In numerous settings, agents lack sufficient data to learn a model directly. Collaborating with other agents may help, but introduces a bias-variance trade-off when local data distributions differ. A key challenge is for each agent to identify clients with similar distributions while learning the model, a problem that remains largely unresolved. This study focuses on a particular instance of the overarching problem, where each agent collects samples from a real-valued distribution over time to estimate its mean. Existing algorithms face impractical per-agent space and time complexities (linear in the number of agents |A|). To address scalability challenges, we propose a framework where agents self-organize into a graph, allowing each agent to communicate with only a selected number of peers r. We propose two collaborative mean estimation algorithms: one employs a consensus-based approach, while the other uses a message-passing scheme, with complexity O(r) and O(r log |A|), respectively. We establish conditions for both algorithms to yield asymptotically optimal estimates and we provide a theoretical characterization of their performance.

The advent of Large Language Models (LLMs) and generative AI is fundamentally transforming information retrieval and processing on the Internet, bringing both great potential and significant concerns regarding content authenticity and reliability.This paper presents a novel quantitative approach to shed light on the complex information dynamics arising from the growing use of generative AI tools.Despite their significant impact on the digital ecosystem, these dynamics remain largely uncharted and poorly understood.We propose a stochastic model to characterize the generation, indexing, and dissemination of information in response to new topics.This scenario particularly challenges current LLMs, which often rely on real-time Retrieval-Augmented Generation (RAG) techniques to overcome their static knowledge limitations.Our findings suggest that the rapid pace of generative AI adoption, combined with increasing user reliance, can outpace human verification, escalating the risk of inaccurate information proliferation across digital resources.An in-depth analysis of Stack Exchange data confirms that high-quality answers inevitably require substantial time and human effort to emerge.This underscores the considerable risks associated with generating persuasive text in response to new questions and highlights the critical need for responsible development and deployment of future generative AI tools.

In Federated Learning (FL), devices– also referred to as clients– can exhibit heterogeneous availability patterns, often correlated over time and with other clients. This paper addresses the problem of heterogeneous and correlated client availability in FL. Our theoretical analysis is the first to demonstrate the negative impact of correlation on FL algorithms’ convergence rate and highlights a trade-off between optimization error (related to convergence speed) and bias error (indicative of model quality). To optimize this trade-off, we propose Correlation-Aware FL (CA-Fed), a novel algorithm that dynamically balances the competing objectives of fast convergence and minimal model bias.CA-Fedachieves this by dynamically adjusting the aggregation weight assigned to each client and selectively excluding clients with high temporal correlation and low availability. Experimental evaluations on diverse datasets demonstrate the effectiveness ofCA-Fedcompared to state-of-the-art methods. Specifically,CA-Fedachieves the best trade-off between training time and test accuracy. By dynamically handling clients with high temporal correlation and low availability,CA-Fedemerges as a promising solution to mitigate the detrimental impact of correlated client availability in FL.

Recently, Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising as a possible solution to balance user’s privacy and advertisement effectiveness. Using the Topics API, the browser builds a user profile based on navigation history, which advertisers can access. The Topics API aim at becoming the new standard for behavioural advertising, thus it is necessary to fully understand its operation and find possible limitations. In this article, we evaluate the robustness of the Topics API to a re-identification attack. To build a user profile, we suppose an attacker accumulates over time the topics a user exposes to different websites. The attacker later re-identifies the same user matching the profiles of their audience. We leverage real traffic traces and realistic population models, and we present increasingly powerful attack threats. We find that the Topics API mitigates but cannot prevent re-identification from taking place, as there is a sizeable chance that a user’s profile remains unique within a website’s audience and the attacker successfully matches it with the profile of the same user on a second website. Depending on environmental factors, the probability of correct re-identification can reach 50%, considering a pool of 1,000 users. We offer the code and data we use in this work to stimulate further studies and the tuning of the Topic API parameters. 1

The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm’s convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.

Web tracking through third-party cookies is considered a threat to users' privacy and is supposed to be abandoned in the near future. Recently, Google proposed the Topics API framework as a privacy-friendly alternative for behavioural advertising. Using this approach, the browser builds a user profile based on navigation history, which advertisers can access. The Topics API has the possibility of becoming the new standard for behavioural advertising, thus it is necessary to fully understand its operation and find possible limitations. This paper evaluates the robustness of the Topics API to a re-identification attack where an attacker reconstructs the user profile by accumulating user's exposed topics over time to later re-identify the same user on a different website. Using real traffic traces and realistic population models, we find that the Topics API mitigates but cannot prevent re-identification to take place, as there is a sizeable chance that a user's profile is unique within a website's audience. Consequently, the probability of correct re-identification can reach 15-17%, considering a pool of 1,000 users. We offer the code and data we use in this work to stimulate further studies and the tuning of the Topic API parameters.

A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. This is for example the case of 360° videos where user’s head orientation—expressed in spherical coordinates—determines what part of the video needs to be retrieved, or of recommendation systems where a metric learning technique is used to embed the objects in a finite dimensional space with an opportune distance to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and${q}$LRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose GRADES, a new similarity caching policy that uses gradient descent to navigate the continuous space and find appropriate objects to store in the cache. We provide theoretical convergence guarantees and show GRADES increases the similarity of the objects served by the cache in both applications mentioned above.

This paper focuses on similarity caching systems, in which a user request for an object$o$that is not in the cache can be (partially) satisfied by a similar stored object$o'$, at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

A relevant fraction of human interactions occurs on online social networks. Freshness of content seems to play an important role, with content popularity rapidly vanishing over time. In this paper, we investigate how influencers' generated content (i.e., posts) attracts interactions, measured by number of likes or reactions. We analyse the activity of Italian influencers and followers over more than 5 years, focusing on two popular social networks: Facebook and Instagram, including more than 13 billion interactions and about 4 million posts. We characterise the influencers' and followers' behaviour over time, show that influencers' posts are short-lived with an exponential temporal decay, and characterise the time evolution of the interactions from their initial peak till the end of a post lifetime. Finally, leveraging our findings, we discuss how they can be exploited to develop an analytical model of the interactions temporal dynamics.

A similarity cache can reply to a query for an object with similar objects stored locally. In some applications of similarity caches, queries and objects are naturally represented as points in a continuous space. Examples include 360° videos where user's head orientation-expressed in spherical coordinates- determines what part of the video needs to be retrieved, and recommendation systems where the objects are embedded in a finite-dimensional space with a distance metric to capture content dissimilarity. Existing similarity caching policies are simple modifications of classic policies like LRU, LFU, and qLRU and ignore the continuous nature of the space where objects are embedded. In this paper, we propose Grades, a new similarity caching policy that uses gradient descent to navigate the continuous space and find the optimal objects to store in the cache. We provide theoretical convergence guarantees and show Grades increases the similarity of the objects served by the cache in both applications mentioned above.

We consider a dense cellular network, in which a limited-size cache is available at every base station (BS). Coordinating content allocation across the different caches can lead to significant performance gains, but is a difficult problem even when full information about the network and the request process is available. In this paper we present $q$ LRU- $\Delta $ , a general-purpose online caching policy that can be tailored to optimize different performance metrics also in presence of coordinated multipoint transmission techniques. The policy requires neither direct communication among BSs, nor a priori knowledge of content popularity and, under stationary request processes, has provable performance guarantees.

This paper focuses on similarity caching systems, in which a user request for an object o that is not in the cache can be (partially) satisfied by a similar stored object o', at the cost of a loss of user utility. Similarity caching systems can be effectively employed in several application areas, like multimedia retrieval, recommender systems, genome study, and machine learning training/serving. However, despite their relevance, the behavior of such systems is far from being well understood. In this paper, we provide a first comprehensive analysis of similarity caching in the offline, adversarial, and stochastic settings. We show that similarity caching raises significant new challenges, for which we propose the first dynamic policies with some optimality guarantees. We evaluate the performance of our schemes under both synthetic and real request traces.

While the cost of the access network could be considerably reduced by the use of caching, this is not currently happening because content providers (CPs), who alone have the detailed demand data required for optimal content placement, have no natural incentive to use them to minimize access network operator (ANO) expenditure. We argue that ANOs should therefore provide such an incentive in the form of direct subsidies paid to the CPs in proportion to the realized savings. We apply coalition game theory to design the required subsidy framework and propose a distributed algorithm, based on Lagrangian decomposition, allowing ANOs and CPs to collectively realize the optimal memory for bandwidth tradeoff. The considered access network is a cache hierarchy with per-CP central office caches, accessed by all ANOs, at the apex, and per-ANO dedicated bandwidth and storage resources at the lower levels, including wireless base stations, that must be shared by multiple CPs.

Sharding is a method for allocating data items to nodes of a distributed caching or storage system based on the result of a hash function computed on the item's identifier. It is ubiquitously used in key-value stores, CDNs and many other applications. Despite considerable work that has focused on the design and implementation of such systems, there is limited understanding of their performance in realistic operational conditions from a theoretical standpoint. In this paper we fill this gap by providing a thorough modeling of sharded caching systems, focusing particularly on load balancing and caching performance aspects. Our analysis provides important insights that can be applied to optimize the design and configuration of sharded caching systems.

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT) which aggregates requests for a given content that arrive within the download delay. We extend prior work on caching with non-zero download delay by proposing a novel mathematical framework that is applicable to general traffic models and alternative cache insertion policies. Specifically we consider the impact of time locality in demand due to finite content lifetimes and we evaluate the use of an LRU filter to improve CS hit rate performance. The analysis is used to demonstrate that the impact of the PIT on upstream bandwidth reduction is significant only for relatively small content catalogues or high average request rate per content. We also show that the filter can be counterproductive when contents have finite lifetimes and traffic intensity is low.

We focus on a dense cellular network, in which a limited-size cache is available at every base station (BS). In order to optimize the overall performance of the system in such scenario, where a significant fraction of the users is covered by several BSs, a tight coordination among nearby caches is needed. To this end, this paper introduces a class of simple and fully distributed caching policies, which require neither direct communication among BSs nor a priori knowledge of content popularity. Furthermore, we propose a novel approximate analytical methodology to assess the performance of interacting caches under such policies. Our approach builds upon the well-known characteristic time approximation [1] and provides predictions that are surprisingly accurate (hardly distinguishable from the simulations) in most of the scenarios. Both synthetic and trace-driven results show that our caching policies achieve an excellent performance (in some cases provably optimal). They outperform state-of-the-art dynamic policies for interacting caches, and, in some cases, also the greedy content placement, which is known to be the best performing polynomial algorithm under static and perfectly known content popularity profiles.

In this paper, we propose a methodology for the study of general cache networks, which is intrinsically scalable and amenable to parallel execution. We contrast two techniques: one that slices the network and another that slices the content catalog. In the former, each core simulates requests for the whole catalog on a subgraph of the original topology, whereas in the latter each core simulates requests for a portion of the original catalog on a replica of the whole network. Interestingly, we find out that when the number of cores increases (and so the split ratio of the network topology), the overhead of message passing required to keeping consistency among nodes actually offsets any benefit from the parallelization: this is strictly due to the correlation among neighboring caches, meaning that requests arriving at one cache allocated on one core may depend on the status of one or more caches allocated on different cores. Even more interestingly, we find out that the newly proposed catalog slicing, on the contrary, achieves an ideal speedup in the number of cores. Overall, our system, which we make available as open source software, enables performance assessment of large-scale general cache networks, i.e., comprising hundreds of nodes, trillions contents, and complex routing and caching algorithms, in minutes of CPU time and with exiguous amounts of memory.

Online social networks offer the opportunity to collect a huge amount of valuable information about billions of users. The analysis of this data by service providers and unintended third parties are posing serious treats to user privacy. In particular, recent work has shown that users participating in more than one online social network can be identified based only on the structure of their links to other users. An effective tool to de-anonymize social network users is represented by graph matching algorithms. Indeed, by exploiting a sufficiently large set of seed nodes, a percolation process can correctly match almost all nodes across the different social networks. In this article, we show the crucial role of clustering, which is a relevant feature of social network graphs (and many other systems). Clustering has both the effect of making matching algorithms more prone to errors, and the potential to greatly reduce the number of seeds needed to trigger percolation. We show these facts by considering a fairly general class of random geometric graphs with variable clustering level. We assume that seeds can be identified in particular sub-regions of the network graph, while no a priori knowledge about the location of the other nodes is required. Under these conditions, we show how clever algorithms can achieve surprisingly good performance while limiting the number of matching errors.

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.

Bootstrap percolation is a well-known activation process in a graph, in which a node becomes active when it has at least r active neighbors. Such process, originally studied on regular structures, has been recently investigated also in the context of random graphs, where it can serve as a simple model for a wide variety of cascades, such as the spreading of ideas, trends, viral contents, etc. over large social networks. In particular, it has been shown that in G(n,p) the final active set can exhibit a phase transition for a sub-linear number of seeds. In this paper, we propose a unique framework to study similar sub-linear phase transitions for a much broader class of graph models and epidemic processes. Specifically, we consider i) a generalized version of bootstrap percolation in G(n,p) with random activation thresholds and random node-to-node influences; ii) different random graph models, including graphs with given degree sequence and graphs with community structure (block model). The common thread of our work is to show the surprising sensitivity of the critical seed set size to extreme values of distributions, which makes some systems dramatically vulnerable to large-scale outbreaks. We validate our results running simulation on both synthetic and real graphs.

We analyze throughput-delay scaling laws of mobile ad hoc networks under a content-centric traffic scenario, where users are mainly interested in retrieving contents cached by other nodes. We assume limited buffer size available at each node and Zipf-like content popularity. We consider nodes uniformly visiting the network area according to a random-walk mobility model, whose flight size varies from the typical distance among the nodes (quasi-static case) up to the edge length of the network area (reshuffling mobility model). Our main findings are: (1) the best throughput-delay tradeoffs are achieved in the quasi-static case: increasing the mobility degree of nodes leads to worse and worse performance; (ii) the best throughput-delay tradeoffs can be recovered by power control (i.e., by adapting the transmission range to the content) even in the complete reshuffling case.

We tackle the problem of user de-anonymization in social networks characterized by scale-free relationships between users. The network is modeled as a graph capturing the impact of power-law node degree distribution, which is a fundamental and quite common feature of social networks. Using this model, we present a de-anonymization algorithm that exploits an initial set of users, called seeds, that are known a priori. By employing the bootstrap percolation theory and a novel graph slicing technique, we develop a rigorous analysis of the proposed algorithm under asymptotic conditions. Our analysis shows that large inhomogeneities in the node degree lead to a dramatic reduction in the size of the seed set that is necessary to successfully identify all the other users. We characterize this set size when seeds are properly selected based on the node degree as well as when seeds are uniformly distributed. We prove that, given n nodes, the number of seeds required for network de-anonymization can be as small as n∈, for any small ∈ > 0. In addition, we discuss the complexity of our de-anonymization algorithm and validate our results through numerical experiments on a real social network graph.

We address the problem of social network de-anonymization when relationships between people are described by scale-free graphs. In particular, we propose a rigorous, asymptotic mathematical analysis of the network de-anonymization problem while capturing the impact of power-law node degree distribution, which is a fundamental and quite ubiquitous feature of many complex systems such as social networks. By applying bootstrap percolation and a novel graph slicing technique, we prove that large inhomogeneities in the node degree lead to a dramatic reduction of the initial set of nodes that must be known a priori (the seeds) in order to successfully identify all other users. We characterize the size of this set when seeds are selected using different criteria, and we show that their number can be as small as n% for any small ε > 0. Our results are validated through simulation experiments on real social network graphs.

In this paper we develop a novel technique to analyze both isolated and interconnected caches operating under different caching strategies and realistic traffic conditions. The main strength of our approach is the ability to consider dynamic contents which are constantly added into the system catalogue, and whose popularity evolves over time according to desired profiles. We do so while preserving the simplicity and computational efficiency of models developed under stationary popularity conditions, which are needed to analyze several caching strategies. Our main achievement is to show that the impact of content popularity dynamics on cache performance can be effectively captured into an analytical model based on a fixed content catalogue (i.e., a catalogue whose size and objects' popularity do not change over time).

In this paper we develop an analytical framework, based on Che's approximation [2], for the analysis of Least Recently Used (LRU) caches operating under the Shot Noise requests Model (SNM). The SNM was recently proposed in [10] to better capture the main characteristics of today Video on Demand (Vod) traffic. In this context, Che's approximation is derived as the application of a mean field principle to the cache eviction time. We investigate the validity of this approximation through an asymptotic analysis of the cache eviction time. Particularly, we provide a large deviation principle and a central limit theorem for the cache eviction time, as the cache size grows large. Furthermore, we obtain a non-asymptotic analytical upper bound on the error entailed by Che's approximation of the hit probability.

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.

To assess the performance of caching systems, the definition of a proper process describing the content requests generated by users is required. Starting from the analysis of traces of YouTube video requests collected inside operational networks, we identify the characteristics of real traffic that need to be represented and those that instead can be safely neglected. Based on our observations, we introduce a simple, parsimonious traffic model, named shot noise model (SNM), that allows us to capture temporal and geographical locality of content popularity. The SNM is sufficiently simple to be effectively employed in both analytical and scalable simulative studies of caching systems. We demonstrate this by analytically characterizing the performance of the LRU caching policy under the SNM, for both a single cache and a network of caches. With respect to the standard independent reference model (IRM), some paradigmatic shifts, concerning the impact of various traffic characteristics on cache performance, clearly emerge from our results.

We propose an analytical framework to tightly characterize the scaling laws for the additional bandwidth that servers must supply to guarantee perfect service in peer-assisted Video-on-Demand systems, taking into account essential aspects such as peer churn, bandwidth heterogeneity, and Zipf-like video popularity. Our results reveal that the catalog size and the content popularity distribution have a huge effect on the system performance. We show that users' cooperation can effectively reduce the servers' burden for a wide range of system parameters, confirming to be an attractive solution to limit the costs incurred by content providers as the system scales to large populations of users.

Peer-to-peer live-streaming (P2P-TV) systems' goal is disseminating real-time video content using peer-to-peer technology. Their performance is driven by the overlay topology, i.e., the virtual topology that peers use to exchange video chunks. Several proposals have been made in the past to optimize it, yet few experimental studies have corroborated results. The aim of this paper is to provide a comprehensive experimental comparison based on PeerStreamer in order to benchmark different strategies for the construction and maintenance of the overlay topology in P2P-TV systems. We present only experimental results in which fully distributed strategies are evaluated in both controlled experiments and the Internet using thousands of peers. Results confirm that the topological properties of the overlay have a deep impact on both user quality of experience and network load. Strategies based solely on random peer selection are greatly outperformed by smart yet simple and actually implementable strategies. The most performing strategy we devise guarantees to deliver almost all chunks to all peers with a playout delay as low as 6 s even when system load approaches 1, and in almost adversarial network scenarios. PeerStreamer is open-source to make results reproducible and allow further research by the community.

We propose a unified methodology to analyse the performance of caches (both isolated and interconnected), by extending and generalizing a decoupling technique originally known as Che's approximation, which provides very accurate results at low computational cost. We consider several caching policies, taking into account the effects of temporal locality. In the case of interconnected caches, our approach allows us to do better than the Poisson approximation commonly adopted in prior work. Our results, validated against simulations and trace-driven experiments, provide interesting insights into the performance of caching systems.

One of the main research directions along which the future Internet is evolving can be identified in the paradigmatic shift from a network of hosts toward a network of caches. Yet, several questions remain concerning the scalability of individual algorithms (e.g., name based lookup and routing) and components (e.g., caches) of these novel Information Centric Networking (ICN) architectures. Exploiting a peculiar characteristics of ICN (i.e., the fact that contents are split in chunks), and the nature of video streaming (which dominates Internet traffic), this paper proposes a novel two-layers caching scheme that allows multi-Terabyte caches to sustain content streaming at multi-Gbps speed. We model the system as an extension, to the case of chunked contents, of the well known Che approximation, that has the advantage of being very simple and accurate at the same time. Simulations under synthetic and realistic trace-driven traffic confirm the accuracy of the analysis and the feasibility of the proposed architecture.

We consider extended wireless networks characterized by a random topology of access points (APs) contending for medium access over the same wireless channel. Recently, stochastic geometry has emerged as a powerful tool to analyze random networks adopting MAC protocols such as ALOHA and CSMA. The main strength of this methodology lies in its ability to account for the randomness in the nodes' location jointly with an accurate description at the physical layer, based on the SINR, that allows considering also random fading on each link. In this paper, we extend previous stochastic geometry models of CSMA networks, developing computationally efficient techniques to obtain throughput distributions, in addition to spatial averages, which permit us to get interesting insights into the impact of protocol parameters and channel variability on the spatial fairness among the nodes. Moreover, we extend the analysis to a significant class of topologies in which APs are not placed according to a Poisson process.

We analyze a peer-assisted Video-on-Demand (VoD) system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case, as well as in controlled nonsequential swarms. Our approach is able to capture several stochastic effects related to peer churn, upload bandwidth heterogeneity, and nonstationary traffic conditions, which have not been documented or analyzed before. Finally, we provide important hints for the design of efficient peer-assisted VoD systems under server capacity constraints.

We analyze throughput-delay scaling laws of mobile ad-hoc networks under a content-centric traffic scenario, where users are mainly interested in retrieving contents cached by other nodes. We assume limited buffer size available at each node and Zipf-like content popularity. We consider nodes uniformly visiting the network area according to a random-walk mobility model, whose flight size is varied from the typical distance among the nodes (quasi-static case) up to the edge length of the network area (reshuffling mobility model). Our main findings are i) the best throughput-delay trade-offs are achieved in the quasi-static case: increasing the mobility degree of nodes leads to worse and worse performance; ii) the best throughput-delay trade-offs can be recovered by power control (i.e., by adapting the transmission range to the content) even in the complete reshuffling case.

We propose an analytical framework to tightly characterize the scaling laws for the additional bandwidth that servers must supply to guarantee perfect service in peer-assisted Video-on-Demand systems, taking into account essential aspects such as peer churn, bandwidth heterogeneity, and Zipf-like video popularity. Our results reveal that the catalog size and the content popularity distribution have a huge effect on the system performance. We show that users' cooperation can effectively reduce the servers' burden for a wide range of system parameters, confirming to be an attractive solution to limit the costs incurred by content providers as the system scales to large populations of users.

We consider a communication system in which a given digital content has to be delivered sequentially at constant rate to a set of users who asynchronously request it according to a Poisson process. Users can retrieve data: 1) from one or more sources that statically store the entire content; and 2) from users who have previously requested the content, and contribute (for limited time) a random amount of upload bandwidth to the system. We propose a stochastic fluid framework that allows characterizing the aggregate streaming rate necessary at the sources to satisfy all active requests. In particular, we establish the conditions under which the system becomes asymptotically scalable as the number of users grows. Our theoretical results apply to increasingly popular video-on-demand systems exploiting users' cooperation.

Interference among simultaneous transmissions represents the main limitation factor for the capacity and connectivity of dense wireless networks. In this paper, we provide efficient simulation laws for the tail of the interference in a simple wireless ad hoc network model. Particularly, we consider node locations distributed according to a Poisson point process and various classes of light-tailed fading distributions.

We consider a peer-assisted Video-on-demand system, in which video distribution is supported both by peers caching the whole video and by peers concurrently downloading it. We propose a stochastic fluid framework that allows to characterize the additional bandwidth requested from the servers to satisfy all users watching a given video. We obtain analytical upper bounds to the server bandwidth needed in the case in which users download the video content sequentially. We also present a methodology to obtain exact solutions for special cases of peer upload bandwidth distribution. Our bounds permit to tightly characterize the performance of peer-assisted VoD systems as the number of users increases, for both sequential and non-sequential delivery schemes. In particular, we rigorously prove that the simple sequential scheme is asymptotically optimal both in the bandwidth surplus and in the bandwidth deficit mode, and that peer-assisted systems become totally self-sustaining in the surplus mode as the number of users grows large.

We analyze a peer-assisted Video-on-Demand system in which users contribute their upload bandwidth to the redistribution of a video that they are downloading or that they have cached locally. Our target is to characterize the additional bandwidth that servers must supply to immediately satisfy all requests to watch a given video. We develop an approximate fluid model to compute the required server bandwidth in the sequential delivery case. Our approach is able to capture several stochastic effects related to peer churn, upload bandwidth heterogeneity, non-stationary traffic conditions, which have not been documented or analyzed before. We provide an analytical methodology to design efficient peer-assisted VoD systems and optimal resource allocation strategies.

P2P-TV systems performance are driven by the overlay topology that peers form. Several proposals have been made in the past to optimize it, yet little experimental studies have corroborated results. The aim of this work is to provide a comprehensive experimental comparison of different strategies for the construction and maintenance of the overlay topology in P2P-TV systems. To this goal, we have implemented different fully-distributed strategies in a P2P-TV application, called Peer-Streamer, that we use to run extensive experimental campaigns in a completely controlled set-up which involves thousands of peers, spanning very different networking scenarios. Results show that the topological properties of the overlay have a deep impact on both user quality of experience and network load. Strategies based solely on random peer selection are greatly outperformed by smart, yet simple strategies that can be implemented with negligible overhead. Even with different and complex scenarios, the neighborhood filtering strategy we devised as most performing guarantees to deliver almost all chunks to all peers with a play-out delay as low as only 6s even with system loads close to 1.0. Results are confirmed by running experiments on PlanetLab. PeerStreamer is open-source to make results reproducible and allow further research by the community.

Stochastic geometry proves to be a powerful tool for modeling dense wireless networks adopting random MAC protocols such as ALOHA and CSMA. The main strength of this methodology lies in its ability to account for the randomness in the nodes' location jointly with an accurate description at the physical layer, based on the SINR, that allows to consider also random fading on each link. Existing models of CSMA networks adopting the stochastic geometry approach suffer from two important weaknesses: 1) they permit to evaluate only spatial averages of the main performance measures, thus hiding possibly huge discrepancies in the performance achieved by individual nodes; 2) they are analytically tractable only when nodes are distributed over the area according to simple spatial processes (e.g., the Poisson point process). In this paper we show how the stochastic geometry approach can be extended to overcome the above limitations, allowing to obtain node throughput distributions as well as to analyze a significant class of topologies in which nodes are not independently placed.

In this paper we consider mesh based P2P streaming systems focusing on the problem of regulating peer upload rate to match the system demand while not overloading each peer upload link capacity. We propose Hose Rate Control (HRC), a novel scheme to control the speed at which peers offer chunks to other peers, ultimately controlling peer uplink capacity utilization. This is of critical importance for heterogeneous scenarios like the one faced in the Internet, where peer upload capacity is unknown and varies widely. HRC nicely adapts to the actual peer available upload bandwidth and system demand, so that users' Quality of Experience is greatly enhanced. Both simulations and actual experiments involving up to 1000 peers are presented to assess performance in real scenarios. Results show that HRC consistently outperforms the Quality of Experience achieved by non-adaptive schemes.

In this paper, we investigate the impact of peer bandwidth heterogeneity on the performance of a mesh-based P2P system for live streaming. We show that bandwidth heterogeneity constitutes an important resource for P2P live streaming systems. Indeed, by effectively exploiting it, the overall performance of the system is significantly improved. This requires the adoption of smart schemes for both the overlay topology construction and chunk scheduling mechanisms that discriminate among peers based on their bandwidth.

We analyze the capacity scaling laws of clustered ad hoc networks comprising significant inhomogeneities in the node spatial distribution over the area. In particular, we consider the class of networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process, which allows to model a wide variety of inhomogeneous topologies. For this class of networks, we derive information theoretic upper-bounds to the capacity, identifying six operational regions. We also provide constructive lower bounds by devising, for each region, an optimal communication strategy to achieve the maximum network throughput. The performance of our communication schemes match, in terms of scaling exponent, the theoretical upper-bounds.

The end-to-end available bandwidth of an Internet path is a desirable information that can be exploited to optimize system performance. Several tools have been proposed in the past to estimate it. However, existing measurement techniques were not designed for large-scale deployments. In this paper we show that current tools do not properly work where multiple probing processes share a portion of a path. We provide experimental evidence to quantify the impact of mutual interference between measurements. We further analyze the characteristics of popular tools, quantifying (i) the impact of mutual interference, (ii) the total overhead imposed to the network and (iii) the intrusiveness of the measurement process in a large-scale scenario. Our goal is to effectively quantify the impact of concurrent measurements on current estimation techniques and to offer some simple guidelines for dimensioning a large-scale measurement system.

We extend the analysis of the scaling laws of wireless ad hoc networks to the case of correlated nodes movements, which are commonly found in real mobility processes. We consider a simple version of the Reference Point Group Mobility model, in which nodes belonging to the same group are constrained to lie in a disc area, whose center moves uniformly across the network according to the i.i.d. model. We assume fast mobility conditions and take as a primary goal the maximization of per-node throughput. We discover that correlated node movements have a huge impact on asymptotic throughput and delay and can sometimes lead to better performance than the one achievable under independent nodes movements.

We consider large-scale mesh-based P2P systems for the distribution of real-time video content. Our goal is to study the impact that different design choices adopted while building the overlay topology may have on the system performance. In particular, we show that the adoption of different strategies leads to overlay topologies with different macroscopic properties. Representing the possible overlay topologies with different families of random graphs, we develop simple, yet accurate, fluid models that capture the dominant dynamics of the chunk distribution process over several families of random graphs. Our fluid models allow us to compare the performance of different strategies providing a guidance for the design of new and more efficient systems. In particular, we show that system performance can be significantly improved when possibly available information about peers location and/or peer access bandwidth is carefully exploited in the overlay topology formation process.

We extend the analysis of the scaling laws of wireless ad hoc networks to the case of correlated nodes movements, which are commonly found in real mobility processes. We consider a simple version of the Reference Point Group Mobility model, in which nodes belonging to the same group are constrained to lie in a disc area, whose center moves uniformly across the network according to the i.i.d. model. We assume fast mobility conditions, and take as primary goal the maximization of per-node throughput. We discover that correlated node movements have huge impact on asymptotic throughput and delay, and can sometimes lead to better performance than the one achievable under independent nodes movements.

We analyze the capacity scaling laws of clustered ad hoc networks in which nodes are distributed according to a doubly stochastic shot-noise Cox process. We identify five different operational regimes, and for each regime we devise a communication strategy that allows to achieve a throughput featuring the same scaling exponent as the maximum theoretical capacity.

P2P-TV systems have become part of the Internet landscape (See for instance http://www.pplive.com, http://www.soapcast.com, http://www.tvants.com, and many others). The architecture of these (normally proprietary) applications is generally receiver-driven, in that receivers actively search for suitable peers to download from, trying to maximize their performance. This results in very aggressive applications that generate huge and non optimized traffic loads. The demo summarized in this short paper shows the impact of various P2P streaming options and the efficiency of the Napa-Wine approach (compared to more traditional approaches) by running real streaming clients in realistic conditions. To make this comparison possible, the software developed in Napa-Wine is highly modular and configurable, allowing the user to test different topology management and chunk trading techniques developed within the Napa-Wine project, as well as to configure it to mimic other chunk/peer selection strategies known from literature.

This paper presents a systematic performance analysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: (1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; (2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; (3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support.

In this paper, we analyze asymptotic delay-throughput tradeoffs in mobile ad hoc networks comprising heterogeneous nodes with restricted mobility. We show that node spatial heterogeneity has the ability to drastically improve upon existing scaling laws established under the assumption that nodes are identical and uniformly visit the entire network area. In particular, we consider the situation in which each node moves around its own home-point according to a restricted mobility process which results into a spatial stationary distribution that decays as a power law of exponent δ with the distance from the home-point. For such restricted mobility model, we propose a novel class of scheduling and routing schemes, which significantly outperforms all delay-throughput results previously obtained in the case of identical nodes. In particular, for δ = 2 it is possible to achieve almost constant delay and almost constant per-node throughput (except for a polylogarithmic factor) as the number of nodes increases, even without resorting to sophisticated coding or signal processing techniques.

Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work, due to their closed and proprietary design. In this paper, we present large scale experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts. Our goal is to assess what level of "network awareness" has been embedded in the applications. We first define a general framework to quantify which network layer parameters leverage application choices, i.e., what parameters mainly drive the peer selection and data exchange. We then apply the methodology to a large dataset, collected during a number of experiments where we deployed about 40 peers in several European countries. From analysis of the dataset, we observe that TVAnts and PPLive exhibit a mild preference to exchange data among peers in the same autonomous system the peer belongs to, while this clustering effect is less intense in SopCast. However, no preference versus country, subnet or hop count is shown. Therefore, we believe that next-generation P2P live streaming applications definitively need to improve the level of network-awareness, so to better localize the traffic in the network and thus increase their network-friendliness as well.

We consider static ad hoc wireless networks comprising significant inhomogeneities in the node spatial distribution over the area and analyze the scaling laws of their transport capacity as the number of nodes increases. In particular, we consider nodes placed according to a shot-noise Cox process (SNCP), which allows to model the clustering behavior usually recognized in large-scale systems. For this class of networks, we propose novel scheduling and routing schemes that approach previously computed upper bounds to the per-flow throughput as the number of nodes tends to infinity.

This paper presents “Self-Chord,” a peer-to-peer (P2P) system that inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomous behavior, self-organization, and capacity to adapt to a changing environment. As opposed to the structured P2P systems deployed so far, resource indexing and placement is uncorrelated with network structure and topology, and resource keys are organized and managed by self-organizing mobile agents through simple local operations driven by probabilistic choices. Self-Chord has three main features that are particularly advantageous in Grid and Cloud Computing: 1) it is possible to give a semantic meaning to keys, which enables the execution of range queries; 2) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities; 3) maintenance load is also limited because it is not necessary to reassign keys when new peers or resources are added to the system-the mobile agents will spontaneously reorganize the keys. The efficiency and effectiveness of Self-Chord were assessed both with a simulation framework and with an analytical model inspired by fluid dynamics.

In this paper we define a simple experimental setup to analyze the behavior of commercial P2P-TV applications under adverse network conditions. Our goal is to reveal the ability of different P2P-TV applications to adapt to dynamically changing conditions, such as delay, loss and available capacity, e.g., checking whether such systems implement some form of congestion control. We apply our methodology to four popular commercial P2P-TV applications: PPLive, SOPCast, TVants and TVUPlayer. Our results show that all the considered applications are in general capable to cope with packet losses and to react to congestion arising in the network core. Indeed, all applications keep trying to download data by avoiding bad paths and carefully selecting good peers. However, when the bottleneck affects all peers, e.g., it is at the access link, their behavior results rather aggressive, and potentially harmful for both other applications and the network.

We consider static ad hoc wireless networks comprising significant inhomogeneities in the node spatial distribution over the area, and analyze the scaling laws of their transport capacity as the number of nodes increases. In particular, we consider nodes placed according to a shot-noise Cox process, which allows to model the clustering behavior usually recognized in large-scale systems. For this class of networks, we propose novel scheduling and routing schemes which approach previously computed upper bounds to the per-flow throughput as the number of nodes tends to infinity.

Early P2P-TV systems have already attracted millions of users, and many new commercial solutions are entering this market. Little information is however available about how these systems work. In this paper we present large scale sets of experiments to compare three of the most successful P2P-TV systems, namely PPLive, SopCast and TVAnts. Our goal is to assess what level of "network awareness" has been embedded in the applications, i.e., what parameters mainly drive the peer selection and data exchange. By using a general framework that can be extended to other systems and metrics, we show that all applications largely base their choices on the peer bandwidth, i.e., they prefer high-bandwidth users, which is rather intuitive. Moreover, TVAnts and PPLive exhibits also a preference to exchange data among peers in the same autonomous system the peer belongs to. However, no evidence about preference versus peers in the same subnet or that are closer to the considered peer emerges. We believe that next-generation P2P live streaming applications definitively need to improve the level of network-awareness, so to better localize the traffic in the network and thus increase their network-friendliness as well.

In this paper, we analyze asymptotic delay-throughput performance of mobile ad-hoc networks comprising heterogeneous nodes with restricted mobility. In particular, we consider a scenario in which each node moves around one or more home-points (in a finite number) randomly placed over the area. For such restricted mobility model, we propose a new class of scheduling and routing schemes, which significantly outperforms all delay-throughput results previously obtained.

In this paper, we propose a simple and fully distributed mechanism for constructing and maintaining the overlay topology in mesh-based P2P-TV systems. Our algorithm optimizes the topology to better exploit large bandwidth peers, so that they are automatically moved close to the source. This improves the chunk delivery delay so that all peers benefit, not just the high bandwidth ones. A key property of the proposed scheme is its ability to indirectly estimate the upload bandwidth of peers without explicitly knowing or measuring it. Simulation results show that our scheme significantly outperforms overlays with homogeneous properties, achieving up to 50% performance improvement. Moreover, the algorithm is robust to both parameter setting and changing conditions, e.g., peer churning.

We describe an analytical model, based on a Markov chain, suitable to study different bandwidth allocation policies for video streams over a WiMax access link. The Markov chain models an MPEG source, wireless channel conditions derived from a model compliant with WiMax specifications, and different bandwidth allocation policies. Validation with simulation results shows the correctness of the analytical model. The model permits to discuss the properties of various bandwidth allocation policies in terms of wasted slots, amount of lost data and access delays.

We analyze the capacity scaling laws of wireless ad hoc networks comprising significant inhomogeneities in the node spatial distribution over the network area. In particular, we consider nodes placed according to a shot-noise Cox process, which allows to model the clustering behavior usually recognized in large-scale systems. For this class of networks, we introduce novel techniques to compute upper bounds to the available per-flow throughput as the number of nodes tends to infinity, which are tight in the case of interference limited systems.

A fundamental issue arising in mobile ad hoc networks (MANETs) is the selection of the optimal path between any two nodes. A method that has been advocated to improve routing efficiency is to select the most stable path so as to reduce the latency and the overhead due to route reconstruction. In this work, we study both the availability and the duration probability of a routing path that is subject to link failures caused by node mobility. In particular, we focus on the case where the network nodes move according to the Random Direction model, and we derive both exact and approximate (but simple) expressions of these probabilities. Through our results, we study the problem of selecting an optimal route in terms of path availability. Finally, we propose an approach to improve the efficiency of reactive routing protocols.

In this paper, we consider two popular peer-to-peer TV (P2P-TV) systems: PPLive, one of the today most widely used P2P-TV systems, and Joost, a promising new generation application of which no previous measurement study has been considered. Besides the traditional measurements like the amount of generated traffic for signaling and data transmission, the novel contribution of the paper consists in investigating the content distribution mechanisms. In particular, we evaluate the characteristics of both data distribution and signaling process for the overlay network discovery and maintenance. By considering two or more clients in the same sub-network, we observe the capability of the system to exploit the locality of peers. We also explore how the system adapts to different network conditions. The methodology we develop allows also to identify periodic behavior of the application, highlighting bursts of both data and signaling traffic.

We provide the scaling laws for the transport capacity of a wide class of mobile wireless ad hoc networks. Our analysis generalizes previous results obtained under restrictive assumptions on the node mobility process and overall node density over the network area. The broader family of mobile networks that we consider is able to account for many important characteristics usually recognized in real traces of both human and vehicular mobility. In particular, we consider clustered, sparse networks of heterogeneous nodes, in which the shape of the spatial distribution of each node around one or more home-points plays a fundamental role in determining the overall transport capacity. We identify different operational regimes that arise within our general class of mobile networks, and for each regime we propose optimal scheduling and routing strategies achieving the maximum asymptotic capacity.

P2P-TV systems distribute live streaming contents by organizing the information flow in small chunks that are exchanged among peers. Different strategies can be implemented at the peers to select the chunk to distribute and the destination neighboring peer. Recent work showed that a good strategy consists in selecting the latest received chunk and a random neighboring peer (latest useful chunk, random peer). In this paper, leveraging on the idea that it is convenient to favor those peers that can contribute the most to the chunk distribution, we propose to select the destination peer with a probability proportional to the peer upload bandwidth. We show that the proposed scheme has a limited sensitivity to cheating peers that maliciously declare higher bandwidth than they actually have. Considering the overlay topology, we evaluate both systems in which nodes have fixed degree and systems whose overlay setup takes into account the actual peer bandwidth by assigning more neighbors to peer with higher bandwidth. We evaluate the performance in terms of delay percentiles and loss probability and evaluate the achieved improvements. Simulation results considering scenarios with up to 10,000 peers shows that the proposed schemes significantly outperform the traditional ones, so that the chunk distribution delay drops to less than 2 s from about 12 s.

Input queued (IQ) switches exploiting buffered crossbars (CICQ switches) are widely considered very promising architectures that outperform IQ switches with bufferless switching fabrics both in terms of architectural scalability and performance. Indeed the problem of scheduling packets for transfer through the switching fabric is significantly simplified by the presence of internal buffers in the crossbar, which makes possible the adoption of efficient, simple and fully distributed scheduling algorithms. This paper studies the throughput performance of CICQ switches supporting multicast traffic, showing that, similarly to IQ architectures, also CICQ switches with arbitrarily large number of ports may suffer of significant throughput degradation under ldquopathologicalrdquo multicast traffic patterns. Despite the asymptotic nature of these results, the authors believe that they can contribute to a deeper understanding of the behavior of CICQ architectures supporting multicast traffic.

In this paper, we study the performance of the 2-hop routing scheme proposed for ad hoc wireless networks with mobile nodes, considering realistic node mobility patterns. First, we provide a formal definition of optimal routing maximizing the throughput of a mobile ad hoc network, in terms of a multi-commodity flow problem over the associated contact graph. Then, we relate the effectiveness of the 2-hop routing strategy to structural properties of the contact graph. We present experimental results showing that, in real networks, contact times among the nodes are largely inhomogeneous. Our results show that, in networks with inhomogeneous contact times, the 2-hop routing strategy can result strongly inefficient in terms of network throughput.

Dealing with RTTs (round trip time) in IQ switches has been recently recognized as a challenging problem, especially if considering distributed (multi-chip) scheduler implementation which are suited to reduce the hardware complexity in very large, high-speed, switches. Traditional iterative three- or two-phase scheduling algorithms are based on a monolithic implementation, thus allowing instantaneous information exchange among input and output selectors to determine a matching. Multi-chip implementation imply that information exchange among inputs and outputs is delayed by an inter-chip latency. This delay requires non-trivial modifications to scheduling algorithms to allow a fully distributed implementation while keeping good performance. We propose a new scheduling algorithm, named SRR (synchronous round robin), which is suited to a fully distributed implementation and provides good performance if compared with more complex, non fully distributed, previously proposed scheduling algorithms.

We revisit the problem of characterizing the capacity of an ad hoc wireless network with n mobile nodes. Grossglauser and Tse (2001) showed that, by exploiting user mobility, it is possible to maintain a constant per-node throughput as the number of nodes grows. Their scheme allows to overcome the throughput decay (at least as 1/radicn) that affects networks with static nodes, which was first pointed out by Gupta and Kumar (2000). Subsequent works have analyzed the delay-capacity trade-off that arises in mobile networks under various mobility models. Almost invariably, however, available asymptotic results strongly rely on the assumption that nodes are identical, and move according to some ergodic process that is equally likely to visit any portion of the network area. In this paper, we relax such 'homogeneous mixing' assumption on the node mobility process, and analyze the network capacity in the more realistic case in which nodes are heterogeneous, and the motion of a node does not necessarily cover uniformly the entire space. We propose a general framework to characterize the capacity of networks with arbitrary mobility patterns, considering both the case of finite number of nodes (also with the support of experimental traces), as well as asymptotic results when the number of nodes grows to infinity.

We consider the problem of self-deployment and relocation in mobile wireless networks, where nodes are both sensors and actuators. We propose a unified, distributed algorithm that has the following features. During deployment, our algorithm yields a regular tessellation of the geographical area with a given node density, called monitoring configuration. Upon the occurrence of a physical phenomenon, network nodes relocate themselves so as to properly sample and control the event, while maintaining the network connectivity. Then, as soon as the event ends, all nodes return to the monitoring configuration. To achieve these goals, we use a virtual force-based strategy, which proves to be very effective even when compared to an optimal centralized solution.

We provide a general framework for the analysis of the capacity scaling properties in mobile ad-hoc networks with heterogeneous nodes and spatial inhomogeneities. Existing analytical studies strongly rely on the assumption that nodes are identical and uniformly visit the entire network space. Experimental data, however, have shown that the mobility pattern of individual nodes is typically restricted over the area, while the overall node density is often largely inhomogeneous, due to prevailing clustering behavior resulting from hot-spots. Such ubiquitous features of realistic mobility processes demand to reconsider the scaling laws for the per-user throughput achievable by the store-carry-forward communication paradigm which provides the foundation of many promising applications of delay tolerant networking. We show how the analysis of the asymptotic capacity of dense mobile ad-hoc networks can be transformed, under mild assumptions, into a Maximum Concurrent Flow (MCF) problem over anassociated Generalized Random Geometric Graph (GRGG). Our methodology allows to identify the scaling laws for a general class of mobile wireless networks, and to precisely determine under which conditions the mobility of nodes can indeed be exploited to increase the per-node throughput. At last we propose a simple, asymptotically optimal, scheduling and routing scheme that achieves the maximum transport capacity of the network.

In this paper, we revisit two classes of mobility models which are widely used to represent users' mobility in wireless networks: random waypoint (RWP) and random direction (RD). For both models, we obtain systems of partial differential equations which describe the evolution of the users' distribution. For the RD model, we show how the equations can be solved analytically both in the stationary and transient regime, adopting standard mathematical techniques. Our main contributions are 1) simple expressions which relate the transient duration to the model parameters and 2) the definition of a generalized random direction model whose stationary distribution of mobiles in the physical space corresponds to an assigned distribution.

Most of the current communication networks, including the Internet, are packet switched networks. One of the main reasons behind the success of packet switched networks is the possibility of performance gain due to multiplexing of network bandwidth. The multiplexing gain crucially depends on the size of the buffers available at the nodes of the network to store packets at the congested links. However, most of the previous work assumes the availability of infinite buffer-size. In this paper, we study the effect of finite buffer-size on the performance of networks of interacting queues. In particular, we study the throughput of flow-controlled loss-less networks with finite buffers. The main result of this paper is the characterization of a dynamic scheduling policy that achieves the maximal throughput with a minimal finite buffer at the internal nodes of the network under memory-less (e.g., Bernoulli IID) exogenous arrival process. However, this ideal performance policy is rather complex and, hence, difficult to implement. This leads us to the design of a simpler and possibly implementable policy. We obtain a natural trade-off between throughput and buffer-size for such implementable policy. Finally, we apply our results to packet switches with buffered crossbar architecture

Optical switching fabrics (OSF) are considered to be appealing solutions for the design of high speed packet switches, due to their excellent scalability in terms of bandwidth and power consumption. Candidate technologies are MEMS, bubble switches, broadcast-and-select networks with tunable devices. All of them suffer a reconfiguration latency each time the input/output connections are changed, due to technological constraints; unfortunately, this latency is not negligible with respect to the packet transmission time, and can adversely affect performance, especially delay and throughput. When scheduling the transmission of packets across an OSF, the multi-hop approach was shown to be a promising way to control the tradeoff between delay and throughput. In this case, the OSF is configured just once in a while, on a time scale much larger than the packet transmission time, and packets may be recirculated across the ports to provide full or partial connectivity among ports. Previous works have investigated this approach when a physical ring topology is used for the interconnection. Here, we extend the multi-hop approach to multidimensional regular topologies, which offer a better tradeoff between throughput and delay. We discuss not only the scheduling problem for these topologies, but also the design of routing. We investigate performance by simple analytical models and show the design tradeoff among throughput, speedup and delays.

Input queued (IQ) switches exploiting buffered cross- bars (CICQ switches) are widely considered very promising archi- tectures that outperform IQ switches with bufferless switching fab- rics both in terms of architectural scalability and performance. In- deed the problem of scheduling packets for transfer through the switching fabric is significantly simplified by the presence of in- ternal buffers in the crossbar, which makes possible the adoption of efficient, simple and fully distributed scheduling algorithms. This paper studies the throughput performance of CICQ switches sup- porting multicast traffic, showing that, similarly to IQ architec- tures, also CICQ switches with arbitrarily large number of ports may suffer of significant throughput degradation under patho- logical multicast traffic patterns. Despite the asymptotic nature of these results, the authors believe that they can contribute to a deeper understanding of the behavior of CICQ architectures sup- porting multicast traffic. Index Terms—Buffered crossbars, multicast, packet switching, scheduling.

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

In this paper we revisit two classes of mobility models which are widely used to represent users' mobility in wireless networks: Random Waypoint (RWP) and Random Direction (RD). For both models we obtain systems of partial differential equations which describe the evolution of the users' distribution. For the RD model, we show how the equations can be solved analytically both in the stationary and transient regime adopting standard mathematical techniques. Our main contributions are i) simple expressions which relate the transient duration to the model parameters; ii) the definition of a generalized random direction model whose stationary distribution of mobiles in the physical space corresponds to an assigned distribution.

The advent of packet networks has motivated many researchers to study the performance of networks of queues in the last decade or two. However, most of the previous work assumes the availability of infinite queue-size. Instead, in this paper, we study the maximal achievable throughput in a flow-controlled lossless network with finite-queue size. In such networks, throughput depends on the packet scheduling policy utilized. As the main of this paper, we obtain a dynamic scheduling policy that achieves the maximal throughput (equal to the maximal throughput in the presence of infinite queue-size) with a minimal finite queue-size at the internal nodes of the network. Though the performance of the policy is ideal, it is quite complex and hence difficult to implement. This leads us to a design of simpler and possibly implementable policy. We obtain a natural trade-off between throughput and queue-size for this policy. We apply our results to the packet switches with buffered crossbar architecture. We propose a simple, implementable, distributed scheduling policy which provides high throughput in the presence of minimal internal buffer. We also obtain a natural trade-off between throughput, internal speedup and buffer-size providing a switch designer with a gamut of designs. To the best of authors' knowledge, this is one of the first attempts to study the throughput for general networks with finite queue-size. We believe that our methods are general and can be useful in other contexts.

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 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.

We propose a novel framework to solve the problem of scheduling packets in high-speed input-queued switches with frame-based control. Our approach is based on the application of game theory concepts. We define a flexible scheduling policy, named SSB (slot sell and buy): the existence of a unique Nash equilibrium for the policy is proved, together with properties of convergence of these equilibria. These findings allows us to state that our SSB scheduling policy achieves 100% throughput both in isolated input-queued switches arid in networks of input-queued switches. Simulation results are used to further validate the approach and to show its flexibility in dealing with differentiated QoS guarantees.

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.

In this paper we investigate a new idea for traffic matrix estimation that makes the basic problem less under-constrained, by deliberately changing the routing to obtain additional measurements. Because all these measurements are collected over disparate time intervals, we need to establish models for each Origin-Destination (OD) pair to capture the complex behaviours of internet traffic. We model each OD pair with two components: the diurnal pattern and the fluctuation process. We provide models that incorporate the two components above, to estimate both the first and second order moments of traffic matrices. We do this for both stationary and cyclo-stationary traffic scenarios. We formalize the problem of estimating the second order moment in a way that is completely independent from the first order moment. Moreover, we can estimate the second order moment without needing any routing changes (i.e., without explicit changes to IGP link weights). We prove for the first time, that such a result holds for any realistic topology under the assumption of minimum cost routing and strictly positive link weights. We highlight how the second order moment helps the identification of the top largest OD flows carrying the most significant fraction of network traffic. We then propose a refined methodology consisting of using our variance estimator (without routing changes) to identify the top largest flows, and estimate only these flows. The benefit of this method is that it dramatically reduces the number of routing changes needed. We validate the effectiveness of our methodology and the intuitions behind it by using real aggregated sampled netflow data collected from a commercial Tier-1 backbone.

The data and voice integration over dense wavelength-division-multiplexing (DAVID) project proposes a metro network architecture based on several wavelength-division-multiplexing (WDM) rings interconnected via a bufferless optical switch called Hub. The Hub provides a programmable interconnection among rings on the basis of the outcome of a scheduling algorithm. Nodes connected to rings groom traffic from Internet protocol routers and Ethernet switches and share ring resources. In this paper, we address the problem of designing efficient centralized scheduling algorithms for supporting multiclass traffic services in the DAVID metro network. Two traffic classes are considered: a best-effort class, and a high-priority class with bandwidth guarantees. We define the multiclass scheduling problem at the Hub considering two different node architectures: a simpler one that relies on a complete separation between transmission and reception resources (i.e., WDM channels) and a more complex one in which nodes fully share transmission and reception channels using an erasure stage to drop received packets, thereby allowing wavelength reuse. We propose both optimum and heuristic solutions, and evaluate their performance by simulation, showing that heuristic solutions exhibit a behavior very close to the optimum solution.

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.

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.

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.

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.

We focus on all-optical broadcast and select slotted WDM networks. Each network user is equipped with one tunable transmitter and one fixed receiver; full connectivity is achieved by tuning transmitters to all different wavelengths available in the optical spectrum. Tuning latencies are considered to be not negligible with respect to the slot time. A network controller allocates fixed size slots in a TDM/WDM frame according to requests issued by users via signalling procedures. User requests are accommodated in the frame incrementally, as soon as they are received by the network controller. Since we aim at an incremental solution, we impose a transparency constraint in the scheduling algorithm: new user requests may be accepted only without affecting existing allocations, otherwise they are refused. We propose a novel scheduling algorithm that may route some flows from source to destination through some intermediate nodes, following a multi-hop approach. A formal definition of an optimal transparent incremental scheduling algorithm is provided as an integer linear programming problem. The optimal incremental scheduling algorithm is NP-hard. Thus, a heuristic quasi-optimal scheduling algorithm is proposed, and its complexity is evaluated. Performance results show that significant benefits can be achieved with respect to traditional single-hop approaches and to other multi-hop approaches.

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.

The paper considers optical packet switching networks with slotted operation. A general model of network nodes is introduced, based upon a nonblocking switching fabric, and re-circulating fiber delay lines to solve contentions. Given the current large bandwidth availability in optical networks, and the projected limitations of electronic switches, a new approach to network design is proposed, aiming at balancing wavelength and buffer allocation taking the number of switch ports as a constraint. Given the problem complexity, a heuristic solution is proposed, using simple queuing theory to model network links. The effectiveness of the new network dimensioning approach is demonstrated by running a simulation program on manually dimensioned topologies, and on topologies dimensioned using the proposed approach.

We present the Distributed Frame-Definition Algorithm (DFDA), a novel scheduling policy for input-queued switches with virtual output queueing at the input line cards. The DFDA effectively supports the integration of traffic classes with diverse Quality-of-Service (QoS) requirements (as compelled by emerging QoS frameworks such as Differentiated Services), and scales well with the aggregate capacity of the switch because of the limited complexity of its distributed implementation. The input and output tine cards exchange a minimal amount of control information to define the periodic service schedule that regulates transit through the switch fabric. The composition of the service schedule dynamically adapts to the tight bandwidth requirements of real-time traffic and to the load fluctuations of best-effort traffic. We assess the performance of the DFDA through simulation, and compare it with the most popular scheduling algorithms for input-queued switches. Our experiments show that the DFDA generally sustains higher throughput than the schemes of the prior art.

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.

The speedup of a switch is the factor by which the switch, and hence the memory used in the switch, runs faster compared to the line rate. In high-speed switches, line rates are already touching the limits at which memory can operate. It is very important for a switch to run at as low a speedup as possible. For an input queued (IQ) switch at speedup 1, 100% throughput can be achieved for any admissible traffic (McKeown, N. et al., 1999; Dai, J. and Prabhakar, B., 2000). This gives finite average delays but does not guarantee control on packet delays. S.T. Chuang et al. (see IEEE J. Selected Areas of Commun., vol.17, no.6, p.1030-9, 1999) show that a combined input output queued (CIOQ) switch can emulate perfectly an output queued (OQ) switch at a speedup of 2 and, thus, control the packet delays. This motivates a study of the possibility of obtaining delay control at speedup less than 2. To guarantee optimal control of delays for a general class of traffic, as shown by Chuang et al., speedup 2 is necessary. Hence, to obtain control of delays at lower speedup, we need to restrict the class of arrival traffic. We study the speedup requirement for a class of admissible traffic, which we denote as (1, nF)-regulated traffic, with parameters n and F. We obtain the necessary speedup for this class of traffic. Further, we present a general class of algorithms working at the necessary speedups and thus providing bounded delays.

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.

Wavelength routed optical networks allow to design a logical topology, comprising lightpaths and routers, which is overlayed on the physical topology, comprising optical fibers and optical cross-connects, by solving a routing and wavelength assignment (RWA) problem. In this paper we extend the concept of lightpath to the one of super-lightpath, which uses a simple bit level time division multiplexing that can be directly implemented in the optical domain, to split the wavelength bandwidth among more than one traffic flow. This allows to design logical topologies with an increased number of logical links, thus reducing the average distance among nodes, i.e., the number of electro-optic and opto-electronic conversions, and the traffic congestion on logical links. At the same time, this reduces the number of wavelengths required to solve the RWA problem. Being the super-lightpath RWA problem computationally intractable, we propose two heuristics which show that the number of wavelengths required to overlay the same logical topology on the same physical topology is reduced by more that 65% using super-lightpaths.

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.

We consider all-optical TDM/WDM broadcast and select networks. We assume that each network node is equipped with one fixed transmitter and one tunable receiver; tuning times are assumed to be not negligible with respect to the slot time. We discuss efficient scheduling algorithms to assign TDM/WDM slots to multicast traffic in such networks. Given the problem complexity, heuristic algorithms based on the Tabu Search methodology are proposed, and their performance is assessed using randomly created request matrices based on two types of multicast traffic patterns: a video-conference, and a server distribution traffic pattern. The considered performance index is the frame length required to schedule a given traffic request matrix.

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 describes the network architecture and provides a performance analysis of a passive optical network named SONATA, which has been proposed and demonstrated in the European Union ACTS program. In this nationwide-all-optical network, end-terminals access a single passive routing node via PONs using a TDMA/WDMA access scheme based on slot reservations. The centralized network controller runs resource allocation algorithms to avoid conflicts among end-terminals. Since the resource allocation problem at the network controller can be shown to be in general NP-hard, we provide heuristic algorithms to solve the problem. The analysis of the algorithms is performed via both analysis and simulation.

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.

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.

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 study the bidirectional shufflenet topology, which is obtained from the well-known (unidirectional) shufflenet by considering bidirectional links. More specifically, we define a shortest path routing algorithm, and derive the diameter and the average distance of the topology. The bidirectional shufflenet is then compared, in terms of average distance, with other variations of the perfect shuffle. Bidirectional links are very common in real networks. Possible applications of bidirectional shufflenets are wormhole routing electronic networks with back-pressure flow control, and wavelength routing optical networks. The former class of networks is considered, when virtual channels are used to prevent deadlocks. We show that four virtual channels are sufficient to avoid deadlocks in the bidirectional shufflenet, regardless of the number of nodes in the topology.

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 focus on all-optical broadcast and select slotted WDM networks. Each network user is equipped with one tunable transmitter and one fixed receiver; full connectivity is achieved by tuning transmitters to all different wavelengths available in the optical spectrum. Tuning latencies are considered to be not negligible with respect to the slot time. A centralized network controller allocates slots in a TDM/WDM frame according to requests issued by users. User requests are accommodated in the frame incrementally, as soon as they are received by the network controller. We propose a novel scheduling algorithm that may route some flows from source to destination through some intermediate nodes, following a multi-hop approach. Since we aim at an incremental solution, we impose a transparency constraint: new user requests may be accepted only without affecting existing allocations, otherwise they are refused. A heuristic quasi-optimal scheduling algorithm is proposed. Performance results show that significant benefits can be achieved with respect to traditional single-hop approaches.

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.

This paper describes the network architecture and provides a performance analysis of a passive optical network named SONATA, which has been proposed and demonstrated in the context of the European Union ACTS Program. In this nationwide all-optical network, end terminals access a single passive routing node via PONs using a TDMA/WDMA access scheme based on reservations. The centralized network controller runs resource allocation algorithms in order to avoid conflicts among end terminals. We formally define the resource allocation problem at the network controller, and show that, in general, it is NP-hard. We also provide simple heuristic algorithms to solve the problem. The analysis of the algorithms is performed both via analysis and simulation.

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.

We study signal-space coding for coherent slow frequency-hopped communications over a Gaussian multiple-access collision channel (G-MACC). We define signal sets and interleavers having maximum collision resistance. The packet-error probability and the spectral efficiency obtained by these signal sets concatenated with outer block coding and hard (error-only) decoding is evaluated without assuming perfect interleaving. Closed-form expressions are provided and computer simulations show perfect agreement with analysis. The structure of good interleavers is also discussed. More generally, we present expressions for the information outage probability and for the achievable (ergodic) rate of the G-MACC at hand, under various assumptions on user coding and decoding strategies. The outage probability yields the limiting packet-error probability with finite interleaving depth (delay-limited systems). The achievable rate yields the limiting system spectral efficiency for large interleaving depth (delay-unconstrained systems). Comparisons with other classical multiple-access schemes are provided.

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.

We address the problem of providing transparent, reliable, and efficient network-level multicasting in wormhole LANs. We describe some alternatives for achieving deadlock-free multicasting using fast buffer reservation techniques at the host interface level. Tradeoffs involving complexity and performance of various solutions are discussed, and are illustrated using simulation results. Our results show that in most cases the straightforward approach of sending multiple unicast copies of the multicast message at the originator host turns out to be the most simple and effective approach.

In this paper we study the bidirectional shufflenet topology, which is obtained from the well-known (unidirectional) shufflenet by considering bidirectional links. More specifically, we define a shortest-path routing algorithm, and derive the diameter and the average distance of the topology. The bidirectional shufflenet is then compared, in terms of average distance, with other variations of the perfect shuffle.

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 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.

In order to minimize latency in high-speed interconnection networks, the wormhole routing technique can be employed. With this technique, a switch transmits an incoming message as soon as it receives it, without waiting for the entire message. The problem then is that a message stretches over several links and locks network resources, thus making a contention situation possible. Two principal congestion control mechanisms cast be considered, backpressure flow control and deflection routing. The performance of these mechanisms depends both on the traffic characteristics and on the network topology. In order to compare them, we study analytically the behavior of a wormhole routing network model with random input traffic under both policies. We estimate the probability of collision between messages and express the average message transit delay as a function of the offered load. Simulation provides a good confirmation for the analytical results. This study gives us an understanding of the behavior of the system under different resource management policies.

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.

Wormhole routing networks have become increasingly popular for low latency, high-speed interconnection of supercomputer and workstation clusters. An example is the Supercomputer SuperNet (SSN) at UCLA, which interconnects supercomputers across campus and metropolitan area distances. The SSN employs a two-level network architecture in which an optical backbone network interconnects several high-speed, wormhole-routing local area networks (Myrinets). The SSN applications such as scientific visualization and rendering require that the network support reliable delivery of traffic characterized by quality of service (QoS) parameters. Motivated by this requirement, we investigate QoS support in Myrinet-like high-speed, wormhole routing networks. Since native Myrinet protocols do not provide QoS support, we explore several novel strategies including (a) the use of a separate subnet for carrying such traffic (along with source pacing), (b) the overlay of a virtual synchronous system on the asynchronous network, and (c) the introduction of virtual channels. We discuss the tradeoffs among the different options and evaluate them via selected simulation experiments.

The Supercomputer SuperNet (SSN) is a two-level hierarchical high-speed network. The lower level is a high speed electronic mesh fabric; the higher level is a WDM optical backbone network interconnecting the high-speed fabrics distributed across a campus or metropolitan area. The salient characteristics of this architecture are the use of wormhole routing and backpressure hop-by-hop flow control mechanism. Because of these features, deadlocks are possible in SSN. In this paper, we address the issue of deadlock-free routing which is an essential prerequisite for the proper operation of SSN. To this end, we first present a deadlock free routing scheme for the WDM backbone which is implemented with a shufflenet multihop virtual topology. We use the notion of virtual channels to obtain mappings of virtual channels to physical channels such that deadlock-free routing is achieved for any (p,k) shufflenet (uni and bidirectional). Then, we compare the virtual channels scheme with the more conventional up/down deadlock free routing scheme for the bidirectional shufflenet and show that the former yields much better performance. Finally, we address the problem of deadlock prevention across the entire network (i.e., lower level fabric as well as the optical backbone) and develop an integrated solution combining different schemes best suited for the different levels.

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.

The traditional shufflenet multihop virtual topology for optical networks does not provide easy support for backpressure flow control on a hop-by-hop basis. In the context of an optical backbone network interconnecting high speed electronic LANs that use wormhole routing, as in the Supercomputer SuperNet (SSN) project, hop-by-hop flow control is required in the optical network to eliminate losses due to buffer overflows. We modify the shufflenet topology by using bidirectional links to obtain a new topology called bidirectional shufflenet. This topology provides a natural support for the hop-by-hop backpressure flow control mechanism. We demonstrate the need for flow control in the wormhole routing context. We compare the average hops with the shufflenet and bilayered shufflenet. Then, we compare shufflenet with bidirectional shufflenet via simulation to show the performance improvements yielded by the latter. The throughput of the bidirectional shufflenet is better for large worms while the delay is comparable. The length of the worm has a clear effect on the throughput and delay values obtained.

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.>

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.>