Abstract Driven by the rising interest in machine learning techniques, mathematical continuous optimization algorithms have made great progress. Among them, first-order optimization algorithms, which rely on the first derivative (gradient) to find a function’s minimum or maximum, have gained popularity due to their efficiency and scalability. As a result, a vast number of optimization algorithms have been developed, each applying different techniques and offering diverse guarantees. This variety, while beneficial, makes selecting the most appropriate algorithm a challenging yet crucial task–choosing the wrong one may lead to sub-par accuracy or performance. This paper explores the state of the art in continuous first-order optimization algorithms, offering guidance for selecting the most suitable method. We classify 23 algorithms, detailing their dependency relationships, theoretical foundations, and optimization strategies. The analysis includes a performance evaluation using implementations in the PyTorch framework. Convergence, quantified by the area under the training-loss curve, is assessed with two benchmarks: the Rosenbrock function as a standard test and ResNet-18 training on the CIFAR-10 dataset as a practical test. We evaluate performance using an integral metric and analyze robustness to hyperparameter variations, including learning rate sensitivity. Additionally, we introduce a classification of algorithm convergence behaviors. These experiments provide insights into algorithm performance across varying problem complexities and highlight their stability under hyperparameter changes. Practitioners and researchers can use this work as a guide to identify the set of most likely good candidates as first-order optimization algorithms for their use case.

Parallel And Distributed Simulation (PADS) is a well-known modeling paradigm that allows for efficient implementation of large simulation models as a collection of interacting entities, called Simulated Entities (SEs). SEs can then be assigned to independent execution units for parallel execution, if possible. Allocating SEs to execution units is one of the most challenging problems in PADS: ideally, highly interacting SEs should be placed on the same execution unit so that all communications are local; however, placing too many SEs on the same processor might degrade performance. Furthermore, many simulation models exhibit non-uniform computation and communication patterns among components, that might change during execution at unpredictable times. In this paper we propose a clustering heuristic that exploits communication locality, with the aim to reduce the communication cost experienced by the PADS during the execution. The heuristic adapts automatically to changing interaction patterns by migrating SEs, and can do so without any user-visible modification of the simulation model; there are, however, some parameters that can be used to tune the heuristic. We perform a large set of computational experiments to assess the effectiveness of the heuristic on a real-world scenario, with the aim of guiding the users in selecting optimal values of the heuristic parameters.

The rise of blockchain has expanded the possibilities for asset representation, particularly through Non-Fungible Tokens (NFTs), which enable unique connections between digital and physical assets. Despite their potential, existing NFT systems face challenges such as high transaction costs and uncertainties related to the management and transfer of associated physical assets. This paper introduces a decentralized marketplace architecture designed to address these challenges, by making use of blockchain-based smart contracts, decentralized storage solutions, and trustless validation mechanisms. The proposed system separates ownership from possession, ensuring secure and transparent asset transfers. The architecture was then tested in a blockchain-based environment, demonstrating its economic viability. This work paves the way for decentralized asset management across various domains, such as rental services and remote logistics. By addressing existing challenges, the proposed architecture provides a usable and cost-effective solution that links digital and physical asset ecosystems while preserving the fundamental principles of blockchain technology.

Multilevel modeling is a design technique that allows complex models to be specified in terms of simpler ones. An obvious advantage of multilevel modeling is the possibility of reusing existing models, therefore reducing development cost. However, multilevel modeling entails more than the mere application of the ubiquitous principle of decomposition. Indeed, in a multilevel model the same entity may be described at different levels of detail during execution. This allows, for example, to focus on areas of interest by switching to a more detailed model, and revert back to a faster but less accurate one when possible. In this paper we propose GEMMA (GEneric Multilevel Modeling Abstraction), a methodology inspired by DEVS that can help the designer to produce sound and correct multilevel models. We propose a practical realization of GEMMA as a concrete software architecture that can be instantiated in different programming languages. We illustrate a simple use case where different sub-models written in different programming languages are combined according to the GEMMA methodology. Our ultimate goal is to bridge the gap between theory and practice of multilevel modeling, to foster a broader adoption of this useful technique.

Computing intersections among sets of one-dimensional intervals is an ubiquitous problem in computational geometry with important applications in bioinformatics, where the size of typical inputs is large and it is therefore important to use efficient algorithms. In this paper we propose a parallel algorithm for the 1D intersection-counting problem, that is, the problem of counting the number of intersections between each interval in a given set A and every interval in a set B. Our algorithm is suitable for shared-memory architectures (e.g., multicore CPUs) and GPUs. The algorithm is work-efficient because it performs the same amount of work as the best serial algorithm for this kind of problem. Our algorithm has been implemented in C++ using the Thrust parallel algorithms library, enabling the generation of optimized programs for multicore CPUs and GPUs from the same source code. The performance of our algorithm is evaluated on synthetic and real datasets, showing good scalability on different generations of hardware.

Mobile devices entered people's lives by leaps and bounds, offering various applications relying on private third-party entities to manage their users' data. Centralized control of personal health data endangers the privacy of the users directly involved. In the future, there will likely be a trend toward decentralizing the health data collection, relieving central entities of this task. This comes with several challenges in a decentralized environment, such as avoiding a single point of failure to guarantee data availability. The following work proposes an architecture based on Distributed Ledger Technology to allow users to decide on their data while ensuring availability by employing social networks. We will outline the mechanisms behind data storage and the implications of using smart contracts in the architecture. In concluding the work, we show the developed architecture and results deriving from its assessment, highlighting possible use cases applied to the specific health data management context.

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area.

The rise of Internet-of-Things enables the development of smart applications devoted to improving the quality of life in urban and rural areas, thus fostering the creation of smart territories. However, some dislocated areas are underprivileged in providing such services due to the lack, inefficiency, or excessive cost of Internet access. Opportunistic networking techniques might aid in surmounting these problems. In this article, we propose a framework that relies on an untrusted Data Mule to carry data from an offline source to an online destination. In particular, we present a framework that enables the communication between different actors and a reward mechanism using Distributed Ledger Technologies, Smart Contracts, and Decentralized File Storage. The protocol involved in bringing a Client’s message online and getting back a response is thoroughly explained in all its steps and then discussed on the most important trust and security issues. Finally, we evaluate such a protocol and the whole framework through a series of communication latency tests, an analysis of the Smart Contract usage, and simulations in which buses act as Data Mules. Our results suggest the feasibility of our proposal in a smart territory scenario.

The Internet Computer Protocol is a new generation blockchain that aims to provide better security and scalability than the traditional blockchain solutions. In this paper, this innovative distributed computing architecture is introduced, modeled and then simulated by means of an agent-based simulation. The result is a digital twin of the current Internet Computer, to be exploited to drive future design and development optimizations, investigate its performance, and evaluate the resilience of this distributed system to some security attacks. Preliminary performance measurements on the digital twin and simulation scalability results are collected and discussed. The study also confirms that agent-based simulation is a prominent simulation strategy to develop digital twins of complex distributed systems.

Multilevel modeling is increasingly relevant in the context of modelling and simulation since it leads to several potential benefits, such as software reuse and integration, the split of semantically separated levels into sub-models, the possibility to employ different levels of detail, and the potential for parallel execution. The coupling that inevitably exists between the sub-models, however, implies the need for maintaining consistency between the various components, more so when different simulation paradigms are employed (e.g., sequential vs parallel, discrete vs continuous). In this paper we argue that multilevel modelling is well suited for the simulation of human mobility, since it naturally leads to the decomposition of the model into two layers, the “micro” and “macro” layer, where individual entities (micro) and long-range interactions (macro) are described. In this paper we investigate the challenges of multilevel modeling, and describe some preliminary results using prototype implementations of multilayer simulators in the context of epidemic diffusion and vehicle pollution.

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas, i.e. "smart territories". With the aim to offer a practical and secure way to transport data in such constrained scenarios, we focus on the problem of incentivizing to Data Mules, i.e. devices dedicated to enable communication even in the absence of the Internet. We combine decentralized technologies and State-Channels to verify the correct behavior of participants in an offline scenario.

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas yet, due to the lack of or too costly wide area network connectivity. With the aim to offer a practical and secure way to transport data and allow for communications in such constrained scenarios, we focus on the problem of incentivizing to data mules, i.e. devices dedicated to enable the data transfer even in the absence of the Internet. Our solution combines the use of several distributed technologies for verifying the correct behavior of all the partici-pants and incentivize them. We focus on the use of state channels to support the flow of smart-contract-based tokens as a form of payment, in a condition where participants communicate only with others in physical proximity. Furthermore, we validate the viability of the application through the simulation of peer-to-peer interactions between the participants. In this work we achieve positive results in terms of communication latency and percentage of client nodes which are able to benefit from the system.

Abstract The number of users approaching the world of cryptocurrencies exploded in the last years, and consequently the daily interactions on their underlying distributed ledgers have intensified. In this paper, we analyze the flow of these digital transactions in a certain period of time, trying to discover important insights on the typical use of these technologies by studying, through complex network theory, the patterns of interactions in four prominent and different Distributed Ledger Technologies (DLTs), namely Bitcoin, DogeCoin, Ethereum, Ripple. In particular, we describe the Distributed Ledger Network Analyzer (DiLeNA), a software tool for the investigation of the transactions network recorded in DLTs. We show that studying the network characteristics and peculiarities is of paramount importance, in order to understand how users interact in the DLT. For instance, our analyses reveal that all transaction graphs exhibit small world properties.

Dealing with a growing amount of data is a crucial challenge for the future of information and communication technologies. More and more devices are expected to transfer data through the Internet, therefore new solutions have to be designed in order to guarantee low latency and efficient traffic management. In this paper, we propose a solution that combines the edge computing paradigm with a decentralized communication approach based on Peer-to-Peer (P2P). According to the proposed scheme, participants to the system are employed to relay messages of other devices, so as to reach a destination (usually a server at the edge of the network) even in absence of an Internet connection. This approach can be useful in dynamic and crowded environments, allowing the system to outsource part of the traffic management from the Cloud servers to end-devices. To evaluate our proposal, we carry out some experiments with the help of LUNES, an open source discrete events simulator specifically designed for distributed environments. In our simulations, we tested several system configurations in order to understand the impact of the algorithms involved in the data dissemination and some possible network arrangements.

Plenty of research on smart mobility is currently devoted to the inclusion of novel decentralized software architectures to these systems, due to the inherent advantages in terms of transparency, traceability, trustworthiness. MOVO is a decentralized application (dApp) for smart mobility. It includes: (i) a module for collecting data from vehicles and smartphones sensors; (ii) a component for interacting with Distributed Ledger Technologies (DLT) and Decentralized File Storages (DFS), for storing and validating sensor data; (iii) a module for "offline" interaction between devices. The dApp consists of an Android application intended for use inside a vehicle, which helps the user/driver collect contextually generated data (e.g. a driver’s stress level, an electric vehicle’s battery level), which can then be shared through the use of DLT (i.e., IOTA DLT and Ethereum smart contracts) and DFS (i.e., IPFS). The third module consists of an implementation of a communication channel that, via Wi-Fi Direct, allows two devices to exchange data and payment information with respect to DLT (i.e. cryptocurrency and token) assets. In this paper, we describe the main software components and provide an experimental evaluation that confirms the viability of the MOVO dApp in real mobility scenarios.

Distributed Ledger Technologies (DLT) and Decentralized File Storages (DFS) are becoming increasingly used to create common, decentralized and trustless infrastructures where participants interact and collaborate in Peer-to-Peer interactions. A prominent use case is represented by decentralized data marketplaces, where users are consumers and providers at the same time, and trustless interactions are required. However, data in DLTs and DFS are usually unstructured and there are no efficient mechanisms to query a certain type of data for the search in the market. In this paper, we propose the use of a Distributed Hash Table (DHT) as a layer on top of DLTs where, once the data are acquired and stored in the ledger, these can be searched through multiple keyword based queries, thanks to the lookup functionalities offered by the DHT. The DHT network is a hypercube overlay structure, organized for an efficient processing of multiple keyword-based queries. We provide the architecture of such solution for a decentralized data marketplace and an analysis based on a simulation that proves the viability of the proposed approach.

DTLS is a protocol that provides security guarantees to Internet communications. It can operate on top of both TCP and UDP transport protocols. Thus, it is particularly suited for peer-to-peer and distributed multimedia applications. The same holds if the endpoints are mobile devices. In this scenario, mechanisms are needed to surmount possible network disconnections, often arising due to the mobility or the scarce resources of devices, that can jeopardize the quality of the communications. Session resumption is thus a main issue to deal with. To this aim, we propose a fast reconnection scheme that employs non-connected sockets to quickly resume DTLS communication sessions. The proposed scheme is assessed in a performance evaluation that confirms its viability.

This paper presents a system architecture to promote the development of smart transportation systems. Thanks to the use of distributed ledgers and related technologies, it is possible to create, store and share data generated by users through their sensors, while moving. In particular, IOTA and IPFS are used to store and certify data (and their related metadata) coming from sensors or by the users themselves. Ethereum is exploited as the smart contract platform that coordinates the data sharing and provisioning. The necessary privacy guarantees are provided by the usage of Zero Knowledge Proof. We show some results obtained from some use case scenarios that demonstrate how such technologies can be integrated to build novel smart services and to promote social good in user mobility.

This paper presents an architecture, based on Distributed Ledger Technologies (DLTs) and Decentralized File Storage (DFS) systems, to support the use of Personal Information Management Systems (PIMS). DLT and DFS are used to manage data sensed by mobile users equipped with devices with sensing capability. DLTs guarantee the immutability, traceability and verifiability of references to personal data, that are stored in DFS. In fact, the inclusion of data digests in the DLT makes it possible to obtain an unalterable reference and a tamper-proof log, while remaining compliant with the regulations on personal data, i.e. GDPR. We provide an experimental evaluation on the feasibility of the use of DFS. Three different scenarios have been studied: i) a proprietary IPFS approach with a dedicated node interfacing with the data producers, ii) a public IPFS service and iii) Sia Skynet. Results show that through proper configuration of the system infrastructure, it is viable to build a decentralized Personal Data Storage (PDS).

This paper presents an architecture of a Personal Information Management System, in which individuals can define the access to their personal data by means of smart contracts. These smart contracts, running on the Ethereum blockchain, implement access control lists and grant immutability, traceability and verifiability of the references to personal data, which is stored itself in a (possibly distributed) file system. A distributed authorization mechanism is devised, where trust from multiple network nodes is necessary to grant the access to the data. To this aim, two possible alternatives are described: a Secret Sharing scheme and Threshold Proxy Re-Encryption scheme. The performance of these alternatives is experimentally compared in terms of execution time. Threshold Proxy Re- Encryption appears to be faster in different scenarios, in particular when increasing message size, number of nodes and the threshold value, i.e. number of nodes needed to grant the data disclosure.

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Summary In this paper, we analyze the Ethereum blockchain using the complex networks modeling framework. Accounts acting on the blockchain are represented as nodes, while the interactions among these accounts, recorded on the blockchain, are treated as links in the network. Using this representation, it is possible to derive interesting mathematical characteristics that improve the understanding of the actual interactions happening in the blockchain. Not only, by looking at the history of the blockchain, it is possible to verify if radical changes in the blockchain evolution happened.

Research on data confidentiality, integrity and availability is gaining momentum in the ICT community, due to the intrinsically insecure nature of the Internet. While many distributed systems and services are now based on secure communication protocols to avoid eavesdropping and protect confidentiality' the techniques usually employed in distributed simulations do not consider these issues at all. This is probably due to the fact that many real-world simulators rely on monolithic, offline approaches and therefore the issues above do not apply. However, the complexity of the systems to be simulated, and the rise of distributed and cloud based simulation, now impose the adoption of secure simulation architectures. This paper presents a solution to ensure both anonymity and confidentiality in distributed simulations. A performance evaluation based on an anonymized distributed simulator is used for quantifying the performance penalty for being anonymous. The obtained results show that this is a viable solution.

On battery-operated devices, energy and power consumption are main concerns. With the recent advancement of technology, mobile devices can be integrated with traditional systems for running complex computations. In fact, mobile devices can easily become part of computational networks and share their computational and memory resources. Despite this, traditional simulation frameworks are not designed to perform well on heterogeneous networks. This is mainly due to the limited computational resources that are available on mobile devices. In this paper, we propose SEECSSim (SEECSSim is derived from School of Electrical Engineering and Computer Science (SEECS)) that is a simulation framework specifically designed for mobile devices. SEECSSim includes state-of-the-art distributed synchronization algorithms that are implemented to run on mobile or embedded devices. To benchmark the proposed framework, the well-known PHOLD model is used and performance results are reported in terms of execution time, CPU usage, memory and energy consumption.

Summary This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent‐based parallel and distributed simulator, an OMNeT++ based discrete event simulator, and a script‐language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.

Real-Time Collaborative Editing (RTCE) is a popular way of instrumenting cooperative work on documents, in particular on the Web. Little is known in the literature yet about RTCE usage patterns in the real world. In this paper we study how a popular RTCE editor (Etherpad) is used in the wild, digging into the edit histories of a large collection of documents (about 14 000 pads), retrieved from one of the most popular public instances of the platform, hosted by the Wikimedia Foundation. The pad analysis is supported by a novel conceptual model that allows to label edit operations as "collaborative" or not depending on their distance-in edit position (space), edit time, or spacetime (both)-from edits made by other authors. The model is applied to classify all edits from the pad corpus. Classification results are further used to characterize the collaboration behavior of pad authors. Findings show that: 1) about half of the pads have a single author and hence witnessed no collaboration; 2) collaboration on common document parts happens often, but it happens asynchronously with authors taking turns in editing; and 3) simultaneous editing of common document parts happens very rarely. These findings help in revisiting early RTCE design decisions (e.g., the granularity of conflict management in RTCE protocols) and give insights on how to address novel needs (e.g., end-to-end encryption and offline editing).

This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.

In this paper we consider the problem of identifying intersections between two sets of d-dimensional axis-parallel rectangles. This is a common operation that arises in many agent-based simulation studies, and is of central importance in the context of High Level Architecture (HLA), where it is at the core of the Data Distribution Management (DDM) service. Several realizations of the DDM service have been proposed; however, many of them are either inefficient or inherently sequential. We propose a parallel version of the Sort-Based Matching algorithm for shared-memory multiprocessors. SortBased Matching is one of the most efficient serial algorithms for the DDM problem, but is quite difficult to parallelize because of data dependencies. We describe the algorithm and compute its asymptotic running time; we complete the analysis by assessing its performance and scalability through extensive experiments on two commodity multicore systems based on a dual socket Intel Xeon processor, and a single socket Intel Core i7 processor.

Discrete Event Simulation is a widely used technique that is used to model and analyze complex systems in many fields of science and engineering. The increasingly large size of simulation models poses a serious computational challenge, since the time needed to run a simulation can be prohibitively large. For this reason, Parallel and Distributes Simulation techniques have been proposed to take advantage of multiple execution units which are found in multicore processors, cluster of workstations or HPC systems. The current generation of HPC systems includes hundreds of thousands of computing nodes and a vast amount of ancillary components. Despite improvements in manufacturing processes, failures of some components are frequent, and the situation will get worse as larger systems are built. In this paper we describe FT-GAIA, a software-based fault-tolerant extension of the GAIA/ARTIS parallel simulation middleware. FT-GAIA transparently replicates simulation entities and distributes them on multiple execution nodes. This allows the simulation to tolerate crash-failures of computing nodes, furthermore, FT-GAIA offers some protection against Byzantine failures since synchronization messages are replicated as well, so that the receiving entity can identify and discard corrupted messages. We provide an experimental evaluation of FT-GAIA on a running prototype. Results show that a high degree of fault tolerance can be achieved, at the cost of a moderate increase in the computational load of the execution units.

This paper discusses the need to devise novel strategies to create smart services specifically designed to non-metropolitan areas, i.e. countrysides. These solutions must be viable, cheap an should take into consideration the different nature of countrysides, that cannot afford the deployment of services designed for smart cities. These solutions would have an important social impact for people leaving in these countrysides, and might slow down the constant migration of citizens towards metropolis. In this work, we focus on communication technologies and practical technological/software distributed architectures. An important aspect for the real deployment of these “smart shires” is their simulation. We show that “priority-based broadcast” schemes over ad-hoc networks can represent an effective communication substrate to be used in a software middleware promoting the creation of applications for smart shire scenarios.

This paper discusses on the need to focus on effective and cheap communication solutions for the deployment of smart services in countrysides. We present the main wireless technologies, software architectures and protocols that need to be exploited, such as multihop, multipath communication and mobility support through multihoming. We present Always Best Packet Switching (ABPS), an operation mode to perform network handover in a seamless way without the need to change the current network infrastructure and configuration. This is in accordance with the need of having cheap solutions that may work in a smart shire scenario. A simulation assessment confirms the effectiveness of our approach.

Identifying intersections among a set of d-dimensional rectangular regions (d-rectangles) is a common problem in many simulation and modeling applications. Since algorithms for computing intersections over a large number of regions can be computationally demanding, an obvious solution is to take advantage of the multiprocessing capabilities of modern multicore processors. Unfortunately, many solutions employed for the Data Distribution Management service of the High Level Architecture are either inefficient, or can only partially be parallelized. In this paper we propose the Interval Tree Matching(ITM) algorithm for computing intersections among d-rectangles. ITMis based on a simple Interval Tree data structure, and exhibits an embarrassingly parallel structure. We implement the ITM algorithm, and compare its sequential performance with two widely used solutions(brute force and sort-based matching). We also analyze the scalability of ITM on shared-memory multicore processors. The results show that the sequential implementation of ITM is competitive with sort-based matching, moreover, the parallel implementation provides good speed upon multicore processors.

In this paper we argue that the set of wireless, mobile devices (e.g., portable telephones, tablet PCs, GPS navigators, media players) commonly used by human users enables the construction of what we term a “digital ecosystem”, i.e., an ecosystem constructed out of so-called “digital organisms” (see below), that can foster the development of novel distributed services. In this context, a human user equipped with his/her own mobile devices, can be though of as a “digital organism” (DO), a subsystem characterized by a set of peculiar features and resources it can offer to the rest of the ecosystem for use from its peer DOs. The internal organization of the DO must address issues of management of its own resources, including power consumption. Inside the DO and among DOs, peer-to-peer interaction mechanisms can be conveniently deployed to favor resource sharing and data dissemination. Throughout this paper, we show that most of the solutions and technologies needed to construct a digital ecosystem are already available. What is still missing is a framework (i.e., mechanisms, protocols, services) that can support effectively the integration and cooperation of these technologies. In addition, in the following we show that that framework can be implemented as a middleware subsystem that enables novel and ubiquitous forms of computation and communication. Finally, in order to illustrate the effectiveness of our approach, we introduce some experimental results we have obtained from preliminary implementations of (parts of) that subsystem.

We model peer-to-peer live streaming as a multistage congestion game where certain strategy restrictions allow, at equilibrium, to minimize both streaming duration and congestion. We also propose a distributed algorithm (ConGaS) that can be easily executed at peers, enabling them to coordinate toward streaming optimization. Finally, ConGaS is compared against two other dissemination policies through experimental evaluation, and simulations confirm the viability and efficacy of the former.

In this paper, we presentWiFra, a new framework for the detailed simulation of very large-scale wireless networks. WiFra is based on the parallel and distributed simulation approach and provides high scalability in terms of size of simulated networks and number of execution units running the simulation. In order to improve the performance of distributed simulation, additional techniques are proposed. Their aim is to reduce the communication overhead and to maintain a good level of load-balancing. Simulation architectures composed of low-cost Commercial-Off-The-Shelf (COTS) hardware are specifically supported by WiFra. The framework dynamically reconfigures the simulation, taking care of the performance of each part of the execution architecture and dealing with unpredictable fluctuations of the available computation power and communication load on the single execution units. A fine-grained model of the 802.11 DCF protocol has been used for the performance evaluation of the proposed framework. The results demonstrate that the distributed approach is suitable for the detailed simulation of very-large scale wireless networks.

This paper illustrates the effects of the Hyper- Threading processor technology on the runtime performance of a parallel and distributed simulation middleware. A preliminary analysis of the middleware design and execution parameters is given to identify the tuning parameters and to evaluate the scalability of parallel simulation. A real testbed scenario has been illustrated, based on the ART'S parallel and distributed simulation middleware. The experimental analysis has provided some interesting guidelines about the way to adapt the parallel and distributed simulation middleware to Hyper-Threading and to increase the execution speed of the simulation.

The distributed and the Grid Computing architectures for the simulation of massively populated wireless systems have recently been considered of interest, mainly for cost reasons. Solutions for generalized proximity detection for mobile objects is a relevant problem, with a big impact on the design and the implementation of parallel and distributed simulations of wireless mobile systems. In this paper, a set of solutions based on tailored data structures, new techniques and enhancements of the existing algorithms for generalized proximity detection are proposed and analyzed, to increase the efficiency of distributed simulations. The paper includes the analysis of computation complexity of the proposed solutions and the performance evaluation of a testbed distributed simulation of ad hoc network models. Recent works have shown that the performance of distributed simulation of dynamic complex systems could benefit from a runtime migration mechanism of model entities, which reduces the communication overheads. Such migration mechanisms may interfere with the generalized proximity detection implementations. The analysis performed in this paper illustrates the effects of many possible compositions of the proposed solutions, in a real testbed simulation framework.

In this work we define and test a new framework obtained as the integration of two recently developed middlewares defined to support the parallel and distributed simulation of large scale, complex and dynamically interacting system models (like wireless and mobile network systems). In a distributed simulation of highly interacting system models, the main bottleneck may become the communication and synchronization required to maintain the causality constrains between distributed model components. We designed and implemented the ARTÌS middleware as a new framework incorporating a set of features that allow an adaptive optimization of the communication layer management in a distributed simulation scenario. ARTÌS has been integrated with GAIA, a dynamic mechanism for the runtime management and adaptive allocation of model entities in a distributed simulation. By adopting a runtime evaluation of causal bindings between model entities GAIA adapts the dynamic and time-persistent causal effects of model interactions to dynamic migration of model entities. Preliminary results demonstrate that the combined effect of ARTÌS management and GAIA heuristics leads to a significant reduction in the communication and synchronization overheads between the physical execution units. Simulation performance enhancements have been obtained also in worst-case modelling assumptions and simulation scenarios.

The simulation of ad hoc and sensor networks often requires a large amount of computation, memory and time to obtain significant results. The parallel and distributed simulation approach can be a valuable solution to reduce the computation time, and to support model components' modularity and reuse. In this work we perform a testbed evaluation of a new middleware for the simulation of large scale wireless systems. The proposed middleware has been designed to adapt and to scale over a heterogeneous distributed execution infrastructure. To realize a testbed evaluation of the considered framework we implemented and investigated a set of wireless systems' models. Specifically, we identified two classes of widely investigated wireless models: mobile ad hoc, and static sensor networks. In this work we present the performances of the simulation framework, with respect to the heterogeneous set of execution architectures, and the modeled systems' characteristics. Results demonstrate that the framework leads to increased model scalability and speed-up, by transparently adapting and managing at runtime the communication and synchronization overheads, and the load balancing.