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 studies the performance of Floating Gossip, a novel decentralized approach for Gossip Learning at the network edge. Floating Gossip utilizes Floating Content to facilitate location-based probabilistic evolution of Machine Learning models, without external infrastructure support. We investigate dynamic scenarios requiring continuous learning, leveraging a mean field approach to analyze Floating Gossip's performance boundaries. Our focus is on the quantity of data that users can integrate into their models, as a function of key system parameters. Unlike previous studies that separately optimize communication or computational aspects of Gossip Learning, our methodology considers their combined effect. We validate our analysis through comprehensive simulations, demonstrating the high accuracy of our analytical model. Our methodology reveals Floating Gossip's effectiveness in training and updating Machine Learning models collaboratively, leveraging opportunistic exchanges between mobile users, while flexibly adapting to different user characteristics and mobility patterns. This research highlights Floating Gossip's potential for continuous, cooperative model training in dynamic, infrastructure-less environments, offering insight into its performance patterns and its potential in practical applications.

The efficient execution of data center jobs that require simultaneous use of different resource types is of critical importance. When processing capacity is the crucial resource for jobs execution, the locution multiserver jobs is used, where the term server indicates processors or CPU cores providing processing capacity. Each multiserver job carries a requirement expressed in number of servers it requires to run, and service duration. Achieving efficient execution of multiserver jobs relies heavily on effective scheduling of jobs on the existing servers. Several schedulers have been proposed, aimed at improving resource utilization, at the cost of increased complexity. Due to the limited availability of theoretical results on scheduler behavior in the case of multiserver jobs, data center schedulers are often designed based only on managers' experience. In this paper, aiming to expand the understanding of the multiserver job schedulers' performance, we study Small Shuffle (SMASH) schedulers, a class of nonpreemptive, service time oblivious, window-based multiserver job scheduling algorithms that strike a balance between simplicity and efficient resource utiliza tion, while allowing performance evaluation in simpler settings. SMASHimplies only a marginal increase in complexity compared to FIFO, yet it delivers substantial performance improvements for multiserver jobs. Depending on the system parameters, SMASH can nearly double the system's stability region with respect to FIFO, leading to significantly lower response times across a broad region of loads. Moreover, the magnitude of this improvement scales with the chosen window size, allowing performance to be tuned to the system's operating conditions. We first study the capacity of SMASH with analytical tools in simple settings, then we investigate the performance of SMASH and other schedulers with simulations under more realistic workloads, designed with parameters derived from measurements of real data centers. Results show that SMASH offers a very good compromise between performance and complexity.

Network Sharing (NS) has gained increasing interest for Mobile Operators (MOs) because of the high investment costs of 5G combined with a period of low return of investment. The benefits that NS can offer include reduced capital and operational expenditures, because of fewer equipment, and lower energy consumption, possibly combined with higher network resiliency. While these aspects have been investigated in the literature, in those works more attention was paid to the overall benefits, disregarding asymmetries between the involved MOs. In this paper we address the issue of fairness in sharing network infrastructure among MOs and we introduce a Fair Cooperative Network Sharing (FCNS) framework that dynamically offloads traffic among co-located BSs owned by different MOs with two primary objectives: distributing active operational time more equitably between BSs in a pair, and significantly decreasing the failure rate of BS pairs. Simulation results based on empirical mobile traffic data demonstrate that the proposed FCNS framework effectively balances the BS active time across operators. In addition, FCNS achieves energy savings of up to 38% for each MO within a BS pair and reduces the failure rate by approximately 20%. These findings highlight the potential of cooperative network sharing as a feasible and sustainable solution for resilient 5G deployments.

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.

The Multiserver Job Queuing Model (MJQM) is a queuing system that plays a key role in the study of the dynamics of resource allocation in data centers. The MJQM comprises a waiting line with infinite capacity and a large number of servers. In this paper, we look at the limiting case in which the number of servers is infinite. Jobs are termed “multiserver” because each one is characterized by a resource demand in terms of number of simultaneously used servers and by a service duration. Job classes are defined by collecting all jobs that require the same number of servers. Job service times are independent and identically distributed random variables whose distributions depend on the class of the job. We consider the case of only two job classes: “small” jobs use a fixed number of servers, while “big” jobs use all servers in the system. The service discipline is First-In First-Out (FIFO). This means that if the job at the Head-of-Line (HOL) cannot enter service because the number of free servers is not sufficient to meet the job requirement, it blocks all subsequent jobs, even if there are sufficient free servers for them. Despite its importance, only few results exist for the MJQM, whose analysis is challenging, especially because the MJQM is not work-conserving. This implies that even the stability region of the MJQM is known only in special cases. In a previous work, we obtained a closed-form stability condition for MJQM with big and small jobs under the assumption of exponentially distributed service times for small jobs. In this paper, we compute the stability condition of MJQM with an infinite number of servers processing big and small jobs, considering different distributions of the service times of small jobs. Simulations are used to support the analytical results and to investigate the impact of service time distributions on the average job waiting time before saturation.

Datacenters comprise a variety of resources (processors, memory, input/output modules, etc.) that are shared among requests for the execution of computing jobs submitted by datacenter users. Jobs differ in their frequency of arrivals, demand for resources, and execution times. Resource sharing generates contention, especially in heavily loaded systems, that must therefore implement effective scheduling policies for incoming jobs. The First-In First-Out (FIFO) policy is often used for batch jobs, but may produce under-utilization of resources, in terms of wasted servers. This is due to the fact that a job that requires many resources can block jobs arriving later that could be served because they require fewer resources. The mathematical construct often used to study this problem is the Multiserver Job Queuing Model (MJQM), where servers represent resources which are requested and used by jobs in different quantities. Unfortunately, very few explicit results are known for the MJQM, especially at realistic system loads (i.e., before saturation), and hardly any considers the case of non-exponential service time distributions. In this paper, we propose the first exact analytical model of the non-saturated MJQM in case of two classes of customers with service times having 2-phase Coxian distribution. Our analysis is based on the matrix geometric method. Our results provide insight into datacenter dynamics, thus supporting the design of more complex schedulers, capable of improving performance and energy consumption within large datacenters.

Modern data centers feature an extensive array of cores that handle quite a diverse range of jobs. Recent traces, shared by leading cloud data center enterprises like Google and Alibaba, reveal that the constant increase in data center services and computational power is accompanied by a growing variability in service demand requirements. The number of cores needed for a job can vary widely, ranging from one to several thousands, and the number of seconds a core is held by a job can span more than five orders of magnitude. In this context of extreme variability, the policies governing the allocation of cores to jobs play a crucial role in the performance of data centers. It is widely acknowledged that the First-In First-Out (FIFO) policy tends to underutilize available computing capacity due to the varying magnitudes of core requests. However, the impact of the extreme variability in service demands on job waiting and response times, that has been deeply investigated in traditional queuing models, is not as well understood in the case of data centers, as we will show. To address this issue, we investigate the dynamics of a data center cluster through analytical models in simple cases, and discrete event simulations based on real data. Our findings emphasize the significant impact of service demand variability, both in terms of requested cores and service times, and allow us to provide insight for enhancing data center performance. In particular, we show how data center performance can be improved thanks to the control of the interplay between service and waiting times through the assignment of cores to jobs.

The transition towards the era of 5G and beyond is currently fostered by the extensive penetration of extremely demanding communication services, characterized by the need for exchanging increasingly larger traffic volumes with tight throughput and latency constraints. Nevertheless, the consequent massive densification of radio access networks (RANs) entails remarkable sustainability concerns, related to the staggering increase of energy demand and to the costly deployment of new infrastructure that, being dimensioned for future peak demands, may result underutilized for long periods of time. Furthermore, new potential vulnerabilities emerge that may impair the provisioning of resilient communication services. In this context, sharing network resources among different mobile operators (MOs) may play a key role to improve energy efficiency and to enhance resilience of future mobile networks. We hence investigate the potential benefits derived from the sharing of network infrastructure (primarily Base Stations with their portion of spectrum) among different MOs, comparing different areas, from a urban densely populated environment to a rural region. Based on real mobile traffic data, we design data-driven strategies to dynamically offload traffic among Base Stations owned by different MOs, allowing the switch off of unneeded resources. Our results shows that network sharing (NS) is effective in achieving huge energy saving and significant reduction of the electricity bill. Furthermore, proper configuration settings of the offloading strategies allow to trade off between sustainability goals and Quality of Service, hence enabling a feasible deployment of 5G scenarios and a sustainable evolution towards 6G.

Datacenters comprise large quantities of processors, memory, and input/output modules. These resources are shared among requests (jobs) submitted by datacenter users. Jobs differ in their frequency of arrivals, demand for resources, and execution times. Resource sharing generates contention, especially in heavily loaded systems, that must therefore implement effective scheduling policies for incoming jobs. The First-In First-Out (FIFO) policy is often used for batch jobs, but may produce under-utilization of resources, in terms of wasted servers. This is due to the fact that a job that requires many resources can block jobs arriving later that could be served because they require fewer resources. The mathematical construct often used to study this problem is the Multiserver Job Queuing Model (MJQM), where servers represent resources which are requested and used by jobs in different quantities. Unfortunately, very few explicit results are known for the MJQM, especially at realistic system loads (i.e., before saturation). In this paper, we propose the first exact analytical model of the non-saturated MJQM in case of two classes of customers with exponentially distributed service times and an arbitrary number of identical servers. Our analysis is based on the matrix geometric method. Our results provide insight into datacenter dynamics, thus supporting the design of more complex schedulers, capable of improving performance and energy consumption within large datacenters.

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.

In future radio access networks, machine learning (ML) based strategies for short-term forecasting of vehicular trajectories will be key for anticipatory resource allocation and management at the mobile edge. However, training ML models in a centralized fashion, over data collected from a massive heterogeneous and dynamic set of devices, poses significant scalability, reliability, and efficiency challenges, which are still open to date. In this article, we look at the specific issue of scalable and resource-efficient training of ML models in a vehicular environment. To address such a challenge, we propose a new Gossip Learning scheme, i.e., a fully distributed, collaborative training approach based on direct, opportunistic model exchanges via wireless device-to-device (D2D) communications with no centralized support. Our approach is based on constantly improving each node's own model instance through knowledge transfer among nodes, and on different strategies for estimating the potential contribution of neighboring nodes to the training process at a node. Extensive numerical assessments on a variety of measurement-based dynamic urban scenarios suggest that our schemes are able to converge rapidly and provide sufficiently accurate forecasts of vehicle position for time horizons which are typical of future 5 G/6 G dynamic resource allocation algorithms.

As pretty much every reader of this foreword knows, the IEEE International Conference on Communications (ICC) is the worldwide largest and most relevantconference in the field of communication technologies at large - together with GLOBECOM, it is the “flagship” event of the IEEE Communications Society. ICC has come now to its 58th annual edition since its inception in 1966, and for the first time ever it comes to Italy, heralding the activity of a large and well-structured community working in the diverse fields of communication technologies in Academia, in research Institutions, in companies. ICC 2023 is held in a very special Italian city, one that holds a particular place in worldwide history, and for sure a special place in the organizers' heart: Roma.

In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Gossip Learning scheme which relies on Floating Content to implement location-based probabilistic evolution of machine learning models in an infrastructure-less manner.We consider dynamic scenarios where continuous learning is necessary, and we adopt a mean field approach to investigate the limit performance of Floating Gossip in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Different from existing approaches in which either communication or computing aspects of Gossip Learning are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, based on opportunistic exchanges among moving users.

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.

Nowcasting, i.e., short-term forecasting, of end user location is becoming increasingly important for anticipatory resource management in radio access networks (RAN). In this paper, we look at the case of vehicles moving in dense urban environments, and we tackle the location nowcasting problem with a particular class of machine learning (ML) algorithms that goes under the name Gossip Learning (GL). GL is a peer-to-peer machine learning approach based on direct, opportunistic exchange of models among nodes via wireless device-to-device (D2D) communications, and on collaborative model training. It has recently proven to scale efficiently to large numbers of static nodes, and to offer better privacy guarantees than traditional centralized learning architectures. We present new decentralized algorithms for GL, suitable for setups with dynamic nodes. In our approach, nodes improve their personalized model instance by sharing it with neighbors, and by weighting neighbors' contributions according to an estimate of their marginal utility. Our results show that the proposed GL algorithms are capable of providing accurate vehicle position predictions for time horizons of a few seconds, which are sufficient to implement effective anticipatory radio resource management.

Internet of Things (IoT) devices often have batteries of limited capacity, which are not easily replaced or recharged. This implies very short device lifetimes, and calls for a very careful device configuration to achieve the optimal trade-off between performance and power consumption. SWIPT (Simultaneous Wireless Information and Power Transfer) deals with this problem by harvesting energy at IoT devices from the received RF signals. Studying the efficiency of SWIPT in dealing with the energy and data transfer demands of IoT nodes leads to a number of open issues. In this paper, we devise an analytical model based on stochastic geometry for a SWIPT radio access network with a dense population of IoT users. With our model, it is possible to accurately study the impact of the system parameters on the key system performance indicators, while accounting in a realistic manner for device performance, and for the statistics of time scheduling at base stations. This allows us to understand (not without some surprise) what are the most effective strategies to minimize energy consumption in a SWIPT network, and what is their potential for energy savings.

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.

Floating Content (FC) is a paradigm for localized infrastructure-less content dissemination, that aims at sharing information among nodes within a restricted geographical area by relying only on opportunistic content exchanges. FC provides the basis for the probabilistic spatial storage of shared information in a completely decentralized fashion, usually without support from dedicated infrastructure. One of the key open issues in FC is the characterization of its performance limits as functions of the system parameters, accounting for its reliance on volatile wireless exchanges and on limited user resources. This paper takes a first step towards tackling this issue, by elaborating a model for the storage capacity of FC, i.e., for the maximum amount of information that can be stored through the FC paradigm. The storage capacity of FC, and of similar probabilistic content dissemination systems, is evaluated with a powerful information theoretical approach, based on a mean field model of opportunistic information exchange. In addition, an extremely simple explicit approximate expression for storage capacity is derived. The numerical results generated by our analytical models are compared to the predictions of realistic simulations under different setups, proving the accuracy of our analytical approaches, and characterizing the properties of the FC storage capacity.

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.

Platooning of cars or trucks is one of the most relevant applications of autonomous driving, since it has the potential to greatly improve efficiency in road utilization and fuel consumption. Traditional proposals of vehicle platooning were based on distributed architectures with computation on board platoon vehicles and direct vehicle-to-vehicle (V2V) communications (or Dedicated Short Range Communication - DSRC), possibly with the support of roadside units. However, with the introduction of the 5G technology and of computing elements at the edge of the network, according to the multi-access edge computing (MEC) paradigm, the possibility emerges of a centralized control of platoons through MEC, with several significant advantages with respect to the V2V approach. For this reason, in this paper we investigate the feasibility of vehicle platooning in a centralized scenario where the control of vehicle speed and acceleration is managed by the network through its MEC facilities, possibly with a platooning-as-a-service (PaaS) paradigm. Using a detailed simulator, we show that, with realistic values of latency and packet loss probability, large platoons can be effectively controlled by MEC hosts.

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed.

Floating Content (FC) is a paradigmatic example of opportunistic infrastructure-less content sharing system where information is spread upon mobile node encounters within an area which is called the replication zone. FC allows the probabilistic spatial storage of information, even in the case of unreliable communications, with no support from dedicated servers. Given the large amount of communication and storage resources typically required to guarantee content persistence despite node mobility, a major open issue for the practical viability of FC and of similar distributed storage systems is the characterization of their storage capacity, i.e., of the maximum amount of information which can be stored for a given set of system parameters. In this paper, we propose a simple yet powerful information theoretical model of the storage capacity of probabilistic distributed storage systems such as FC, based on a mean field model of opportunistic information exchange. We evaluate numerically our results, and validate the model by means of realistic simulations, showing the accuracy of our mean field approach and characterizing the properties of the FC storage capacity versus the main system parameters.

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 capacity (i.e., the maximum achievable throughput) of radio access networks that exploit mobile small cell base stations carried by vehicles for adaptive densification in urban areas. While traditional approaches for radio access network densification with fixed small cell base stations are proving ineffective and extremely costly, mobile small cell base stations carried by vehicles can provide adaptive densification while achieving higher efficiency and lower cost. As a matter of fact, the existence of correlations between the number of mobile network subscribers and the number of vehicles in a given area allows for the spontaneous creation of temporary dense small cell deployments where and when needed. Ultimately, this approach to Radio Access Network densification increases efficiency, hence reducing costs for the operator. In this context, we first present an approach for the computation of the maximum throughput that can be obtained in an area served by traditional fixed base stations and mobile small cell base stations. We then provide initial estimates for the throughput improvements with respect to traditional deployments that rely on fixed base stations only. Evaluations in the realistic case study of the main railway station area in Milan, Italy, reveal that the use of mobile base stations achieves throughout gains up to 120% over legacy fixed access infrastructures, while granting higher fairness among subscribers.

The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

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.

We look at a very simple RAN (Radio Access Network) configuration comprising one macro cell and one small cell, the latter being strategically positioned to absorb the traffic peaks that occur in some time periods in a portion of the area covered by the macro cell. We study this two-cell system with a simple model based on a network of two queues, and we examine the system performance for variable parameter values, showing that some of the emerging behaviors can be critical. In particular, we see that when the handover rate out of the small cell increases, the blocking probability in the macro cell also increases, quickly reaching unacceptable levels. This can be a problem, since high handover rates correspond to limited dimensions of the small cell with respect to the macro cell, which is what is normally expected, unless the small cell is deployed in an area of very slow end user mobility. These behaviors (although possibly not applicable to all small cell scenarios) can have an important impact on the deployment of small cells, which are expected to become increasingly popular because of the need to provide additional capacity in RANs through densification of the cell layout.

This paper studies the performance gains resulting from the sharing of energy and network resources in the case of co-located base stations of different mobile network operators, powered by photovoltaic panels, and equipped with energy storage. Three configurations are considered for base station cooperation. The first one assumes two non-cooperating base stations, each one exploiting its own power system and serving its own customers, hence with no sharing. The second considers a shared power system, but no cooperation in customer service. The third looks at cooperation in both energy production and service provisioning, since only one base station handles all customers when traffic is low. Using an analytical modeling framework, we compute performance metrics for the three cases, and we show that significant gains are possible in the case of energy and network sharing.

Information sharing is becoming a relevant issue for mobile broadband operators, due to the increasing popularity of social networks, to the increasing volumes of shared information, and to the steady increase in the number and capabilities of mobile devices connected to the Internet. Offloading information sharing services from the cellular infrastructure to device-to-device (D2D) communications can offer a welcome reduction of traffic. This paper discusses experiments with a smartphone information sharing application that can be used on commercial-off-the-shelf devices, with no need to root the device's software. In order to avoid unrealistic assumptions on the behavior of D2D communications, this work includes and builds upon the implementation of an Android application that supports infras-tructureless distributed content sharing among wireless devices using Wi-Fi Direct. The collected experimental data permit a detailed analysis of the occurring events, and a careful assessment of the performance of pervasive information sharing services. Our experiments reveal that many assumptions commonly used in the literature do not hold in real settings. We conclude that delay-tolerant services can be supported, albeit we also show that high densities of devices can (somewhat counter-intuitively) impair performance.

We consider the practical problem of video surveillance in public transport systems, where security videos are stored onboard, and a central operator occasionally needs to access portions of the recordings. When this happens, the selected video must be uploaded within a deadline, possibly using multiple parallel wireless interfaces. Interfaces have different associated costs, related to tariffs charged by Mobile Network Operators (MNOs), energy consumption, data quotas, system load. Our goal is to choose which interfaces to use, and when, so as to minimize the cost of the upload while meeting the deadline, despite the unknown short-term variations in throughput. To achieve this goal, we first collect real traces of mobile uploads from vehicles for different MNOs. Examination of these traces confirms the unpredictability of the short-term throughput of wireless connections, and motivates the adoption of adaptive schedulers with limited a-priori knowledge of the system status. To effectively solve our problem, we devised a family of adaptive algorithms, that we thoroughly evaluated using a trace-driven approach. Results show that our adaptive approach can effectively leverage the fundamental tradeoff between the total cost and the delivery time of content upload, despite unknown short-term variations in throughput.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

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.

This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori” decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm.

We consider a solar power supply for a LTE macro base station (BS) based on a photovoltaic (PV) panel and a battery, and we develop two discrete-time Markov chain (DTMC) models for the analysis and the dimensioning of the system elements (PV panel size and battery capacity). The DTMC models account for the solar irradiance levels in pairs or triples of consecutive days, and for the quantity of energy stored in the battery. From the DTMC steady-state (or transient) solution it is possible to derive performance metrics on which the system dimensioning can be based. We apply our models to BS locations in southern and northern Italy. Results show that the simpler model contains sufficient details for an effective system design.

Network densification is currently seen as one of the key techniques enabling Radio Access Networks (RANs) to meet the performance and functional requirements of the 5G paradigm in urban areas. Avoiding the connection of small cells to the power grid facilitates their deployment and reduces both capital and operational expenditures (CAPEX and OPEX). In this paper, we propose an approach to enable net-Zero Energy Networking (ZEN) in urban scenarios, based on dynamically managing the operating point of Base Stations (BSs), so as to drastically decrease their overall energy requirements. More specifically, we formalize the problem of optimizing the CAPEX of a ZEN, while guaranteeing quality of service (QoS) and a given level of service availability. Optimization is over power system parameters (solar panel area, battery capacity) as well as over BS power levels and user association over time. We propose a practical algorithm for the derivation of QoS-aware spatio-temporal strategies in terms of user association and BS power allocation, which, for a given expected pattern of renewable power generation, minimize the probability of service unavailability due to power shortage. Through extensive simulations using measured data, and realistic BS deployments, we show that our algorithm leads to substantial reduction in CAPEX, and to feasible power system requirements.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

Base station sharing is currently considered one of the most promising solutions for reducing the energy consumption costs of cellular networks. This paper presents a game theoretic framework for the study of such cooperative solutions where different mobile network operators (MNOs) decide to switch off subsets of their base stations during off-peak hours and roam their traffic to the remaining stations. The solution is based on a detailed optimization framework that determines exactly which base stations should remain active and how much traffic each one of them should serve, so as to maximize the aggregate energy savings. Accordingly, using the axiomatic Shapley value rule, it is determined how the benefits from the cooperation, i.e., the cost savings, should be dispersed among the cooperating MNOs. It is proved that this coalitional game with transferrable utilities has a nonempty core, and thus there exists a cooperation solution that incentivizes the participation of all operators. Moreover, using a thorough numerical analysis, it is shown that the benefits achieved with the implementation of the cooperation strategy depend mainly on the power consumption characteristics of the MNOs, which in turn are related to the number, type, and technology of their base stations. Overall, the energy savings are found to be most sensitive to the technology of the used base stations, and more precisely to the no-load base station energy consumption which defines the energy waste in a network.

The use of solar energy to power base stations of cellular networks is becoming increasingly interesting, in both areas where the power grid is not present or not reliable, and where the power grid is ubiquitous and reliable, but energy costs keep growing. In this paper, we investigate the dimensioning of the photovoltaic panel and energy storage of a hybrid base station powering system that can exploit both solar and grid energy. The objective of the dimensioning is the minimization of the total capital and operational expenditures over a period of 10 years, accounting for the evolution of technology and traffic load. Results show that in a south European city like Torino, a hybrid base station powering system allows significant cost and size reductions, with respect to the case of solar energy only (and of a diesel power generator), and roughly equals the cost of the grid-only case in 8–9 years. When the extra energy produced by the solar panel can be sold back to the grid, the hybrid systems allow significant savings with respect to the grid-only case. For the city of Aswan, with a production that is much higher than in Torino and more constant over the year, costs of pure solar and hybrid systems are significantly lower in absolute terms; hybrid systems result to be still advantageous with respect to pure solar systems.

This work presents the first experimental evaluation of the Floating Content (FC) communication paradigm in a campus/large office setting. By logging information transfer events we have characterized mobility patterns, and we have assessed the performance of services implemented using the FC paradigm. Our results unveil the key relevance of group dynamics in user movements for the FC performance. Surprisingly, in such an environment, our results show that a relatively low user density is enough to guarantee content persistence over time, contrarily to predictions from available models. Based on these experimental findings, we develop a novel simple analytical model that accounts for the peculiarities of the mobility patterns in such a setting, and that can accurately predict the effectiveness of FC for the implementation of services in a campus/large office setting.

This paper presents the first systematic investigation of the potential performance gains for crowdsourcing systems, deriving from available information at the requester about individual worker earnestness (reputation). In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple “maximum a-posteriori“ decision rule. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the LRA decision rule introduced in the literature.

In this paper we investigate the potential energy saving inherent in the network sharing approach, whereby all (or significant parts) of the network infrastructures existing in a country can be shared by different network operators. In our study we consider European mobile network operators, and we use simple analytical models to show that in most European countries the amount of energy necessary to run mobile networks can be reduced by 35 to 60% with respect to the case in which each operator manages a separate network infrastructure.

Context-awareness is a peculiar characteristic of an ever expanding set of applications that make use of a combination of restricted spatio-temporal locality and mobile communications, to deliver a variety of services to the end user. It is expected that by 2014 more than 1.5 billion people would be using applications based on local search (search restricted on the basis of spatio-temporal locality), and that mobile location based services will drive revenues of more than $15 billion worldwide. A common feature of such context-aware applications is the fact that their communication requirements significantly differ from ordinary applications. For most of them, the scope of generated content itself is local. This locally relevant content may be of little concern to the rest of the world, therefore moving this content from the user device to store it in a well-accessible centralized location and/or making this information available beyond its scope represents a clear waste of resources (connectivity, storage). Due to these specific requirements, opportunistic communication can play a special role when coupled with context-awareness. The benefit of opportunistic communications is that it naturally incorporates context as spatial proximity is closely associated with connectivity.

Context-awareness is a peculiar characteristic of an expanding set of applications that make use of a combination of restricted spatio-temporal locality and mobile communications, to deliver a variety of services. Opportunistic communications satisfy well the communication requirements of these applications, because they naturally incorporate context. Recently, an opportunistic communication paradigm called "Floating Content" (FC) was proposed, to support infrastructure-less, distributed content sharing. But how good is floating content in supporting context-aware applications? In this work, we present a simple approximate analytical model for the performance analysis of context-aware applications that use floating content. We estimate the "success probability" for a representative category of context-aware applications, and show how the system can be configured to achieve the application's target QoS. We validate our model using extensive simulations under different settings and mobility patterns, showing that our model-based predictions are highly accurate under a wide range of conditions.

We consider the problem of minimizing the energy consumed in a cellular access network, under loads that slowly vary over space and time, while guaranteeing quality of service (QoS). In particular, we formalize the problem of jointly optimizing the base stations (BS) power levels and the association of users to BSs, while guaranteeing a minimum throughput to each user, and a target value of blocking probability. We propose abstractions that enable tracking of long-term spatial load distributions, and a practical algorithm for energy efficient user association and base station power allocation. Our algorithm is applicable to arbitrary (planar) BS layouts, to settings with interference, to different BS energy models, and to arbitrary user distributions over the service area. Through extensive simulations using measured data, and realistic BS deployments, we show that our algorithm leads to substantial energy savings both with traditional BS designs and with energy-proportional equipment, and we demonstrate the potential of BS sleep modes to achieve network-level energy proportionality.

In this paper, we address the problem of determining an optimal topology for Bluetooth wireless personal area networks (BT-WPANs). In BT-WPANs, multiple communication channels are available, through a frequency hopping technique. The way network nodes are grouped to share the same channel, and which nodes are selected to bridge traffic from a channel to another, has a significant impact on the capacity and throughput of the system, as well as the nodes' battery lifetime. The determination of an optimal topology is thus extremely important. Our approach is based on a min-max formulation of the optimization problem, which produces topologies that minimize the traffic load of the most congested node in the network (thus also minimizing energy consumption) while meeting the traffic requirements and the constraints posed by the BT-WPAN technology. We investigate the performance of the topologies produced by our optimization approach as the system requirements vary, and evaluate the trade-offs existing between system complexity and network efficiency. Results show that a topology optimized for some traffic requirements is remarkably robust to changes in the traffic pattern. Due to the problem complexity, the optimal solution is attained in a centralized manner. Although this implies severe limitations, a centralized solution can be applied whenever a network coordinator is elected, and provides a useful term of comparison for any distributed heuristics.

Lists the Autonomic Networks Program Committee ((in alphabetical order).

In this paper we consider packet networks loaded by admissible traffic patterns, i.e. by traffic patterns that, if optimally routed, do not overload network resources. In these conditions, we study the combined behavior of distributed dynamic routing and scheduling algorithms based upon link state information, with no knowledge of the average traffic pattern, and we prove that simple schemes can achieve the same network throughput as optimal centralized routing and scheduling algorithms with complete information on the traffic pattern. Our study is based on a flow-level abstract model of the network, and considers elastic traffic, i.e., we assume that flows can adapt their transmission rates to network conditions. As a result, our model captures some of the main features of Internet traffic and of quality-of-service routing approaches being currently proposed for IP networks. We show that efficient dynamic routing and scheduling algorithms can be implemented in a distributed way, and we prove that maximum throughput is achieved also in case of temporary mismatches between the actual link metrics and those used by the routing algorithm. This is a particularly relevant aspect, since any distributed implementation of a routing algorithm requires a periodic exchange of link state information among nodes, and this implies delays, and thus time periods in which the actual link state is not known.

In this work we present an approach for maintaining the topology of a Bluetooth scatternet at an optimal configuration despite the dynamic behavior of the nodes in time. Our goal is to keep the ratio of the average scatternet throughput and node power consumption as high as possible while nodes unpredictably change their communication peers and migrate across the network. The approach consists in keeping the total number of hops between communicating nodes relatively low by periodically reconfiguring the scatternet topology based on the actual traffic pattern of the network

In this correspondence, we discuss the stability of scheduling algorithms for input-queueing (IQ) and combined input/output queueing (CIOQ) packet switches. First, we show that a wide class of IQ schedulers operating on multiple traffic classes can achieve 100% throughput. Then, we address the problem of the maximum throughput achievable in a network of interconnected IQ switches and CIOQ switches loaded by multiclass traffic, and we devise some simple scheduling policies that guarantee 100% throughput. Both the Lyapunov function methodology and the fluid modeling approach are used to obtain our results.

In this paper we propose a new fluid model approach in which a different description of the dynamics of traffic sources is adopted, exploiting partial differential equations. This new description of the source dynamics allows the natural representation of short-lived as well as long-lived TCP connections, with no sacrifice in the scalability of the model. In addition, the use of partial differential equations permits the description of distributions, instead of averages, thus providing better accuracy in the results. The comparison between the performance estimates obtained with fluid models and with ns-2 simulations proves the accuracy of the proposed modeling approach.

This paper proposes a distributed self-healing technique for topology formation in dynamic Bluetooth wireless personal area networks (BT-WPANs) and analyzes three new algorithms for scatternet formation. The three algorithms employ distributed procedures for the insertion of one or more nodes in a BT-WPAN, and are able to effectively compromise between the need for system efficiency and the desire to promptly adapt to topology changes. Depending on which algorithm is employed, the proposed approach generates BT-WPANs with different connectivity properties as well as topology structures.

In this paper, we propose a MMPP (Markov modulated Poisson process) traffic model that accurately approximates the LRU (long range dependence) characteristics of Internet traffic traces. Using the notion of sessions and flows, the proposed MMPP model mimics the real hierarchical behavior of the packet generation process by Internet users. Thanks to its hierarchical structure, the proposed model is both simple and intuitive: it allows the generation of traffic with the desired characteristics by easily setting a few input parameters which have a clear physical meaning. Results prove that the queuing behavior of the traffic generated by the MMPP model is coherent with the one produced by the real traces collected at our institution edge router under different networking scenarios and loads. Due to its characteristics, the proposed MMPP traffic model can be used as a simple and manageable tool for IP network performance analysis, as well as for network planning and dimensioning.

Fluid models of IP networks have been recently proposed as a way to break the scalability barrier of traditional discrete state-space models, both simulative (e.g., ns-2) and analytical (e.g., queues and Markov chains). Fluid models adopt an abstract deterministic description of the average network dynamics through a set of ordinary differential equations that are then solved numerically, obtaining estimates of the time-dependent network behavior. However, an important limit of the fluid model approaches presented so far in the literature is their unnatural representation of scenarios comprising the short-lived TCP flows that dominate in today's Internet. In this paper we propose a new fluid model approach in which a different description of the dynamics of traffic sources is adopted, exploiting partial differential equations. This new description of the source dynamics allows the natural representation of short-lived as well as long-lived TCP connections, with little sacrifice in the scalability of the model. In addition, the use of partial differential equations permits the description of distributions, instead of averages, thus providing better accuracy in the results. The comparison between the performance estimates obtained with fluid models and with ns simulations proves the accuracy of the proposed modeling approach.

Mobility, and the fact that nodes may change their communication peers in time, generates a permanently changing traffic flows in the Bluetooth scatternet. Thus, forming an optimal scatternet for a given traffic pattern may not be enough, rather a scatternet that best supports the traffic flows as they vary in time is required. In this article we propose an algorithm suite that enables us to modify the nodes' links and roles. Periodically executing these algorithms helps in maintaining the distance (measured in hops weighted with the corresponding traffic intensity) between every source-destination pair at a minimum. This allows for a higher network throughput, lower packet delivery delay, nodes' energy consumption, and reduced communication overhead.

In this paper, we present a new methodology for the design of fault-tolerant logical topologies in wavelength-routed optical networks supporting Internet protocol (IP) datagram flows. Our design approach generalizes the "design protection" concepts, and relies on the dynamic capabilities of IP to reroute datagrams when faults occur, thus achieving protection and restoration, and leading to high-performance cost-effective fault-tolerant logical topologies. In this paper, for the first time we consider resilience properties during the logical topology optimization process, thus extending the optimization of the network resilience also to the space of logical topologies. Numerical results clearly show that our approach outperforms previous ones, being able to obtain very effective survivable logical topologies with limited computational complexity.

This paper presents a new analytical model for the estimation of the performance of TCP connections. The model is based on the description of the behavior of TCP in terms of a closed queueing network. The model is very accurate, deriving directly from the finite state machine description of the protocol. The assessment of the accuracy of the analytical model is based on comparisons against detailed simulation experiments developed with the ns-2 package. The protocol model interacts with an IP network model that can take into account meshed topologies with several bottlenecks. Numerical results indicate that the proposed closed queueing network model provides accurate performance estimates in all situations. A novel and interesting property of the model is the possibility of deriving ensemble distributions of relevant parameters, such as, for instance, the transmission window size or the timeout probability, which provide useful insight into the protocol behavior and properties.

Instability in packet-switching networks is normally associated with overload conditions, since queueing network models show that, in simple configurations, only overload generates instability. However, some results showing that instability can happen also in underloaded queueing networks began to appear about a decade ago. Underload instabilities can be produced by: 1) customer routes that visit the same queues several times; 2) variations of the customer service times at the different queues; and 3) complex scheduling algorithms. We study, using fluid models and adversarial queueing theory, possible underload instabilities due to flow schedulers in packet networks, focusing on output queued switches with strict priority (SP) schedulers and Generalized Processor Sharing (GPS) schedulers. The considered scenarios always refer to acyclic packet routes and consider customer service times that vary only according to channel capacities, thus resembling the approaches being currently considered to provide QoS in the Internet. Our (in)stability results are rather surprising: SP schedulers appear to be more robust than GPS schedulers whenever exact information on the effective average packet flow rates is not available.

In this paper, we propose a new technique for the performance analysis of cellular mobile communication networks based on a frequency division multiple access/time division multiple access scheme (such as global system for mobile communications), in which the utilization of two separate frequency bands leads to a complex cellular structure with overlapping microcells and macrocells. Call durations and dwell times are described by random variables with general distributions; the call duration distribution is then approximated by a two-phase hyper-exponential distribution with the same first and second moments as the original general distribution. The analysis technique is based on Markovian assumptions as regards the traffic flows entering both microcells and macrocells, as well as an assumption of flow balance between handovers into and out of any cell. The analytical model is validated against results of detailed simulation experiments for various system configurations, and shown to provide quite accurate predictions.

Analytical approaches for the performance investigation of portions of the Internet often consider the behavior of TCP over congested (or bottleneck) links. In several cases, the analysis is based on an iterative fixed point approximation (FPA) to compute the equilibrium point, in terms of packet loss rate and offered load, that represents the operating point of the network. Almost invariably, the FPA is conjectured to converge, but no proof of convergence is provided. This paper proves that a general model of a reliable protocol (such as TCP) over congested links converges to a unique stable solution under mild regularity conditions. This provides a justification of the convergence observed in the literature and a solid base for the further development of analytical approaches based on FPAs.

Although instability in packet networks has been traditionally associated with overload conditions (because queueing network models show that, in simple configurations, only overload generates instability), some results showing instability in underloaded packet networks have appeared in the recent literature. In M. Ajmone Marsan, et al. (2003) we studied, with fluid models and with adversarial queueing theory, possible underload instabilities due to complex scheduling algorithms that closely resemble quality of service (QoS) schedulers considered today for packet networks, when sources are non-adaptive. In this paper we extend the study of the underload instabilities to packet networks carrying the traffic generated by elastic (rate-adaptive) sources. In particular, we consider additive-increase, multiplicative-decrease (AIMD) sources, and we show this type of adaptivity is not sufficient to mitigate the phenomena leading to underload instabilities and to reduced network throughput.

Instability in packet-switching networks is normally associated with overload conditions, since queueing network models show that, in simple configurations, only overload generates instability. However, some results showing that instability can happen also in underloaded queueing networks appeared in the recent literature. Underload instabilities can be produced by complex scheduling algorithms, that bear significant resemblance to the Quality of Service (QoS) schedulers considered today for packet networks. In this paper, we study with fluid models and with adversarial queueing theory possible underload instabilities due to strict-priority schedulers and to Generalized Processor Sharing (GPS) schedulers.

A significant research effort has been devoted in recent years to the design of simple and efficient scheduling policies for input queued (IQ) and combined input output queued (CIOQ) packet switches. As a result, a number of switch control algorithms have been proposed. Among these, scheduling policies based on maximum weight matching (MWM) were identified as optimal, in the sense that they were proved to achieve 100% throughput under any admissible arrival process satisfying the strong law of large number. On the contrary, it has been recently shown that the usual MWM policies fail to guarantee 100% throughput in networks of interconnected IQ/CIOQ switches. Hence, new policies suited for networks of interconnected switches were proposed and proved to achieve 100% throughput. All of these new policies require coordination and cooperation among different switches. In this paper we address the open problem of the existence of local scheduling policies that guarantee 100% throughput in a network of IQ/CIOQ switches, providing a positive answer to such question. The only assumptions on the input traffic are that it satisfies the strong law of large numbers and that it does not oversubscribe any link in the network.

In this paper we describe an analytical model to compute the packet delay distribution in a cell of a wireless network operating according to the GSM/GPRS standard. GSM (Global System for Mobile communications) is the most widely deployed wireless telephony standard, and GPRS (Generalized Packet Radio Service) is the technology that is now available to integrate packet data services into GSM networks. By comparing the performance estimates produced by the analytical model against those generated by detailed simulation experiments, we show that the proposed modeling technique is quite accurate. In addition, we show that the results produced by the analytical model are extremely useful in the design and planning of a wireless voice and data network.

No abstract available.

In this article, we develop a general methodology, mainly based upon Lyapunov functions, to derive bounds on average delays, and on averages and variances of queue lengths in complex systems of queues. We apply this methodology to cell-based switches and routers, considering first output-queued (OQ) architectures, in order to provide a simple example of our methodology, and then both input-queued (IQ), and combined input/output queued (CIOQ) architectures. These latter switching architectures require a scheduling algorithm to select at each slot a subset of input-buffered cells that can be transferred toward output ports. Although the stability properties (i.e., the limit throughput) of IQ and CIOQ cell-based switches were already studied for several classes of scheduling algorithms, very few analytical results concerning cell delays or queue lengths are available in the technical literature. We concentrate on Maximum Weight Matching (MWM) and Maximal Size Matching (mSM) scheduling algorithms; while the former was proved to maximize throughput, the latter allows simpler implementation. The derived bounds are shown to be rather tight when compared to simulation results.

In small cell-based Internet protocol routers, multicast traffic is generally handled by appending to each cell a local multicast label (LML) containing a bitmap with as many bits as switch ports, so as to identify the ports to which a copy of the cell has to be transferred. This approach is not feasible for switches having 128 ports or more, because the LML length would rise above 16 bytes, thus representing an intolerable overhead, given the small size of cells (typically 64 bytes). We discuss both static and adaptive lossy compression algorithms to reduce the size of LMLs to be attached to multicast cells, at the price of the delivery of cells to a larger set of outputs than necessary, and we compare the compression algorithms performance in terms of switch bandwidth waste, using both analytical and simulation models.

A significant research effort has been devoted to the design of simple and efficient scheduling policies for input queued (IQ) and combined input-output queued (CIOQ) packet switches. As a result, a number of switch control algorithms have been proposed. Among these, scheduling policies based on maximum weight matching (MWM) were identified as optimal, in the sense that they were proved to achieve 100% throughput under any admissible arrival process satisfying the strong law of large number. On the contrary, it has been shown that the usual MWM policies fail to guarantee 100% throughput in networks of interconnected IQ/CIOQ switches. Hence, new policies suited for networks of interconnected switches were proposed and proved to achieve 100% throughput. All of these new policies require coordination and cooperation among different switches. We identify scheduling policies that require no coordination among switches (and are, thus, said to be local), and that guarantee 100% throughput in a network of IQ/CIOQ switches. The only assumptions on the input traffic pattern are that it is stationary, satisfies the strong law of large numbers and does not oversubscribe any link in the network.

The paper studies input-queued packet switches loaded with both unicast and multicast traffic. The packet switch architecture is assumed to comprise a switching fabric with multicast (and broadcast) capabilities, operating in a synchronous slotted fashion. Fixed-size data units, called cells, are transferred from each switch input to any set of outputs in one time slot, according to the decisions of the switch scheduler, that identifies at each time slot a set of nonconflicting cells, i.e., cells neither coming from the same input, nor directed to the same output. First, multicast traffic admissibility conditions are discussed, and a simple counterexample is presented, showing intrinsic performance losses of input-queued with respect to output-queued switch architectures. Second, the optimal scheduling discipline to transfer multicast packets from inputs to outputs is defined. This discipline is rather complex, requires a queuing architecture that probably is not implementable, and does not guarantee in-sequence delivery of data. However, from the definition of the optimal multicast scheduling discipline, the formal characterization of the sustainable multicast traffic region naturally follows. Then, several theorems showing intrinsic performance losses of input-queued with respect to output-queued switch architectures are proved. In particular, we prove that, when using per multicast flow FIFO queueing architectures, the internal speedup that guarantees 100% throughput under admissible traffic grows with the number of switch ports.

In this paper we consider the problem of resource management in GSM/GPRS cellular networks offering not only mobile telephony services, but also data services for the wireless access to the Internet. In particular we investigate channel allocation policies that can provide a good tradeoff between the QoS guaranteed to voice and data services end users, considering three different alternatives, and developing analytical techniques for the assessment of their relative merits. The first channel allocation policy is called voice priority, since it gives priority to voice in the access to radio channels; we show that this policy cannot provide acceptable performance to data services, and we discuss the reasons for this shortcoming. The second channel allocation policy is called R-reservation; it statically reserves a fixed number of channels to data services, thus drastically improving their performance, but subtracting resources from voice users, even when these are not needed for data, thus inducing an unnecessary performance degradation for voice services. The third channel allocation policy is called dynamic reservation; as the name implies, it dynamically allocates channels to data when necessary, using the information about the queue length of GPRS data units within the base station. A threshold on the queue length is used in order to decide when channels must be allocated to data. Numerical results, show that the dynamic reservation channel allocation policy can provide very effective performance tradeoffs for data and voice services, with the additional advantage of being easily managed through the setting of the threshold value.

We investigate techniques for the support of multicast traffic in IP routers that have a large number of switch fabric ports (from 128 to 1024) and internally operate on relatively small fixed-size data units (cells of 64 bytes each). In small packet switches, multicast traffic is typically handled by prepending a local multicast label (LML) to each cell. The LML consists of a bitmap with as many bits as switch ports. The bitmap identifies the set of ports to which copies of the cell must be transferred. The bitmap approach is no longer feasible in switches with 128 ports or more, where the LML length cannot be smaller than 16 bytes, an intolerable overhead when the internal cell payload is only 64 bytes. We devise several compression algorithms, both static and adaptive, to reduce the size of the LML's to be attached to multicast cells. The algorithms define compressed representations of the distribution sets of the multicast cells, trading the additional bandwidth needed to transfer redundant cell copies (i.e., copies directed to outputs that do not belong to the actual distribution sets) for the cell header overhead otherwise needed to deliver the cells only to the proper outputs. We use simulation experiments to compare the performance of the compression algorithms.

We define an analytical approach and a methodology to determine the set of service level agreements (SLA) that can be effectively supported by a DiffServ IP network. We consider the assured forwarding (AF) per hop behavior (PHB), and, based on the SLA probabilistic description, we derive a worst-case mathematical formulation for the overbooking probability, i.e., the probability that the traffic crossing any link of a source-destination path exceeds the link capacity. We then compare analytical results with simulation of different traffic models.

Many studies provide an extended investigation of the maximum throughput achievable in input-queueing (IQ) or combined-input-and-output-queueing (CIOQ) packet switches. Some scheduling policies, among which are maximum weight matching algorithms, were identified as optimal, in the sense that they were proved to achieve 100% throughput under any admissible single-class traffic pattern. Most of the results in the literature, however, consider just one switch in isolation, operating on packets belonging to a single traffic class. In this paper we first generalize known results, showing that a wide class of IQ schedulers operating on multiple traffic classes can achieve 100% throughput. In addition, we address the problem of the maximum throughput achievable in a network of interconnected IQ switches loaded by multiclass traffic, and we devise some simple scheduling policies that guarantee 100% throughput when switches are interconnected in a network. Both the Lyapunov function methodology and the fluid models approach are used to obtain our results.

In this paper, we address the problem of determining an optimal topology for Bluetooth wireless personal area networks (BT-WPAN). In BT-WPAN, multiple communication channels are available, thanks to the use of a frequency hopping technique. The way network nodes are grouped to share the same channel, and which nodes are selected to bridge traffic from a channel to another, has a significant impact on the capacity and the throughput of the system, as well as the nodes' battery lifetime. The determination of an optimal topology is thus extremely important; nevertheless, to the best of our knowledge, this problem is tackled here for the first time. Our optimization approach is based on a model derived from constraints that are specific to the BT-WPAN technology, but the level of abstraction of the model is such that it can be related to the more general field of ad hoc networking. By using a min-max formulation, we find the optimal topology that provides full network connectivity, fulfills the traffic requirements and the constraints posed by the system specification, and minimizes the traffic load of the most congested node in the network, or equivalently its energy consumption. Results show that a topology optimized for some traffic requirements is also remarkably robust to changes in the traffic pattern. Due to the problem complexity, the optimal solution is attained in a centralized manner. Although this implies severe limitations, a centralized solution can be applied whenever a network coordinator is elected, and provides a useful term of comparison for any distributed heuristics.

We consider input-queued switch architectures dealing at their interfaces with variable-size packets, but internally operating on fixed-size cells. Packets are segmented into cells at input ports, transferred through the switching fabric, and reassembled at output ports. Cell transfers are controlled by a scheduling algorithm, which operates in packet-mode: all cells belonging to the same packet are transferred from inputs to outputs without interruption. We prove that input-queued switches using packet-mode scheduling can achieve 100% throughput, and we show by simulation that, depending on the packet size distribution, packet-mode scheduling may provide advantages over cell-mode scheduling.

In this paper we discuss the optimal design of logical topologies in wavelength-routed WDM networks supporting unicast and multicast transfer of IP datagrams. We first explain the key aspects of the problem, emphasizing the fact that in IP networks the routing algorithms are an input to the optimization problem, not an optimization target. We then provide a mixed integer linear programming formulation of the optimization problem., which however leads to unacceptably high complexity for networks of non-trivially small size. We then propose both greedy and metaheuristic approaches for the sub-optimal design of logical topologies with acceptable complexity. Finally, we derive lower bounds that allow the assessment of the performance of the proposed algorithms. Some numerical results indicate that the proposed metaheuristics largely outperform the greedy approaches, and are able to obtain very good logical topologies.

In this paper we illustrate a new methodology for the design of fault-tolerant logical topologies in wavelength-routed optical networks exploiting wavelength division multiplexing, and supporting both unicast and multicast IP datagram flows. Our approach to protection and restoration generalizes the "design protection" concepts, and relies on the dynamic capabilities of IP routing to re-route IP datagrams when faults occur, thus leading to high-performance cost-effective fault-tolerant logical topologies. Our design methodology for the first time considers the resilience properties or the topology during the logical topology optimization process, thus extending the optimization of the network resilience performance also on the space of the logical topologies. Numerical results clearly show that our approach is able to obtain very good logical topologies with limited complexity.

This paper focuses on multicast support in input-queued packet switches with internal multicast capabilities. Besides providing an overview of some alternative architectures and algorithms proposed in the literature, the paper brings two original contributions. First, multicast traffic admissibility conditions are defined, and theorems showing intrinsic performance losses of input-queued with respect to output-queued switch architectures are proved. Second, the optimal scheduling discipline in transferring multicast packets from switch inputs to switch outputs is defined. From the definition of the optimal multicast scheduling discipline, the formal characterization of the sustainable multicast traffic region naturally follows. Both results aim at a correct formal definition of the considered problem, in order to identify a sound starting point for the design of heuristics that approximate the optimal solution at a complexity compatible with available technologies.

This paper presents a new analytical model for the estimation of the performance of TCP connections. The model is based on the description of the behavior of TCP-Tahoe in terms of a closed queueing network, whose solution can be obtained with very low cost, even when the number of TCP connections that interact over the underlying IP network is huge. The protocol model can be very accurate, deriving directly from the finite state machine description of the protocol. The assessment of the accuracy of the analytical model is based on comparisons against detailed simulation experiments developed with the ns-2 package. Numerical results indicate that the proposed closed queueing network model provides extremely accurate performance estimates, not only for average values, but even for distributions, in the case of the classical single-bottleneck configuration, as well as in more complex networking setups.

We develop a general methodology, mainly based upon Lyapunov functions, to derive bounds on average delays, and on queue size averages and variances of complex systems of queues. We then apply this methodology to input-buffered, cell-based switch and router architectures. These architectures require a scheduling algorithm to select at each slot a subset of input-buffered cells which can be transferred towards output ports. Although the stability properties (i.e., the limit throughput) of input-buffered, cell-based switches was already studied for several classes of scheduling algorithms, no analytical results concerning cell delays or queue sizes are yet available in the technical literature. We concentrate on purely input-buffered switches that adopt a maximum weight matching scheduling algorithm, that was proved to be the scheduling algorithm providing the best performance. The derived bounds proved to be rather tight, when compared to simulation results.

Input-queued switch architectures play a major role in the design of high performance switches and routers for packet networks. These architectures must be controlled by a scheduling algorithm, which solves contentions in the transfer of data units from inputs to outputs. Several scheduling algorithms were proposed in the literature for input-queued cell switches, operating on fixed-size data units. In this paper we consider the case of packet switches, i.e., devices operating on variable-size data units at their interfaces, but internally operating on cells, and we propose novel extensions of known scheduling algorithms. We prove that the maximum throughput achievable by input-queued packet switches is identical to that achievable with input- and output-queued cell switches. We show by simulation that, in the case of packet switches, input-queued architectures may provide performance advantages over output-queued architectures.

In this paper we discuss the throughput achievable in input-queued cell-based switches loaded with multicast traffic. The switch architecture is assumed to comprise a synchronous broadcast switching fabric, where fixed-size data units, called cells, can be transferred in one slot from one Input to any set of outputs. The switch scheduler must select the time slots for transfers of non-conflicting cells, i.e., cells neither coming from the same input nor directed to the same output. Contrary to the case of unicast traffic, for which input-queued switches were proved to yield the same throughput as output queued switches, we show by simulation experiments and analytical modeling that throughput limitations exist in input-queued switches loaded with multicast traffic.

We present an analytical approach for the joint optimization of quality of service (in terms of call blocking probability) and tariffs for a mobile telephony network. The analytical approach is based on the combination of traditional telecommunication system design techniques and econometric approaches for profit maximization.

A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephony networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments, results of a previously proposed Markovian analysis approach, and upper and lower bounds to the call blocking probability. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.

We consider cell-based switch and router architectures whose internal switching matrix does not provide enough speed to avoid input buffering. These architectures require a scheduling algorithm to select at each slot a subset of input buffered cells which can be transferred toward output ports. We propose several classes of scheduling algorithms whose stability properties are studied using analytical techniques mainly based upon Lyapunov functions. Original stability conditions are also derived for scheduling algorithms that are being used today in high-performance switch and router architectures.

We consider cell-based switch architectures in which the speedup of the internal switching fabric is not large enough to avoid input buffering. These architectures require a scheduling algorithm to select at each slot a subset of input buffered cells which can be transferred towards output ports. The stability properties of maximal size matching (MSM) scheduling algorithms are studied in the paper, using analytical techniques primarily based upon Lyapunov functions. The main result of the paper is the proof that, for a wide class of MSM scheduling algorithms, stability is guaranteed by an internal switch speedup equal to two.

A novel approximate technique is proposed for the estimation of call blocking probabilities in cellular mobile telephone networks where call blocking triggers customer retrials. The approximate analysis technique is based on Markovian models with state spaces whose cardinalities are proportional to the maximum number of calls that can be simultaneously in progress within cells. The accuracy of the approximate technique is assessed by comparison against results of detailed simulation experiments. Numerical results show that the proposed approximate technique is very accurate, in spite of the remarkably small state spaces of the Markovian models.

We consider cell-based switch architectures, whose internal switching matrix does not provide enough speed to avoid input buffering. These architectures require a scheduling algorithm to select at each slot a subset of input buffered cells which can be transferred towards output ports. The stability properties of several classes of scheduling algorithms are studied in the paper, using analytical techniques mainly based upon Lyapunov functions. Original stability conditions are derived for some scheduling algorithms that are being used today in high-performance switch architectures.

In this paper we consider dual-band GSM networks, where voice and data services are offered to users moving over an area covered with overlapping macrocells and microcells. For this wireless network context we develop simple approximate analytical models of the system dynamics, and we exploit such analytical models for the design and planning of the critical system parameters, with particular attention to the number of traffic channels to be activated within macrocells.

This paper proposes an analytical approach for the evaluation of the quality of service (QoS) perceived by end users in cellular communication systems which provide radio coverage of suburban loads or highways, where the user mobility is typically very high. The approach is based on the interaction between the queueing model of an individual cell and an accurate description of the end user behavior, the latter comprising a probabilistic description of the user mobility and call duration. Different classes of user mobility and services can be considered. As a measure of the QoS perceived by end users, the probability that a call completes successfully is derived.

This paper addresses the design of packet transmission schedules in photonic slotted wavelength-division multiplexing/time-division multiplexing broadcast-and-select networks with W wavelengths and N nodes. Nodes are equipped with one tunable-wavelength transmitter with nonnegligible tuning times and one fixed-wavelength receiver. A new scheduling algorithm that exploits multihop packet transfer to shorten the duration of scheduling periods is first proposed. A single-hop scheduling algorithm that performs slightly better than previous proposals is then described. A simulation-based analysis of the two algorithms shows that they jointly lead to significant improvements in both throughput and delay with respect to previous single-hop schedules.

Management of terminal handovers is one of the challenges in mobile wireless ATM (W-ATM) systems due to the connection-oriented nature of ATM. When the ATM connection is re-established to follow the terminal roaming from one base station to another, seamless handover is necessary to guarantee the required quality of service (QoS). A promising procedure is based on the use of handover buffers at the (destination) base station. This paper proposes a technique to reduce the buffer requirements in seamless handovers by introducing the concept of buffer sharing at the base station (B/sup 2/S/sup 2/): connections requiring handover at the same time and towards the same base station share a common pool of buffers available at the base station. When compared to the standard dedicated buffer (DB) approach B/sup 2/S/sup 2/ achieves the required QoS with a reduced total buffer size, and consequently with a reduced cost of the base station. Simulation and numerical results are discussed to quantify this reduction.

This paper presents and evaluates a quasi-optimal scheduling algorithm for input buffered cell-based switches, named reservation with preemption and acknowledgment (RPA). RPA is based on reservation rounds where the switch input ports indicate their most urgent data transfer needs, possibly overwriting less urgent requests by other input ports, and an acknowledgment round to allow input ports to determine what data they can actually transfer toward the desired switch output port. RPA must be executed during every cell time to determine which cells can be transferred during the following cell time. RPA is shown to be as simple as the simplest proposals of input queuing scheduling, efficient in the sense that no admissible traffic pattern was found under which RPA shows throughput limitations, and flexible, allowing the support of packet-mode operations and different traffic classes with either strict priority discipline or bandwidth guarantee requirements. The effectiveness of RPA is assessed with detailed simulations in uniform as well as unbalanced traffic conditions and its performance is compared with output queuing switches and the optimal maximum weighted matching (MWM) algorithm for input-buffered switches. A bound on the performance difference between the heuristic weight matching adopted in RPA and MWM is analytically computed.

This paper presents and evaluates a quasi-optimal policy for input buffered ATM switches, named RPA (reservation with preemption and acknowledgment), comprising an input queuing discipline and a cell scheduling algorithm. RPA is based on reservation rounds where the switch input ports can indicate their most urgent cell transfer needs, possibly overwriting less urgent requests by other input ports, and an acknowledgment round to allow input ports to determine what cell they can actually transfer toward the desired switch output port. RPA is shown to be simpler than previous proposals of input queuing policies, efficient and flexible, allowing the support of different traffic classes and packet-mode operations. The effectiveness of RPA is assessed with detailed simulations in uniform, as well as unbalanced, traffic conditions.

This paper illustrates the development of an approximate analytical model for a communication network providing integrated services to a population of mobile users, and presents performance results to both validate the analytical approach, and assess the quality of the services offered to the end users. The analytical model is based on continuous-time multidimensional birth-death processes, and it is focused on just one of the cells in the network. The cellular system is assumed to provide three classes of service: the basic voice service, a data service with bit rate higher than the voice service and a multimedia service with one voice and one data component. In order to improve the overall network performance, some channels can be reserved to handovers, and multimedia calls that cannot complete a handover are decoupled, by transferring to the target cell only the voice component and suspending the data connection until a sufficient number of channels becomes free. Numerical results demonstrate the accuracy of the approximate model, as well as the effectiveness of the newly proposed multimedia call decoupling approach.

This paper considers SRR (synchronous round robin), an almost optimal collision-free access scheme for all-optical packet networks based on WDM multi-channel ring topologies providing slotted channels for transmissions to disjoint subsets of destination nodes. Only a channel inspection capability and local status information are required at nodes in order to implement the access protocol. Since SRR is not able to enforce fairness by itself, MMR (multi MetaRing), a fairness control algorithm derived from those adopted in the MetaRing high-speed metropolitan area network, is superimposed to SRR. Our analysis proves that the considered access scheme, in spite of its simplicity, allows an almost optimal exploitation of the available resources while guaranteeing a fair access to all nodes.

The paper describes SR/sup 3/ (synchronous round robin with reservations) a collision-free medium access control protocol for all-optical slotted packet networks based on WDM multi-channel ring topologies where the nodes are equipped with one fixed-wavelength receiver and one wavelength-tunable transmitter. SR/sup 3/ is derived from the SRR and MMR protocols previously proposed by the authors for the same class of all-optical networks. SRR and MMR already achieve an efficient exploitation of the available bandwidth, while guaranteeing a throughput-fair access to each node. SR/sup 3/, in addition, allows the nodes to reserve slots, thereby achieving a stronger control on access delays; it is thus well suited to meet tight delay requirements, as is the case for multimedia applications. Simulation results show that SR/sup 3/ provides very good performance to guaranteed quality traffic, but also brings significant performance improvements for best-effort traffic.

WDM rings are receiving significant attention in the field of all-optical networks because their implementation appears to be feasible with state-of-the-art components. Several MAC protocols were recently proposed in order to resolve contentions among nodes sharing the available wavelengths in WDM rings. These MAC protocols achieve high efficiency but unsatisfactory fairness, due to the asymmetric position of sources with respect to destinations. Thus, in order to improve fairness, it is necessary to adopt a fairness control scheme. We discuss the adaptation of the MetaRing fairness control scheme to the context of WDM multi-channel rings. Several alternatives are considered and compared via simulation.

This paper presents an approximate analytical model for the evaluation of throughput and access delays in high-speed all-optical networks with slotted multi-channel ring topology, where each channel is shared in statistical time division by all nodes transmitting to one destination using a collision- and contention-free access protocol based on a channel inspection capability. Nodes are assumed to have only one queue to store packets for all destinations; two configurations are considered: the single buffer queue and the infinite buffer queue. Simulation results are used to assess the accuracy of the approximate model.

The paper considers medium access control protocols for all-optical packet networks based on WDM multichannel ring topologies where nodes are equipped with one fixed-wavelength receiver and one wavelength-tunable transmitter. Such networks provide separate channels for slotted transmissions to disjoint subsets of destination nodes. Some simple access protocols based on local status information are described. Since these protocols are not able to enforce fairness by themselves, fairness control algorithms derived from those adopted in the Metaring high-speed metropolitan area network are also proposed. Analytical and simulation results are presented to assess the capacity of the proposed protocols in uniform traffic conditions, with a particular focus on the case where at each node the packet to be transmitted is randomly selected. In spite of the simplicity of the proposed access schemes, numerical results show that good performance can be achieved and the fairness problems inherent in the considered network topologies can be overcome.

Performance analysis and formal correctness verification of computer communication protocols and distributed systems have traditionally been considered as two separate fields. However, their integration can be achieved by using formal description techniques as paradigms for the development of performance models. This paper presents a novel extension of LOTOS, one of the two formal specification languages that were standardized by ISO. The extension is specifically conceived to integrate performance analysis and formal verification. The extended language syntax and semantics are formally defined, along with a mapping from extended specifications to performance models, The mapping preserves the specified observable behavior. Two simple examples, a stop-and-wait protocol and a time-sharing system, are used to concretely demonstrate the new approach and to validate it.>

Two regular meshed topologies are compared in terms of their possible use for implementing large all-optical wavelength routing communication networks or interconnection systems. It is assumed that the networks provide full connectivity among users and operate with either packet or circuit switching in a wavelength-division-multiplexing (WDM) environment, so that source-destination pairs are identified through a frequency and a physical path. The topologies considered are the K-dimensional bidirectional square lattice and the shuffle topology. The comparison is based on the maximum and average distance between nodes, and on the minimum number of identifiers (frequencies in the WDM comb) necessary to discriminate all source-destination pairs.>

Generalized stochastic Petri nets (GSPNs) are used to study the performances of several dynamic polling orders in symmetric multiserver multiqueue systems with one-buffer queues and Markovian interarrival, service, and walk times. Exact numerical results are obtained by means of Great-SPN, a software tool for the analysis of GSPNs, and are presented as curves of the average customer delay and waiting time. The results quantify the performance differences among the various polling orders, and show that the usual cyclic order can be the most effective, if the possibility of shorter walk times in the cyclic cases is adequately taken into account.>

Most of the medium-access control (MAC) sublayer protocols recently proposed for application in very high-speed local and metropolitan area networks (LANs and MANs) are based on a slotted transmission scheme. Slotting guarantees very good throughput efficiencies, but further gains are possible if slots can be freed after reaching their destination, thus being available for repeated use as they propagate in the network. The authors describe a simulation-based quantitative analysis of the performance gains obtained by introducing slot reuse in distributed-queue dual-bus (DQDB) and cyclic reservation multiaccess (CRMA) MANs. CRMA-II, the latest evolution of CRMA, naturally incorporates slot reuse in the MAC protocol operations and is considered in the study for comparison purposes. In the case of the standard DQDB protocol, some existing proposals are considered. In the case of CRMA, both a previous IBM proposal and a novel approach, leading to very good performances, are studied.>

Three regular meshed topologies are compared in light of their possible use for the implementation of large all-optical wavelength-routing communication networks (or interconnection systems). These systems provide all source-destination pairs with end-to-end transparent channels that are identified through a wavelength and a physical path. The considered topologies are the K-dimensional bidirectional square lattice, the twin shuffle, and the de Bruijn graph. The comparison is based on the maximum and average distance between source and destination (number of traversed nodes), on the degree of connectivity for each node (number of input and output fibers), and on the minimum number of wavelengths in the WDM comb necessary to discriminate all source-destination pairs.>

The class of Petri nets obtained by eliminating timing from generalized stochastic Petri net (GSPN) models while preserving the qualitative behavior is identified. Structural results for those nets are derived, obtaining the first structural analysis of Petri nets with priority and inhibitor arcs. A revision of the GSPN definition based on the structural properties of the models is presented. It is shown that for a (wide) class of nets, the definition of firing probabilities of conflicting immediate transitions does not require the information on reachable markings. Identification of the class of models for which the net-level specification is possible is also based on the structural analysis results. The procedure for the model specification is illustrated by means of an example. It is also shown that a net-level specification of the model associated with efficient structural analysis techniques can have a substantial impact on model analysis.>

The authors consider symmetric multiserver multiqueue systems in the cases of exhaustive and gated service disciplines, and present exact analytical results for the average server cycle and vacation times, as well as approximate closed-form expressions for the average customer waiting time, thus complementing the results obtained by the same authors (1990) for the one-limited service discipline. Arrival processes at each queue are assumed to be Poisson, with the same rate for all queues; service times and walk times are modeled with independent, identically distributed random variables with arbitrary distributions. The two cases in which at most one server or any number of servers can simultaneously attend a queue are considered.>

A study of multiserver multiqueue systems, i.e., queuing systems comprising a set of waiting lines where customers arrive to be served by a pool of servers, is described. Servers cyclically move from one queue to another, and provide service to at most one waiting customer per visit to a queue. Such (1-limited) multiserver polling models are analyzed. Attention is restricted to the case where the movement of the servers from queue to queue takes place in zero time. Two different server utilization policies are considered, and exact results are presented for the average customer waiting time and delay in the simpler case, while simple upper and lower bounds and a very accurate approximation are proposed for the more complex policy. In both cases systems are assumed to be symmetrical, arrivals are taken to be Poisson, and service times exponential.>

Simulation is used to estimate the performance of media access control (MAC) protocols derived from carrier-sense multiple access with collision detection (CSMA/CD), and operating in local area networks comprising several parallel broadcast channels. The influence of possible protocol and system alternatives on the network performance is discussed, based on results of the packet delay average, variance, mean square, coefficient of variation, and histogram, as well as the packet rejection probability due to lack of buffer space. The delay incurred by multipacket messages is estimated, comparing the single channel to the multichannel option. Numerical results indicate that the multichannel option provides reductions in both the packet delay average and variance, even when stations are only able to simultaneously receive from a subset of channels.>

An analysis of 1-persistent carrier-sense multiple-access (CSMA) protocols (possibly with collision detection (CD)) operating in fixed-length time intervals is presented, and closed-form results for the transient protocol throughput and average packet delay are derived. Such results provide a basis for the performance evaluation of integrated local area networks where the 1-persistent CSMA(-CD) protocol is used only in a portion of a fixed-length frame whose remaining part is devoted to the transmission of isochronous traffic. Numerical results are given which provide insight into the transient behavior of 1-persistent CSMA and CSMA-CD protocols.>

An analysis of polling schemes with multiple servers is presented where the service discipline at each station is assumed to be of the nonexhaustive (limited) type, i.e. no more than one customer can be removed from a station per visit of a server. The times spent by servers to walk from station to station are assumed to be nonzero and are modeled by independent, identically distributed random variables. The arrival processes at each queue are assumed to be Poisson, and serving times are modeled with independent, identically distributed random variables with arbitrary PDF. The case in which at most one server and the case in which any number of servers can simultaneously attend a queue are separately considered. Under the assumption of balanced traffic (equal arrival rates at all stations), several results concerning the cycle analysis and the stability of such schemes are obtained. A simple, explicit expression that gives an approximation for the steady-state average customer waiting time is derived.>

TOPNET is a software package for the simulation of communication networks that is being developed with the aim of exploiting as much as possible the visual aspects of a simulation experiment. The communication network topology and architecture are described by drawings, the system dynamics are represented with a class of timed Petri nets, the simulation experiment is controlled through menus and buttons activated with a mouse, and results are graphically displayed on the screen. With the help of an example, the main characteristics of the user interface and the internal architecture of TOPNET are described.>

The authors present the results of a design study of the receiver in a digital transmission system using the combined coding and modulation schemes known as Ungerboeck codes. Specifically, they examine the design of the receiver for encoded 16-PSK (phase shift keying) modulation, presenting first the traditional structure for the optimum receiver and then a simpler structure. The decoding depth of the Viterbi algorithm, the quantization of the metrics inside the Viterbi processor, and the phase jitter in the recovered carrier are considered. The impact of branch and path metric quantization inside the receiver is discussed, showing that a reasonable number of bits (8) is sufficient to obtain nearly optimum performance when the code complexity is limited. The effect of imperfect carrier recovery inside the receiver is studied, providing accurate analytical estimates of the error event probability as well as an upper bound to the symbol error probability. Results of a detailed simulation, including carrier and bit timing recovery blocks, show that the effects of imperfections on the bit error probability are very small, even at low signal-to-noise ratios. On the whole, results show the robustness of the Viterbi algorithm with respect to fairly rough quantizations of the metrics and indicate that carrier recovery is not as critical as expected.>

Petri nets in which random delays are associated with atomic transitions are defined in a comprehensive framework that contains most of the models already proposed in the literature. To include generally distributed firing times into the model one must specify the way in which the next transition to fire is chosen, and how the model keeps track of its past history; this set of specifications is called an execution policy. A discussion is presented of the impact that different execution policies have on semantics of the mode, as well as the characteristics of the stochastic process associated with each of these policies. When the execution policy is completely specified by the transition with the minimum delay (race policy) and the firing distributions are of the phase type, an algorithm is provided that automatically converts the stochastic process into a continuous time homogeneous Markov chain. An execution policy based on the choice of the next transition to fire independently of the associated delay (preselection policy) is introduced, and its semantics is discussed together with possible implementation strategies.>

Recursive relations are derived for the exact computation of the steady-state probability distribution of some queuing models with passive resources that can be used to analyze the performance of multiple-bus multiprocessor system architectures. The most general case that can be shown to admit a product-form solution is described, and a recursive solution is obtained taking into account, considering different processor access rates, different memory selection probabilities, and a first-come-first-served bus scheduling policy. Several simpler cases allowing easier model solutions are also considered. Numerical evaluations for large computing systems with nonuniform memory references show the usefulness of the results.>

The theoretical aspects of the encoding process are investigated, resulting in a precise definition of linear codes together with theorems that clarify how they can be obtained. A particular subset of linear codes, called superlinear codes, for which the performance analysis is highly simplified is identified. The most relevant performance measures for the analysis of this class of codes are discussed. The minimum Euclidean distance and the event and bit error probabilities are found analytically using the uniform error property (when applicable) or variations on it. This yields accurate upper and lower bounds to the error rate at the price of reasonable computational complexity. The theory is then applied to the search for 'good' codes and to their performance evaluation. The cases of 16- and 32-PSK codes, which are good candidates for use in digital satellite transmission, are considered. Several new results in terms of error event and bit error probabilities are presented, showing considerable gains in terms of SNR with respect to the uncoded case.>

A high-level Petri net model of the software architecture of an experimental MIMD multiprocessor system for Artificial Intelligence applications is derived by direct translation of the code corresponding to the assumed workload. Hardware architectural constraints are then easily added, and formal reduction rules are used to simplify the model, which is then further approximated to obtain a performance model of the system based on generalized stochastic Petri nets. From the latter model it is possible to estimate the optimal multiprogramming level of each processor so as to achieve the maximum performance in terms of overall throughput (number of tasks completed per unit time).

The performance of combined coding and modulation schemes in channels with intersymbol interference and nonlinearities is studied using an analytical approach. The class of possible receivers is presented, and a generalization of the minimum Euclidean distance used for the asymptotic analysis of the performance of combined coding and modulation schemes on AWGN channels is introduced. Numerical results are presented for coded 16-PSK modulation.

TOPSIM III is a flexible, easy to use, stand-alone Fortran package for the digital simulation of communication systems in the time domain. TOPSIM III provides a simple high-level language for the description of the system. Programs can be written following the same structure used in drawing the system block diagram. The paper discusses the time domain simulation approach used in the design of the package and describes its capabilities. The contents of the subprogram libraries are presented, describing some of the building blocks that can be used in the simulation as well as the performance evaluation facilities available in the package. Some simple examples illustrate the use of TOPSIM III as a tool for analysis and design of digital communication systems.

Generalized Stochastic Petri Nets (GSPNs) are presented and are applied to the performance evaluation of multiprocessor systems. GSPNs are derived from standard Petri nets by partitioning the set of transitions into two subsets comprising timed and immediate transitions. An exponentially distributed random firing time is associated with each timed transition, whereas immediate transitions fire in zero time. It is shown that GSPNs are equivalent to continuous-time stochastic processes, and solution methods for the derivation of the steady state probability distribution are presented. Examples of application of GSPN models to the performance evaluation of multiprocessor systems show the usefulness and the effectiveness of this modeling tool. 1.

Graph models have been proposed by many authors as a useful tool for the analysis of peculiar features of computer systems such as concurrency, synchronization, communication, and cooperation among subsystems. Much of the work in this field is related to the original ideas developed by C. A. Petri. These graph models are today generally known as Petri Nets (PNs).

Multiple-access protocols suitable for packet switching local area networks that use several parallel broadcast channels are presented and analyzed by evaluating their throughput and delay characteristics. The local network architecture is first described, then the network performances are evaluated considering a variety of multiple-access schemes that are derived from CSMA and CSMA-CD protocols, by extending the original single channel schemes to a multiple channel system. It is shown that significant performance improvements are achievable with the multiple channel option. Further advantages obtained with the proposed network architecture are a higher reliability and fault tolerance, and a modular network design that allows a gradual system growth following users' demands.

Stochastic models of contention for shared resources in an experimental multiprocessor prototype are presented and are validated with simulation and measurement results. Three modeling techniques are used (stochastic Petri nets, Markov chains, and queueing networks) that represent the system operations as Markovian stochastic processes. Each technique is best suited to a specific stage of the analysis. An integrated use of these techniques represents a very powerful tool for the performance analysis of multiprocessor systems and provides ways of investigating several extensions of the prototype architecture. Simulation results and measurements performed on the hardware prototype validate the analysis and show that the accuracy of the analytical results is excellent.

Markovian models are developed for the performance analysis and comparison of several single bus multiprocessor architectures. Processors are assumed to cooperate in a message passing fashion, and messages are exchanged through common memory areas. Four architectures are considered in this paper which differ in the location of the common memory modules. Contention for shared resources is modeled and the corresponding efficiency loss is studied. Numerical results are obtained for the processing power of each architecture, introducing simplifying assumptions that allow a compact Markovian system description.

Markovian models are developed for the performance analysis of multiprocessor systems intercommunicating via a set of buses. The performance index is the average number of active processors, called processing power. From processing power a variety of other performance measures can be derived as dictated by the specific processor application. Exact models are first introduced and are illustrated with a simple example. The computational complexity of the exact models is shown to increase very rapidly with system size, thus making the exact analysis impractical even for medium size systems. To overcome the complexity of computation, several approximate models are introduced. The approximate results are compared with the exact ones and found to be surprisingly accurate for a wide range of configurations. Simulation is used to validate the analytic models and to test their robustness.