This paper proposes a model-based rejuvenation approach that embeds rejuvenation strategies into a stochastic attack–defense model aligned with the Cyber Kill Chain phases. It enables phase-aware reasoning to determine how system refreshes can effectively shorten attacker dwell time and reduce overall exposure. A continuous-time Markov chain (CTMC) jointly captures adversary progression and defender-triggered rejuvenation. The framework links system and service level objectives (SLO) to assess availability and security. Rejuvenation is treated as a timer optimization problem, looking for the refresh rates that satisfy SLO constraints while balancing risk reduction against refresh overhead. Grounded in the classical notion of rejuvenation as periodic return to a known-good state, the proposal demonstrates, through a numerical case study on SQL injection, that tuned timers can bound attacker opportunity windows and improve availability and security, meeting system and service SLO.

Despite the empirical success of self-supervised learning (SSL), the geometric structure of the representations it learns remains insufficiently understood, particularly for 3D point clouds. We present a controlled geometric and spectral analysis of contrastive and asymmetric SSL paradigms under identical architectural and training conditions. Rather than focusing solely on downstream accuracy, we examine intrinsic properties of the learned embeddings through alignment–uniformity trade-offs, class-aware separation, effective dimensionality, spectral decay, feature redundancy, and robustness under controlled geometric perturbations. Our analysis reveals consistent structural differences between objectives: contrastive learning produces more uniformly distributed and semantically organized embeddings, whereas asymmetric methods enforce stronger positive alignment at the cost of reduced global separation and increased redundancy. Robustness experiments further show distinct stability behaviors when generalizing to unseen categories under geometric noise. These results demonstrate that latent space geometry, beyond benchmark performance alone, critically shapes semantic organization and robustness in self-supervised 3D representation learning.

Event cameras are increasingly considered for humanoid human-robot interaction (HRI) because their asynchronous output can preserve fast facial micro-dynamics under motion blur and difficult illumination. The field lacks standardized protocols, covering sensor settings, event integration windows, and evaluation metrics, which limits quantitative comparison across studies and makes robot-mounted transfer uncertain. This PRISMA-ScR scoping review consolidates the emerging evidence through a six-layer taxonomy, spanning sensing to deployability. Across the charted literature, support is strongest for region of interest (ROI)/landmark front-end components and paired-capture supervision. In contrast, humanoid-critical factors, ego-motion quantification, still-user low-motion “signal starvation,” motion-generalization testing, and end-to-end timing, are rarely operationalized as explicit axes. We translate these gaps into a minimal benchmark and on-humanoid dataset direction centered on Facial Action Units and operational engagement proxies, with paired RGB-event capture, logged motion metadata, and reproducible reporting of integration settings and latency.

Speech Emotion Recognition (SER) has achieved significant progress in widely studied languages such as English, driven by the availability of large-scale benchmark datasets. However, research on Italian remains limited due to the scarcity of recent and complementary emotional speech corpora. This paper investigates a feature-based transfer learning approach for Italian SER using audio models pre-trained on AudioSet. Experiments are conducted on two recent Italian corpora with complementary characteristics: AI4SER, recorded in controlled laboratory conditions, and Emozionalmente, developed through crowdsourcing and characterized by higher acoustic variability. A three-scenario evaluation protocol—within-corpus, cross-corpus, and joint training—is adopted to assess robustness to domain shift. Results indicate that embeddings extracted from deeper and higher-capacity models consistently improve performance and robustness. However, significant degradation is observed in cross-corpus settings, highlighting the strong impact of dataset-specific characteristics. Joint training partially mitigates this effect but does not fully eliminate the generalization gap. These findings provide insights into transfer learning effectiveness for Italian SER under heterogeneous recording conditions.

This work presents the design and evaluation of a LoRaWAN-based IoT architecture for wildfire monitoring in rural and forested environments. The proposed system offers a solution for the elaboration of data gathered in the forested area, to support fire prevention and firefighting duties on the edge. To cope with this goal, the solution relies on resourceconstrained sensor nodes and LoRaWAN gateways to enable reliable data transmission under challenging conditions, including dense vegetation and limited communication infrastructure. The study models and analyzes the communication dynamics of largescale LoRaWAN deployments using Generalized Stochastic Petri Nets (GSPNs), capturing the impact of multiple transmission channels and varying traffic loads characterizing the wildfire monitoring system analyzed. Particular emphasis is placed on assessing network scalability and reliability when a large number of sensor nodes concurrently transmit environmental data. The findings contribute to the development of efficient and resilient IoT-based solutions for environmental monitoring and emergency response in remote and resource-constrained scenarios.

Sustainable urban planning manages cities to protect the environment and uses resources wisely for the needs of urban daily life. The Internet of Things (IoT) optimizes the usage of resources and the consideration of urban utilities, such as congestion and pollution. Furthermore, Blockchain supports transparent data collection and securely storing data. Immutability is a fundamental feature of Blockchain, which keeps the data unchanged; however, researchers have proposed mutable Blockchains for data modifications by putting away security. This paper studies mutable Blockchains for sustainable IoT urban planning. It aims to discuss the productivity of mutable Blockchain in IoT-driven Sustainable Urban Planning. To this end, it discusses the challenges of mutable blockchains, presents the analytical modeling in practical applications, and examines the security of mutable Blockchains in public and centralized private Blockchains. Our main goal is to discuss their potential and limitations in IoT-based urban planning environments. The analysis shows that mutable Blockchains reduce computational costs but increase security risks. Our study explains the need for carefully governed mutability to support privacy and adaptability in IoT-based urban planning.

In the realm of audio signal processing, distinguishing between music and speech poses a significant challenge due to the nuanced similarities and complexities inherent in both domains. This study delves into this challenge by employing deep learning techniques to classify audio segments as either music or speech. Our approach involves utilizing the VGGish architecture and Mel-spectrograms as input to provide a rich representations of audio signals. These representations serve as inputs to our classification models, enabling us to discern intricate patterns characteristic of music and speech. We explore the efficacy of our models in this classification task, particularly focusing on their performance in various windowed audio segments. Through rigorous experimentation and evaluation, we observe notable results. Models exhibit remarkable accuracy, exceeding $96\%$ in distinguishing between music and speech. These findings underscore the effectiveness of deep learning models in discerning between music and speech. This work contributes to the understanding of deep learning applications in audio signal processing.

Sleep disorders are continuously growing in the population and can have a significant negative impact on everyday life. Economic and non-invasive systems able to support the diagnosis procedure will be more and more adopted in the next years. The aim of this work is to investigate the classification performance of a convolutional neural network, based on a VGG structure, to identify obstructive sleep apnea events. A recently developed dataset containing audio signals recorded from high-quality contact microphones placed on the trachea of the subjects under study has been adopted to perform transfer learning over a pre-trained VGGish network. Spectrogram images have been extracted from the audio signals to serve as inputs for the classification process. The importance of the time window selection has been also investigated and comparisons with other recent methods proposed in the literature are reported.

Ensuring confidentiality and integrity on data in Cloud computing is still a big challenge. With the advent of the 'Mobile Cloud Computing (MCC)", confidentiality and integrity issues has reemerged inheriting all the limitations introduced by the use of the mobile devices. ARIANNA is an Android application representing the software enabler which allows to extend the SSME Cloud Service, the experimental multi-Cloud system deployed and maintained at the "Cloud Data Center - University of Messina", towards the mobile world represented by the smart devices. In this paper, we present a quantitative performance analysis comparing some commercial Cloud storage services such as Google Drive, Dropbox and OpenStack Swift, with the multi-Cloud approach enabled by the ARIANNA application. In order to evaluate "how much" the overhead introduced by ARIANNA approach costs, we conducted several experiments taking into account the mobile application in real and dynamic multi-Cloud scenarios. The paper discusses some consideration about the actual adoptability of this approach in real application domain and some software and architectural improvements to further improve ARIANNA's performance.

Technology is profoundly changing the way we live. If on one hand Cloud computing has transformed today's computing landscape, on the other hand the spread of mobile devices is changing our everyday life. Because the Cloud infrastructures perform intensive computing tasks and offers unlimited data storage, people and companies started benefit from engaging mobile apps that would not otherwise be possible. For these reasons, in order to maintain the competitive advantage, companies are now deciding to adopt mobile multi-Cloud strategies for their products and services. In this work we propose a mobile secure storage approach primarily oriented to guarantee confidentiality and integrity issues concerning data stored on smart devices which are part of a multi-Cloud environment. In this paper we presented and discussed our Android app, we called "ARIANNA". It enables and integrates our experimental multi-Cloud framework, which description is present in literature. Moreover, in order to evaluate our proposal, we conducted several experiments by considering the mobile application in a real multi-Cloud environment scenario.

Nowadays, we can talk about the Internet of Things (IoT) and Cloud computing union to indicate a new generation of distributed system. An IoT-Cloud system mainly consists of a set of smart objects which are interconnected through the Internet with a remote Cloud infrastructure, platform, or software. It allows to achieve new benefits in several contexts, such as smart cities and industrial businesses. In this paper we propose a context-aware strategy focused on the correct use of the IoT-Cloud services. In particular, a use case driven scenario and a three-step algorithm to define the strategy are presented and discussed.

Cloud computing infrastructures provide services to a wide number of users whose behavior can deeply change at the occurrence of particular events. To correctly handle such situations a cloud infrastructure have to be reconfigured in a way that does not cause degradation in the overall performance. Otherwise, the quality of service specified in the service level agreement could be violated. To prevent such situations, the infrastructure could be organized as an autonomic system where self-adaptation and self-configuration techniques are implemented. Appropriate design choices become important in order not to fail in this goal. We propose a technique, based on a Petri net model and a specific analytical analysis approach, to represent Infrastructure-as-a-Service (IaaS) systems in the case in which the load conditions can suddenly change and reactive autonomic management techniques are applied to mitigate the consequences of the change. The model we propose is able to appropriately evaluate performance metrics in such critical situations making it suitable as a design tool for IaaS cloud systems.

Summary Performance and dependability evaluation plays a key role in the design of a broad range of systems, especially when strict requirements need to be met. This is particularly challenging in distributed contexts, where several components may interact among themselves by influencing each other. In this paper, we present an analytical method that allows the study of a class of systems where different operating conditions alternate by changing the stochastic behavior of the system components but still preserving the continuity of the performance and dependability quantities to investigate. The proposed solution technique, based on phase type distributions, Kronecker algebra, and ad‐hoc fitting algorithms, can be applied for the analytical evaluation of a wide class of distributed systems. Examples are provided to show the usefulness and the applicability of the methodology, characterizing and investigating different performance and dependability aspects of three distributed computing systems, that is, a connection‐oriented network, an Internet of Things application, and an Infrastructure‐as‐a‐Service Cloud. Copyright © 2014 John Wiley & Sons, Ltd.

In recent years, Storage as a Service Cloud gained popularity among both companies and private users. However, security and interoperability issues still have to be adequately faced and solved. In this work, we propose a simple, secure, and privacy-preserving architecture for inter-Cloud data sharing. The proposed solution relies on open standards for both sharing and communication mechanisms, thus ensuring durability, robustness, and compatibility of the approach in the current Internet.

Cloud computing is a promising paradigm able to rationalize the use of hardware resources by means of virtualization. Virtualization allows to instantiate one or more virtual machines (VMs) on top of a single physical machine managed by a virtual machine monitor (VMM). Similarly to any other software, a VMM experiences aging and failures. Software rejuvenation is a proactive fault management technique that involves terminating an application, cleaning up the system internal state, and restarting it to prevent the occurrence of future failures. In this work, we propose a technique to model and evaluate the VMM aging process and to investigate the optimal rejuvenation policy that maximizes the VMM availability under variable workload conditions. Starting from dynamic reliability theory and adopting symbolic algebraic techniques, we investigate and compare existing time-based VMM rejuvenation policies. We also propose a time-based policy that adapts the rejuvenation timer to the VMM workload condition improving the system availability. The effectiveness of the proposed modeling technique is demonstrated through a numerical example based on a case study taken from the literature.

WS-BPEL language has become the industrial standard to design and orchestrate modular applications, formalizing service compositions and business relationships among providers and consumers. Once service level agreements (SLAs) among the parties are established, effective tools for evaluating appropriate measurements have to be developed to meet the requirements. However, the design of quality of service (QoS)-guaranteed composed Web services (WSes) still requires several efforts. This work aims at proposing a complete method to study the QoS of a composed WS at design time, i.e., when the process is specified by using WS-BPEL. Starting from the nonfunctional properties of the WS to compose, we propose a technique to derive non-Markovian stochastic Petri net (NMSPN) models from WS-BPEL processes, with the final goal of evaluating parameters such as the service time distribution and the service reliability. To demonstrate the effectiveness of the proposed method and to validate the obtained model, a nontrivial example implementing a travel agency flight reservation process, exposed as a synchronous composed WS, is investigated.

Abstract In the last few years, remarkable efforts have been made to extend the Grid paradigm to commercial solutions. Business‐oriented grids call for effective Quality of Service strategies able to adapt to different user requirements and to address Service Level Agreements. Performance analysis and prediction with respect to different load conditions or management policies are required to define such strategies. However, the highly distributed nature of Grid systems and the presence of distinct administrative domains make it difficult to carry out performance estimations. In fact, several parameters are involved and the autonomy of each site could make it complex to set them in a proper way. In this paper, we present a non‐Markovian Stochastic Petri Net methodology that allows to conduct performance analysis of Grid systems focusing on aspects related to the Virtual Organization as a whole. In particular, different job allocation techniques can be evaluated with respect to both user and provider points‐of‐view. The influence of different information update policies on the accuracy of the allocation schemes can also be investigated, highlighting the costs/benefits in terms of job waiting time, service availability, and system utilization. The proposed methodology is designed to be as general as possible and it can be applied to analyze a gLite Grid infrastructure taken as case study. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we present an evaluation methodology to validate the performance of a UML model, representing a software architecture. The proposed approach is based on open and well-known standards: UML for software modeling and the OMG Profile for Schedulability, Performance, and Time Specification for the performance annotations into UML models. Such specifications are collected in an intermediate model, called the Performance Context Model (PCM). The intermediate model is translated into a performance model which is subsequently evaluated. The paper is focused on the mapping from the PCM to the performance domain. More specifically, we adopt Petri nets as the performance domain, specifying a mapping process based on a compositional approach we have entirely implemented in the ArgoPerformance tool. All of the rules to derive a Petri net from a PCM and the performance measures assessable from the former are carefully detailed. To validate the proposed technique, we provide an in-depth analysis of a web application for music streaming.

Cloud computing is the "new hot" topic in IT. It combines the maturity of Web technologies (networking, APIs, semantic Web 2.0, languages, protocols and standards such as WSDL, SOAP, REST, WS-BPEL, WS-CDL, IPSEC, etc.), the robustness of geographically distributed computing paradigm (Network, Internet and Grid computing) and self-management capabilities (Autonomic computing), with the capacity to manage quality of services by monitoring, metering, quantifying and billing computing resources and costs (Utility computing). Those have made possible and cost-effective for businesses, small and large, to completely host data- and application-centers virtually... in the Cloud. Our idea of Cloud proposes a new dimension of computing, in which everyone, from single users to communities and enterprises, can, on one hand, share resources and services in a transparent way and, on the other hand, have access to and use such resources and services adaptively to their requirements. Such an enhanced concept of Cloud, enriching the original one with Volunteer computing and interoperability challenges, has been proposed and synthesized in Cloud@Home. The complex infrastructure implementing Cloud@Home has to be supported by an adequate distributed middleware able to manage it. In order to develop such a complex distributed software, in this paper we apply software engineering principles such as rigor, separation of concerns and modularity. Our idea is, starting from a software engineering approach, to identify and separate concerns and tasks, and then to provide both the software middleware architecture and the hardware infrastructure following the hw/sw co-design technique widely used in embedded systems. In this way we want to primarily identify and specify the Cloud@Home middleware architecture and its deployment into a feasible infrastructure; secondly, we want to propose the development process we follow, based on hardware/software co-design, in distributed computing contexts, demonstrating its effectiveness through Cloud@Home.

Service Oriented Architecture (SOA) is the most important and effective software paradigm to design Internet-based services. Using the SOA technology, value-added services can be easily deployed as a combination of existing Web services. In this context, WS-BPEL language has become the SOA industrial standard. To allow services to be composed, business relationships between providers and consumers have to be adequately managed. This implies that a formal definition of Quality of Service (QoS) is agreed and that effective tools for its measurement have to be developed. However, the design of QoS guaranteed composed Web services still requires several efforts due to the highly distributed nature of such software applications. This work aims at proposing a methodology to evaluate Web service performance at the earliest design phase. We present a novel technique to translate WS-BPEL processes into non-Markovian stochastic Petri nets with the final goal to evaluate parameters such as service time distribution and service reliability. The obtained model can be numerically solved through automatic tools, allowing to investigate the service behavior under different operating conditions and thus helping software engineers to develop QoS-guaranteed software solutions.

Computing systems are becoming the heart of modern technology, implementing critical tasks usually demanded to and implying human interactions. This highlights the problem of dependability in computer science contexts. High availability computing/clusters is a possible solution in such cases, implementing standby redundancy as a trade-off between dependability and costs. From the engineering perspective, this implies the use of specific techniques and tools for adequately evaluating the reliability/availability of high availability clusters, also taking into account dependencies among nodes (standby, repair, etc.) and the effect of wear and tear into such nodes, especially when failure and repair times are not exponentially distributed. The solution proposed in this paper is based on the use of phase type distributions and Kronecker algebra. In fact, we represent the reliability and maintainability of each component by specific phase type distributions, whose interactions describe the system availability. This latter is thus modeled by an expanded Markov chain expressed in terms of Kronecker algebra in order to face the state space explosion problem of expansion techniques and to represent the memory policies related to the aging process. More specifically, the paper firstly details the technique and then applies it to the evaluation of a standby redundant system representing a high availability cluster taken as example with the aim of demonstrating its effectiveness. Moreover, in order to show the potentiality of the technique, different maintenance strategies are evaluated and therefore compared.

Grid Computing supports the shared and coordinated use of several resources in dynamic Virtual Organizations. In the last few years, it is evolving into a business-innovating technology that is driving commercial adoption. Such a new scenario calls for powerful strategies able to guarantee stringent QoS requirements in order to meet Service Level Agreements (SLAs) between customers and providers. For this reason, it is necessary to analyze and predict performance with respect to different load conditions or management strategies. In this paper, we present a methodology to analyze performance in gLite Grids through the use of Generalized Stochastic Petri Nets (GSPNs). We introduce a cluster-level model of a typical gLite site taking into account the coexistence between normal and MPI-based jobs. We investigate the influence of different strategies (e.g., scheduling) on the performance of the whole site, highlighting aspects related to both customer and provider point of views. We also provide a business-oriented performance analysis introducing two different SLA typologies and highlighting how the site configuration may influence the expected profit of the service provider.

The spread of worldwide networks and the technological trend are feeding the progress of network and distributed computing in different directions (grid, cloud, autonomic, ubiquitous, pervasive, volunteer, etc). With regard to information, great amount of data widely (geographically) spread over the network require adequate management, to ensure availability for authorized users only, confidentiality and integrity of information and data or, summarizing, security.In order to adequately address security problems such as insider attacks and identity thefts in network-distributed environments, in this work we propose a lightweight cryptography algorithm, combining the strong and highly secure asymmetric cryptography technique with the symmetric cryptography. The algorithm we propose implements a whole secure file system, which preserves and ensures the security of both data and file system structures (directory, links, etc). In the paper we describe in detail the secure distributed file system structure and the algorithms implementing its interface operations.In order to demonstrate the effectiveness of the proposed approach, we also describe its implementation into a Grid (gLite) environment.

Wireless sensor networks are a new emerging area where swarm intelligence can be applied with interesting implications. In fact, a strong analogy between unicellular organism colonies and wireless sensor networks can be emphasized: a sensor network can be viewed as a ldquocolonyrdquo of simple, scarce resource nodes that, autonomously, are able only to perform simple tasks, but all together can accomplish very complex problems. In this paper we propose a routing protocol for multi-sink Wireless Sensor Networks with interesting properties: self organization, fault tolerance and environmental adaptation. The proposed protocol was inspired by the well known behavior (in artificial life studies) of ldquoSlime Moldrdquo. Such colony of unicellular organisms organizes itself in clusters by pheromone generation and evaporation mechanisms. In a similar manner our protocol manages the data traffic towards multiple sink nodes using the gradient concept and reaching high levels of autonomy and fault tolerance. We analyze the proposed protocol to examine the performances, the signalling overhead and the adaptation properties to environmental changes or nodes faults using simulation techniques.

Only commercial cloud solutions have been implemented so far, offering computing resources and services for renting. Some interesting projects, such as Nimbus, OpenNEbula, Reservoir, work on cloud. One of their aims is to provide a cloud infrastructure able to provide and share resources and services for scientific purposes. The encouraging results of volunteer computing projects in this context and the flexibility of the cloud, suggested to address our research efforts towards a combined new computing paradigm we named [email protected] one hand it can be considered as a generalization of the @homephilosophy, knocking down the barriers of volunteer computing, and also allowing to share more general services. On the other hand, Cloud@Home can be considered as the enhancement of the grid-utility vision of cloud computing. In this new paradigm, userspsila hosts are not passive interface to cloud services anymore, but they can interact (free or by charge) with other clouds. In this paper we present the Cloud@Home paradigm, highlighting its contribution to the actual state of the art on the topic of distributed and cloud computing. We detail the functional architecture and the core structure implementing such paradigm, demonstrating how it is really possible to build up a Cloud@Home infrastructure.

Abstract WebSPN is a modeling tool for the analysis of non‐Markovian stochastic Petri nets that we developed some years ago. Its solution algorithm is based on a discretization of time and an approximation of non‐exponentially distributed firing time transitions by means of the phase‐type distributions. In order to solve the problems related to the management of the state space (which can become very large) we parallelized the solution algorithm through the MPICH libraries. To improve the MPICH parallel implementation with an adequate security management, a more efficient load distribution and fault‐tolerance capabilities, in this paper we propose the porting of WebSPN from the MPI to the Grid computational paradigm. In addition to a better flexibility in accessing computational and storage resources, one of the main advantages is the introduction of a fault recovery system to detect and recover from potential machine faults. The resulting new tool is named GridSPN. Copyright © 2006 John Wiley & Sons, Ltd.

Nowadays the great part of devices or systems we commonly use are often driven or managed by microchips and computers: cars, music players, phones, trains, planes, .... A consolidated trend of technology is to substitute mechanical with electronic parts, analogical with digital devices or controls, and so on. In this context, features like security, availability and reliability, usually summarized under the concept of dependability, are receiving higher attention. The dependability analysis, especially for what regards critical parts as computing systems or subsystems, is becoming more strategic: specific requirements and explicit or tighter constraints have to be satisfied. Even though this fact, there is a lack of suitable tools to properly model and analyze these aspects, with particular reference to reliability. To fill this gap, we propose the dynamic reliability block diagram (DRBD) modeling tool derived from the reliability block diagram (RBD) formalism. The DRBD permits to model the dynamic reliability behavior of a system through dependence models, exploited to represent dynamics behaviors as redundancy, load sharing, multiple, probabilistic and common failure mode. In this paper, the DRBD expressiveness and other capabilities, are illustrated through the analysis of a complex distributed computing system taken as example

This paper proposes a methodological approach for the performance analysis of Web-based searching applications on the Internet. It specifically investigates the behavior of the client/server (C/S), remote-evaluation (REV) and mobile-agent (MA) communication paradigms and describes how Petri-net models can be developed to derive performance indices which can help the designer to improve the efficiency of his distributed applications. Our purpose is that of identifying a set of models that can help to understand the environmental situations in which such paradigms should be preferred or combined in order to optimize the performances of a distributed system. In particular, we propose a modeling technique applied to an information retrieval application on the World Wide Web. An analytical evaluation through the solution of nonMarkovian Petri-net models is provided, which allows us to identify the main parameters, as well as the way they interact, to be taken into consideration when distributed applications are to be designed. An experimental environment is also studied in order to obtain real measurements used to validate the analytical models.

This paper wishes to investigate the converging field of mobile and Grid computing by defining an architecture for the provision of Grid services, which Is based on standards, robust and useful across application domains. We propose using the mobile agent paradigm in order to develop a middleware layer that takes care of all the details to allow mobile users to access distributed resources in a transparent, secure and effective way. Our purpose Is also that of Identifying the environmental situations In which such paradigm should be preferred or adopted in conjunction with more traditional communication paradigms (i.e. client/server, Remote Evaluation). For this purpose, we provide an experimental and analytical evaluation of the Client-Server, Remote Evaluation and Mobile Agent communication paradigms.

This paper provides an experimental and analytical evaluation of the client-server, remote evaluation and mobile agent communication paradigms. Our purpose is that of identifying the environmental situations in which such paradigms should be preferred or combined in order to optimize the performances of a distributed system. The scenario selected for the analysis is information retrieval on the WWW. An analytical evaluation throughout the solution of non-Markovian Petri net models is provided.

Abstract In this paper we deal with the study of the actual convenience of using the agent programming paradigm for accesssing distributed service. We try to point out the benefits of such a communication paradigm, by providing an analytical study of its basic features in comparison with the client–server approach and remote evaluation. The aim of the paper is to show how the Petri net analysis technique can be used for deciding whether to use traditional client/server, remote evaluation or mobile agents paradigm in designing a particular evaluation. So, we present several models of non‐Markovian Petri nets, which have been solved through the WebSPN tool, and we provide a close comparison between the agents technique, the client‐server and the remote evaluation communication paradigm. The results that we have obtained show how agents must not always be considered the only solution to any communication issue, since in several cases their use might even reveal a drawback. We also focus out attention on providing some practical remarks, which can help the developer during the design in order to select the communication paradigm which best suits the features of the application that has to be developed. Copyright © 2001 John Wiley & Sons, Ltd.

The recent development of high-speed communication networks has led to great interest in client–server environments. In this context it is becoming increasingly important to have adequate tools to support the study of the behaviour and size of the various parts of these systems. This paper provides a systematic approach to modelling and assessing a particular class of systems—fault-tolerant parallel/distributed systems—which feature a capacity to reconfigure dynamically on the occurrence of partial faults, so as to guarantee a certain level of performance, albeit a reduced one. Using the formalism offered by Petri Nets, the paper presents combined measures of dependability and performance which identify the behaviour of the system in as complete a way as possible, above all in relation to fault/repair phenomena.