach for deriving performance measures for a class of FSPNs. The techniques described in the paper are included in a simulation tool for this class of FSPNs.

An iterative technique for the computation of approximate performance indices of a class of stochastic Petri net models is presented. The proposed technique is derived from the mean value analysis algorithm for product-form solution stochastic Petri nets. In this paper, we apply the approximation technique to stochastic marked graphs. In principle, the proposed technique can be used for other stochastic Petri net subclasses. In this paper, some of these possible applications are presented. Several examples are presented in order to validate the approximate results.

We examine product form equilibrium distributions for stochastic process algebra models, finding conditions that guarantee the existence of a solution for the traffic equations. These equations are a set of linear equations, which are the basis of exact analysis of product form models like queueing networks, stochastic Petri nets, as well as stochastic process algebras. This paper illustrates how the equilibrium distribution can be expressed as a product for a certain class of stochastic process algebra models that satisfy certain conditions. Although the product from criterion derived in this paper is developed in the context of Performance Evaluation Process Algebra (PEPA), the results can be easily generalised to any of the other stochastic process algebra.

We consider a particular class of Stochastic Petri Nets whose stationary probabilities at arbitrary instants exhibit a product form. We study these nets at specific instants in the steady state that occur directly after the firing of a transition. We focus our attention on the instant after tokens are removed from the places specified by a transition's input bag and just before tokens are entered into the places specified by the same transition's output bag. We show that the stationary probabilities at “arrival instants” are related to corresponding stationary probabilities at arbitrary instants in net(s) with lower load. We then show how one of the “arrival” theorems can be applied to the derivation of a formula for the mean sojourn time of a token in a place at steady state. This is the basis for the development of a Mean Value Analysis algorithm for the computation of performance indices for Product-Form Stochastic Petri Nets.