Nowadays, smart urban environments face increasing demands by citizens for reliable wireless connectivity to support the adoption of smart services, such as intelligent transportation systems. In this context, the city of Caltanissetta has implemented a comprehensive project focused on providing research and development services to facilitate the integration of such systems. The initiative includes the assessment and validation of LoRaWAN coverage models through empirical field testing, combining advanced simulation methodologies with real-world measurements to enhance the accuracy of propagation models in urban settings. Within this framework, SmartMe has deployed a software ecosystem based on its Stack4Things framework, alongside a LoRaWAN infrastructure, to monitor critical urban parameters such as weather conditions, air quality, park availability, and traffic flows, contributing to the development of smart mobility solutions. The urban deployment consists of a network of Cyber-Physical Systems (CPS) distributed throughout the city, with multiple edge elements contributing to real-time data collection. The validation process involves comparing simulated coverage predictions with field measurements, refining model parameters to optimize network performance and reliability.

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.

The Industrial Revolution changed the picture of industrial systems by making them highly efficient and more manageable. The inclusion of the internet, smart devices, and various protocols has collectively developed Industrial Control Systems (ICS), which are responsible for the entire industrial activity management. However, the devices in ICS may also malfunction and show abnormal behavior, affecting industrial activities. Hence, it is necessary to have a good abnormal condition detection system in ICS, but the presence of a real environment for developing such a detection system is challenging. Therefore, the proposed work addresses this challenge by modeling the ICS to mimic the actual behavior of the industries and collecting the relevant data to train a model for detecting anomalous behavior in the system. The work employed Discrete Event System Specification (DEVS) as a modeling and simulation formalism for designing the ICS. Through this modeling system, the work collects the states associated with every device and trains a machine-learning model to deal with anomalies in ICS further. This work also aims to show the significance of the DEVS formalism in modeling the ICS and how its state-based data may be used further to make an artificial intelligence-oriented solution.

Urban traffic is one of the most important issues for smart cities, and real-time management is critical for improving mobility and reducing congestion. Traditionally, classification methods based on machine learning need frequent retraining, which is inefficient for adaptive traffic management systems. This work introduces a novel hybrid approach that combines Incremental Learning (IL) with Federated Learning (FL) techniques to support continuous model adaptation without centralizing data or restarting the training process from scratch. The proposed approach employs Convolutional Neural Networks (CNNs) to classify traffic conditions from junction camera feeds and includes a specific mechanism to mitigate the Catastrophic Forgetting (CF) issue, a common drawback in IL. This solution enhances model performance in a decentralized way while protecting data privacy and encouraging knowledge sharing across distributed nodes. Experimental results on a publicly available dataset reveal that this approach significantly improves dynamic traffic management, achieving over 96% validation accuracy throughout the IL process for multiple clients, with minimal loss and no need to centralize data. This work sets the basis for a more effective and secure traffic management infrastructure in smart cities.

The rapid evolution of industrial environments, driven by the proliferation of dynamic devices such as wireless robots and smart sensors, has introduced unprecedented complexity. Navigating this landscape requires not only adapting to internal system changes but also aligning with managers' visions. This paper proposes a new approach that enables real-time reconfiguration of industrial systems. At its core is a modeling framework that captures node-level dependencies, revealing how coordination complexity can exponentially degrade system performance. By leveraging the flexibility offered by virtualization and IT/OT (Information Technology and Operational Technology) infrastructure, the proposed Distributed Model Predictive Control (DMPC) method evaluates a multitude of operational scenarios, allowing the system to adapt dynamically. The workflow culminates in the reconfiguration of Industrial Internet of Things (IIoT) nodes through WebAssembly (WASM), seamlessly bridging the gap between virtual parameters and physical reality. A mathematical process formulation, including an exponential latency model, provides a robust framework that manages complexity while ensuring responsive system behavior.

The rapid expansion of the Internet of Things (IoT) landscape, notably in smart cities, smart metering, environmental monitoring, and smart agriculture, highlights the critical need for seamless and efficient device roaming within Long Range Wide Area Network (LoRaWAN) infrastructures. Although LoRaWAN shows great potential, its deployment faces significant challenges, especially under the constraints of the version 1.0 specifications, to enable efficient device mobility. This study introduces a comprehensive approach to enhance LoRaWAN-supported mobility, using three key advancements: the decoupling of Gateways (GWs) from the core network infrastructures to facilitate flexible deployments, the adoption of an adaptive GW-to-network connection protocol responsive to real-time traffic demands, and the enhancement of GW interoperability to provide dynamic and efficient network configurations. A notable innovation of this research is developing a mechanism that enables packet forwarding through an unidentified GW, eliminating the need for pre-established network agreements. The empirical evaluations of the article detail the effectiveness of the architecture in IoT applications, showcasing significant improvements in LoRaWAN’s roaming capabilities and reporting average latencies of up to 0.39 s in experimental tests. Extensive simulations with up to 100 roaming devices and 100 stationary devices further confirmed scalability, keeping the average roaming-to-stationary latency gap below 0.16 s and the worst-case end-to-end delay below 1.2 s even in highly congested scenarios. This contribution addresses pivotal challenges within the current LoRaWAN networks and sets the background for future advancements in IoT network technologies.

Operational technology (OT) and information technology (IT) are the backbones of modern industrial control systems (ICS). The coupling of IT and OT is significant as it provides various benefits that lead to higher manageability, efficiency, and productivity in industries. However, the presence of an IT network may leads to cyber-security concerns in the OT networking system. Hence, in the emerging and growing field of Industry 4.0, making the system secure from cyber threats is very important. Therefore, the proposed work presents the multi-directional view of ICS concerning cyber-security by discussing possible cyber vulnerabilities and the methods to deal with cyber threats. It also discusses the various industrial protocols, standards, and ICS design frameworks. Last but not least, the work discusses various tools used for network monitoring, security analysis, and penetration testing.

This paper presents a study on the integration of cloud computing with UAV autopilot systems, aiming to significantly enhance operational efficiency, scalability, and autonomy in UAV operations. By leveraging the synergies between the IoT, cloud computing, and sophisticated UAV autopilot technologies, we introduce a novel architectural framework designed to overcome existing challenges in UAV operations, such as advanced mission planning, real-time data analytics, and dynamic resource management. The presented approach emphasizes the integration of the MAVLink protocol and the ROS with the PX4 autopilot system, marking a crucial step towards achieving autonomous UAV operations characterized by enhanced safety and reduced mission execution times. Empirical assessments, supported by detailed operational scenario analyses, demonstrate the effectiveness of this integration, revealing improvements in UAV performance metrics. The outcomes highlight the potential of cloud computing integration with UAV operational paradigms, paving the way for further exploration in autonomous systems and cloud-assisted IoT applications.

This paper describes a novel architecture aiming to create an open-source template for implementing a platform enabling support for the deployment and integration of services and workflows that compose, manage, and continuously evolve a complex system: the Urban Intelligence. More specifically, the proposed Smart City IT architecture is an evolution of a concrete urban intelligence architecture meant mostly to support Data scientist activities and, obviously, the exploitation of Smart City services by citizens and users. The proposed solution, due to its open-source nature, is fully replicable and improves its ancestor by provisioning new characteristics: i) managing facilities of urban Cyber-Physical System via exploitation of a combination of Administrator/Data scientist workflows and IoT management platform, ii) the definition of workflow directly on the Edge (even through the exploitation of FaaS paradigm directly on IoTs), and iii) enhancement of Smart City infrastructure security via the reduction of external attack surface. The prototype of the proposed IT solution has been implemented leveraging open-source frameworks and technologies belonging to the OpenStack ecosystem.

The burgeoning paradigms of Fog and Edge computing propose delegating Cloud-related tasks to the network’s periphery, thus placing computational resources closer to data producers. This shift promises to boost the performance of IoT-based services, providing swift response times while conserving bandwidth. Despite their potential, the current Edge/Fog computing platforms must provide the required flexibility for dynamic service orchestration within a data-oriented context. Addressing this gap, the Function-as-a-Service (FaaS) model emerges as an exceptional strategy for Edge/Fog deployments. Its ability to manage an ever-expanding ecosystem of devices with remarkable flexibility and efficiency holds considerable promise. This paper articulates a novel approach to enhancing the adaptability of IoT Edge/Fog deployments. We propose an innovative extension to OpenStack, an open-source Cloud management system, which pushes its functionality towards the network Edge. Our approach empowers OpenStack to facilitate FaaS services within a distributed IoT infrastructure, thus infusing unprecedented adaptability and efficiency into the Edge/Fog computing paradigms.

To fully exploit the capabilities of the Internet of Things (IoT), it is expected to assemble large-scale networks made of heterogeneous IoT devices (e.g., embedded systems, sensors, and actuators) and smart systems. These devices and systems should interact in a seamless fashion regardless of the underlying protocols. Yet, most of the actual IoT deployments are based on proprietary technologies and tightly coupled systems. To overcome application layer interoperability issues, the Web of Things (WoT) paradigm aims at making smart things an integral part of the Web and, thus, enabling them to interact through Web protocols, in accordance to Web standards. In this paper, we extend our system for WoT meant for associating to the geographically distributed IoT resources publically resolvable URL/URI. In particular, we propose in this work a HATEOAS-enabled mechanism to discover IoT resources available on an IoT node. This approach makes developers, and users alike, able to discover available resources within a node without knowing them beforehand. A use case is also described, in the context of telemedicine.

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This applies also to resource-constrained IoT and edge devices, which will often benefit from relatively small – but smart – local anomaly detection tasks that aim at protecting the device, or the information they convey from sensors towards a central node. This provides the device with fault detection capabilities that are typically required when engineering dependable devices, services or systems. This paper overviews a pitfall-free process to provide small devices with anomaly detection capabilities, to make them self-aware of their health condition, and possibly take appropriate countermeasures. Our methodology applies to a wide range of Linux-based devices: we show an application to a specific ARANCINO device, which has already been successfully used in many smart cities and sensing applications. We craft anomaly detectors that are very effective in detecting most of the anomalies. Additionally, we comment on the beneficial impact of time-series analysis, which could help improve detection performance even further, allowing to equip any small device with responsive and accurate anomaly detection machinery.

The COVID-19 pandemic has emphasized the importance of responsive, efficient, and cost-effective production networks to manufacture essential goods rapidly. The Air Factories 2.0 project is a research-driven initiative designed to withstand the pandemic by leveraging advanced technologies, such as 3D printing, blockchains, and distributed manufacturing, to summon the maker community’s expertise and resources. However, this project’s potential extends beyond the medical field and can be actualized in several domains. This paper comprehensively analyzes the Air Factories 2.0 platform, its technological and scientific contexts, and its potential implications for the future of manufacturing, emergency response, and 3D printing-enabled decentralized supply chains.The study outlines the Air Factories 2.0 project’s operational and organizational structure, stakeholders and roles, participation and tokenization mechanisms, algorithms used to manage production and distribution, blockchains, and smart contracts employed to ensure transparency, security, and efficiency. Furthermore, this paper examines the advantages and limitations of the Air Factories 2.0 platform for the future of advanced manufacturing across various domains.

In industrial wireless sensors networks (IWSNs) deployments, data management and processing tasks are often delegated to centralized computing facilities. Albeit providing an abundance of resources, these solutions impose several drawbacks, stemming from the higher latency they may introduce, as well as from security/privacy issues. To deal with these shortcomings, in this article, we propose an architecture designed for industrial monitoring duties, based on the Fog computing paradigm. In particular, we devised a Fog layer to host a data aggregation algorithm based on singular value decomposition (SVD). Considering a real-world case study about a distributed monitoring system for industrial motors, the benefits of the proposed solution in terms of network performances, i.e., latency and packet delivery ratio, are analyzed using the OMNET++ simulator. Besides, the system implements a set of security mechanisms at different levels (i.e., IWSN, Fog, and Cloud). In particular, we propose a new lightweight and energy-efficient ciphering algorithm for resource-constrained industrial motes. According to our experiments, the new algorithm reduces energy consumption by a factor of three compared to Advanced Encryption Standard-128.

A Smart Factory exploits information and communication technologies (ICT) to improve the production process and the working environment, usually addressing safety concerns. To this concern, factory-grade fire protection systems are governed by several procedures and standards whose application often becomes definitely challenging when the factory premises are dispersed across multiple administrative domains. In such contexts, the Smart Factory approach can prove very effective in the management and coordination of the factory-level fire protection system. However, a catastrophic event may compromise the ICT infrastructure, affecting communication among factory domains and therefore its smart services. A strategy to cope with the latter may be the introduction of mechanisms to handle data analysis on-site for a prompt response while enabling seamless data distribution and processing among neighboring (federated) ICT infrastructures and emergency operators. In this work, a novel software-defined approach for the adaptive management of a Smart Factory infrastructure is proposed, centered around business logic rewiring and reconfiguration at runtime across different factory domains. Thereby, even in the case of catastrophic (e.g., potentially disruptive) events, working devices of the emergency system can go on with their operations, including transferring data to rescuers and others emergency control systems. To demonstrate the effectiveness of the proposed software-defined factory approach, a federated fire protection system operating in an industrial setting is implemented as a case study, able to promptly react and adapt to infrastructure-critical fires and their consequences by leveraging all information and computing facilities pooled over cloud/fog/edge devices spanning the premises.

Intelligent Personal Assistants (IPA) are becoming an essential part of our life. The trend aiming at adopting IPA devices is driven by the uncountable applications enabled by the advent of the Internet of Things (IoT) and its role in home automation. Actually, people are able to use, for example, voice-controlled IPA devices (e.g., Amazon Alexa) to interact and control things in their surrounding environment. Yet, to enable such interactions in an environment with multiple devices, the user has to configure each device. Besides, it is not possible to apply the same operation in a specific environment used for a short period, such as a hotel room, i.e., for security reasons: the owner of a hotel cannot permit access to the devices from their customer as it may be risky. To deal with such a limitation, the adoption of a Software-Defined approach, such as Software-Defined I/O (SDI/O), is a relevant approach to decouple the access to IoT resources from their management duties. In the case of accommodation facilities, such as a hotel room, to connect an IPA to IoT devices, a set of repeated configurations has to be performed. This paper proposes “Everywhere IPA” (EIPA), an approach relying on the Software-Defined I/O approach to automatize the configuration of IoT devices.

The development of Low-Power Wide-Area Networks (LPWAN) has significantly boosted the uptake of Internet of Things technologies, fostering their adoption in such domains as Smart Cities and Industry 4.0. In the context of LPWANs, LoRa is one of the most promising developments, experiencing massive growth in recent years. This situation has led to the steadily growing trend of redundant deployments across locations, or scenarios, where it would be more natural to be able to leverage (e.g., rent) LoRa infrastructure belonging to a third party. Key to reversing this unsustainable trend is roaming approaches. Although the LoRa specifications define mechanisms for roaming, gaps and challenges make roaming difficult to deploy and operate in real-world scenarios. This paper analyzes these concerns and proposes a novel architecture compliant with the LoRa specification to design and accommodate roaming services in a consistent, efficient, and scalable way.

The huge growth in terms of the number of distributed devices connected to the Internet, the so-called Internet of Things (IoT), has led to the expansion of their domain of applicability by enabling advanced and futuristic environments such as the Smart City-like scenarios. In such a use case, a pressing need emerges to cope with the complexity and heterogeneity of the infrastructure by reusing as much tooling as possible without resorting to vertical ad-hoc solutions. Furthermore, having good decision support based on the colossal amount of data (i.e., Big Data) generated by these environments is absolutely critical to make them smart. In fact, the impediment to the smart city concept is to provide efficient aiding decisions whilst using a malleable solutions to meet the information technology evolution. In this context, we introduce in this paper our system for IoT infrastructure management and Big Data processing. In particular, our IoT infrastructure manager middleware Stack4Things (S4T) is integrated with the Elastic Stack (ELK) data management solution. This work thus describes the rationale, efforts, and results so far achieved, for an integration of the IoT paradigm with a Cloud-oriented environment focusing on a Smart City scenario, and featuring data collection and visualization as example use cases of such integration.

Industry 4.0 encourages the integration of intelligent technology with manufacturing systems. Among them, additive manufacturing (AM) is critical to solving some of the fourth industrial revolution's most pressing needs. With AM gaining popularity, the need for the validation of 3D designs grows. In this paper, we introduce a novel concept of a distributed marketplace that will support the attestation of 3D printing designs. We build a mathematical trust model that ensures truthfulness among rational, selfish, and independent agents, which is based on a reward/penalty system. The payment for participating in the evaluation is calculated by factoring in agents' reputations and peer feedback. Moreover, we describe the architecture and the implementation of the trust model on the blockchain using smart contracts for the creation of a distributed marketplace. Our model relies both on theoretical and practical best practices to create a unique platform that elicits effort and truthfulness from the participants. Finally, we present a performance evaluation and cost analysis of the proposed architecture to evaluate scalability and financial viability.

In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.

Air pollution is one of today's most biggest problems, mostly due to greenhouse gas emissions that have increased exponentially since the mid-twentieth century. Today's society is the main culprit due to the emissions caused by the industriaV-manufacturing processes and the different transportation means. In particular, the cities, always affected by traffic congestion and traversed by uncountable vehicles, are crucial points where actuate solutions can mitigate the pollutants generated by the vehicles. The cities, enhanced by the Internet of Things (IoT) advent, are becoming “Smart” thanks to the data generated by IoT devices. The ubiquitous distribution and the possibility of correlating data from different sources (voluntary shared or freeware) have enabled the crowdsourced initiative to realize applications exploiting these data. In this work, a multilevel IoT/Cloud infrastructure lends itself to offering solutions to citizens to solve daily life problems. The architecture, putting in place means to conceive an exciting application, is investigated through different use cases linked to current cities issues: public mobility, free parking slot detection, and air quality monitoring.

The Internet of Medical Things (IoMT), combined with interconnected wearable devices and medical-grade sensors, can play an essential role in healthcare evolution. By exploiting the data generated by the plethora of interconnected devices (vital parameters, location-based info, patients activity and more), advanced ICT systems can be put in place with predicting capabilities. This way potentially critical situations, that may evolve in serious complications to patients’ well-being, can be promptly recognized and successfully addressed, first of all, to save lives and secondarily to limit economical damages. To support continuous patient monitoring in public and private healthcare, this paper proposes "DILoCC", an architecture to manage wearable devices, sensors and applications, that uses a Distributed Incremental Learning (DIL) approach to exploit cooperation among the sensing devices and increase the overall system efficiency through the mitigation of "Catastrophic Forgetting" consequences.

A visit from qualified and authorized personnel to troubleshoot customer-premises equipment (CPE) on-site is an example of a situation where both local wireless connectivity and access to the Cloud may be needed, the former to discover proximal equipment to probe and diagnose upon, the latter to promptly send useful data back to the company providing services to the customer, e.g., without waiting for the repair session to be over and the personnel have left the premises. In such a scenario, the company support service may take advantage of a dynamic coupling and interaction among the Cloud, mobiles (as personnel-borne equipment), and IoT devices (as bolt-on addition to the, possibly legacy, CPE). For this purpose, a software architecture for the collection and transmission of metrics of interest through a combined system, featuring a Cloud instance and a mobile-based agent, has been implemented. The reference architecture is composed of IoT-Cloud and edge/mobile devices, specifically smartphones and embedded boards, the latter equipped with a Cloud-controlled agent as a software component, with the goal to collect useful metrics from hardware devices of an energy substation, as a way to support remote and on-site diagnostics through technologies such as Bluetooth Low Energy. We present the reference architecture, its implementation, and some preliminary results on the effectiveness of the proposed solution.

Smart Building/Environment management is an interesting topic that, although widely investigated in the literature, has not seen wide adoption in real case studies. Current Smart Building solutions provide facilities for: i) automatically managing HVAC (Heating, Ventilation and Air Conditioning) systems; ii) energy management, iii) building automation. Current solutions are tightly coupled with the underlying IT infrastructure of the building and cannot be rearranged according to the events, catastrophic or otherwise, that may modify the building structure (e.g., a disruption to sections of a building). In this work, we present a novel approach for Smart Buildings (we refer to it as "Software Defined Building 2.0") to customize and reprogram IT infrastructure powering buildings, this way enabling cooperation among Cyber-Physical Systems (CPSs) operating within. We analyze an architecture enabling this approach, evaluating overhead management and its impact on systems’ performance.

Summary The Internet of Things is underpinned by the global penetration of network‐connected smart devices continuously generating extreme amounts of raw data to be processed in a timely manner. Supported by Cloud and Fog/Edge infrastructures – on the one hand, and Big Data processing techniques – on the other, existing approaches, however, primarily adopt a vertical offloading model that is heavily dependent on the underlying network bandwidth. That is, (constrained) network communication remains the main limitation to achieve truly agile IoT data management and processing. This paper aims to bridge this gap by defining Clustered Edge Computing – a new approach to enable rapid data processing at the very edge of the IoT network by clustering edge devices into fully functional decentralized ensembles, capable of workload distribution and balancing to accomplish relatively complex computational tasks. This paper also proposes ECStream Processing that implements Clustered Edge Computing using Stream Processing techniques to enable dynamic in‐memory computation close to the data source. By spreading the workload among a cluster of collocated edge devices to process data in parallel, the proposed approach aims to improve performance, thereby supporting agile data management. The experimental results confirm that such a distributed in‐memory approach to data processing at the very edge of an IoT network can outperform currently adopted Cloud‐enabled architectures, and has the potential to address a wide range of IoT‐related data‐intensive time‐critical scenarios.

Thanks to the impact of the advancement in the hardware field, the network size and usage scope of the Internet are continuously growing. Indeed, new smart devices, e.g., sensors, actuators, home appliances are becoming strong enough to communicate and exchange data over the Internet. Accordingly, this distributed ecosystem with sensing/actuation capabilities is introducing new market opportunities with innovative services including, e.g., environmental monitoring, traffic monitoring, homes/buildings control. To conceive new IoT services, enabling the smart devices to join the Internet and expose their capabilities/data through the Web is fundamental. For this purpose, Web Application Programming Interfaces (APIs) or what we refer to also as RESTful Web Services is a paradigm that can enhance the IoT application scope by making smart things part of the Web. In this paper, based on our Stack4Things (S4T) Cloud middleware, we introduce a new approach for exposing services running on IoT devices to the Web so that they become reachable using globally resolvable Uniform Resource Locators (URLs). We emphasized security issues as well by implementing, on the devices, an automated mechanism capable of managing X.509 certificates issuance to enable secure communications using Hypertext Transfer Protocol Secure (HTTPS).

Recently, Industrial Wireless Sensor Networks (IWSN) have gained a lot of interest from both research and industry communities as an evolution of WSN specifically tailored for Industry 4.0 applications and their requirements. Thanks to the set of benefits IWSN technology introduces, it is considered to be a sustainable solution for industrial system monitoring. Yet, this approach has several drawbacks stemming from the limited resources (i.e., compute and storage) available on-board network devices. Although Cloud-based WSN data management solutions are widely adopted, issues related to this approach persist (e.g., high latency, bandwidth consumption, storage costs). This paper introduces an innovative platform enhancing IWSN architectures by adding a processing layer at the network edge: an approach that follows the Fog Computing paradigm. Remote management and programmability of the Fog layer are only some of the most challenging requirements. We show how our approach is suitable for industrial scenarios by applying it to a representative use case, i.e., the monitoring of asynchronous electric motors.

In this work, we propose a low-cost Smart and Healthy Intelligent Room System (SHIRS), able to monitor Indoor Air Quality (IAQ) by enhancing edge-based computation. SHIRS exploits the ability to run Machine Learning (ML) algorithms to infer humans presence (headcount) from environmental data analysis. Experimental results show the validity of the proposed approach, demonstrate the potential of edge-based computing and push towards the adoption of smart integrated Cloud-IoT frameworks for environmental monitoring and control.

Global warming and climate changes are due to several factors, not least vehicle and transportation emissions. Smart City technologies can provide mechanisms for emission (greenhouse gases, particles) containment that may significantly impact on the environment. This paper proposes a solution, based on an intelligent cruise control system, allowing a vehicle to interact with the Smart City infrastructure facilities for cutting down its emissions on the planned route while saving fuel. The proposed approach aims at implementing a (virtually) continuous green wave for a vehicle lowering its emissions by modulating the speed only considering local traffic congestion and traffic light information provided by the Smart City infrastructure, without actuating on the latter. A green-green (green2) wave also reducing the fuel consumption and ensuring a good trade off with travel time. To demonstrate the effectiveness of the proposed solution, a power train model of a c-segment car traveling on while interacting with Smart City facilities has been implemented and evaluated, providing significant insights.

With the widespread emergence of the Internet of Things (IoT), our environment and locations are turning progressively into smart environments ranging from individual houses/offices to schools, factories, and hospitals. Even more interesting, with the rise of Fog/Edge paradigms, the IoT application scope has been extended to provide critical services. By pushing resources such as compute and storage to the network edge, IoT-based services are taking benefits from their proximity to provide better performances. However, albeit an exciting development in and by itself, Edge/Fog computing platforms currently do not provide a convenient level of flexibility and efficiency to support the dynamic composition of services with a data-oriented approach. In this context, the Function-as-a-Service computing paradigm rises as a convenient/suitable paradigm to be adopted in the IoT landscape. For the sake of providing flexible IoT Edge/Fog deployments, this paper introduces a system providing FaaS services based on a distributed IoT infrastructure. Besides, we provide a dashboard based on Node-RED that exploits, in the backend, the FaaS system to make the users able to conceive customized applications using the resources (i.e., sensors and actuators) that the IoT devices can host.

In recent years, a new trend of advanced applications with huge demands in terms of Quality of Service (QoS) is gaining ground. Even though Cloud computing provides mature management facilities with ubiquitous capabilities, novel requirements and workloads, foisted by new services, start to expose its weaknesses. In this context, a new Information and Communication Technologies (ICT) trend aims at pushing computation from the Cloud to be much close as possible to data sources, raising in the evolution of new paradigms namely Fog and Mist computing. Specifically, the Fog computing paradigm exploits powerful nodes such as servers, routers, and cloudlets that are coupled with the end devices or their access networks accordingly; they are ”relatively” close by the data sources. Whereas Mist computing, which is a lightweight form of Fog computing, pushes the resources even closer. Precisely, Mist computing uses particular nodes that could reside within the same network (e.g., Local Area Network (LAN)) as the end-devices. Considering the advancement that the hardware is knowing nowadays, Fog and Mist nodes are seen suitable to provide resources such as processing, storage, and networking in the proximity of data sources; thereby, the requirements of the new services could be met. Together with the Cloud, the Fog and Mist paradigms introduce a stacked architecture for data processing where a data pre-processing could be performed at the Mist level, then offloaded vertically to the upper layers (i.e., Fog nodes or the Cloud). In these circumstances, it is fundamental to build a management system able to provision efficiently the Fog/Mist-based applications. For this purpose, the Operating System (OS)-level virtualization using containerization technologies, considering its light footprint, fits as a suitable solution to provide Fog/Mist services. The industrial-grade Cloud middlewares, such as OpenStack, which is a reference architecture for Infrastructure-as-a-Service solutions, are still far away from incorporating this new trend. This article proposes an OpenStack-based middleware platform through which containers can be deployed/managed at the Fog/Mist levels.

Considering the proliferation of smart devices connected to the Internet, typically going under the aegis of Internet of Things (IoT), a trend has arisen to promote the Cloud paradigm as a suitable management system for such a complex environment. In this context, an effort to extend the OpenStack ecosystem to make it able to support the management of the IoT infrastructure has been made by virtue of the I/Ocloud approach, leading up to its reference implementation, the Stack4Things (S4T) middleware. S4T provides a set of suitable capabilities and features to make the (remote) IoT devices able to join an edge-based IaaS/PaaS Cloud. In the interest of enhancing the S4T middleware scalability and explore new capabilities in particular, ones related to Fog and Edge paradigms, it is becoming a must to test new features in practice at a low financial cost and particular constraints for instance, number/type of devices, network conditions, etc. For this purpose, the use of network emulation tools is a practical and suitable approach. In this paper, we present an integration between the S4T middleware and an emulation tool namely Containernet. Through the integration approach, we model network conditions (e.g., latency, bandwidth, packet loss) and devices (in forms of containers) using Containernet, and we manage the devices (i.e., containers) by means of S4T.

Orchestration is one of the cornerstone facilities in Cloud computing, and it has become critical with the advent of the Software-as-a-Service (SaaS) paradigm. It allows service providers to automatize the deployment of their software in Cloud computing infrastructure, thus making the process swift and scalable. However, trust remains a paramount concern still. In particular, transparency, provenance, and security present significant challenges for SaaS. Blockchain possesses the needed qualities to address these challenges. We propose a system capable of creating transparent, provenance-assured, and secure SaaS. The proposed solution addresses these concerns by dividing the orchestration process, i.e., the SaaS deployment, into self-contained steps, each of which is related to a specific domain, e.g., networking, access rules. The input for these steps, and the output of their execution, are both recorded into the blockchain, creating a trail of trust. An external user requiring to validate the orchestration process can query the blockchain. In this way, if the (infrastructure) Cloud provider is fully trusted, end users do not need to trust the service provider: the SaaS deployment becomes fully transparent, and the provenance of the deployed software stack can be ensured. This capability also guarantees security because it allows, e.g., source code auditing for any security threats. The proposed solution presents a generic ledger interface to interact with several blockchain solutions. The approach gives to the Cloud provider the freedom to select the blockchain technology to be used. We present an early evaluation of the overhead of our system against a standard orchestration framework, also presenting a discussion about the limitations of the current approach and possible solutions.

Prediction-as-a-Service is a promising new paradigm that brings the advantages of Software-as-a-Service's business model to the world of prediction APIs. In such a scenario, prediction API providers can leverage a Cloud provider's infrastructure to offer their inference service to the general public without having to worry about infrastructure acquisition and operation costs. Indeed, in the case of prediction APIs, self-hosting costs could be much higher than usual due to the fact that inference models, e.g., deep learning models, need specific hardware (e.g., graphical processing units) for an efficient execution. In such a context, trust is of great importance as the prediction API provider's most valuable asset, i.e., the inference model, is transferred to the Cloud provider. Thus, specific countermeasures should be designed to mitigate the possible attacks. In this paper, we analyze this scenario identifying the peculiar threat models. Then, we present a decentralized blockchain-based system, implemented on top of the popular Tendermint framework, that provides countermeasures to some of the main attacks. Numerical results, obtained executing deep neural network models, demonstrate that the overhead with respect to a centralized approach is negligible if compared with the advantages in terms of prevention of malicious behaviors.

Cloud storage adoption, due to the growing popularity of IoT solutions, is steadily on the rise, and ever more critical to services and businesses. In light of this trend, customers of cloud-based services are increasingly reliant, and their interests correspondingly at stake, on the good faith and appropriate conduct of providers at all times, which can be misplaced considering that data is the "new gold", and malicious interests on the provider side may conjure to misappropriate, alter, hide data, or deny access. A key to this problem lies in identifying and designing protocols to produce a trail of all interactions between customers and providers, at the very least, and make it widely available, auditable and its contents therefore provable. This work introduces preliminary results of this research activity, in particular including scenarios, threat models, architecture, interaction protocols and security guarantees of the proposed blockchain-based solution.

A Smart City can be envisioned as an ecosystem of smart environments that can be federated to interact one another, making infrastructure suitable to host innovative services for citizens and improve quality of city life. In such a context, interoperability and the presence of different administrative domains are the main challenges. In this paper, we propose to extend the Software-Defined City paradigm to both I/O and networking functions for implementing this vision. Through a motivating use case, we show how this approach can help in making city infrastructure a fully programmable ecosystem of resources that can be rewired any time at will.

A Smart City is an example of a domain where citizens and institutions, at any level, should be empowered by actually decentralizing any mechanism and process that can escape single-authority control and the grip of centrally-managed bureaucracies, while at the same time not relying on any single party trusting any other. In line with this key insight, in this paper, we have designed and implemented a decentralized, trustless system for (environmental) sensing data acquisition, storage, and consumption, in the context of a real-world Smart City deployment and with the participation of independent, institutional, stakeholders. This Open Data system has been extended with an end-user wizard for user-friendly and trustless (i.e., independent) data audit. The acquisition layer requires nodes to authenticate when sending readings. Last but not least, the storage layer is again trustless, as institutions can pool together their resources, without trusting one another, nor entrusting the administration of the system to any third party.

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.

editorial Free Access Share on Guest Editors’ Introduction to the Special Issue on Fog, Edge, and Cloud Integration for Smart Environments Authors: Francesco Longo Università degli Studi di Messina, Italy Università degli Studi di Messina, ItalyView Profile , Antonio Puliafito Università degli Studi di Messina, Italy Università degli Studi di Messina, ItalyView Profile , Omer Rana Cardiff University, UK Cardiff University, UKView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 19Issue 2May 2019 Article No.: 17pp 1–4https://doi.org/10.1145/3319404Published:12 April 2019Publication History 4citation490DownloadsMetricsTotal Citations4Total Downloads490Last 12 Months56Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Combining the Internet of Things (IoT) and Cloud computing is pursuing new levels of efficiency in delivering IoTas-a-Service (IoTaaS). In such scenario, Public Administrations (PAs) benefit from digitalization process improving development of innovation and administrative transparency. In this paper we propose a strategy to evaluate the quality of information in order to deliver IoTaaS in Smart City context. The goal of the work is to provide an advanced method to support PAs in the process of enhancing public information assets. The proposed strategy extends traditional approaches by using Multiple Criteria Decision Making (MCDM) models for the numerical and qualitative evaluation of public data and related metadata. Furthermore, it provides guidelines to implement the models starting from the data path.

The cover image, by Rustem Dautov et al., is based on the Research Article Metropolitan Intelligent Surveillance Systems for Urban Areas by Harnessing IoT and Edge Computing Paradigms, https://doi.org/10.1002/spe.2586. Photo Credit: Rustem Dautov.

Summary Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.

In this paper, we examine a spectrum sharing opportunities over the existing Global System of Mobile Communication (GSM) networks, by identifying the unused channels at a specific time and location. For this purpose, we propose a wideband spectrum sensing mechanism to analyze the status of 51 channels at once, belonging to the 10 MHz bandwidth centered at the frequency 945 MHz, in four different areas. We propose a subspace based spectral estimation mechanism, adapted to deal with real measurements. The process begins with data collection using Secondary User (SU) device enabled with Software Defined Radio (SDR) technology, configured to operate in the GSM band. Obtained samples are used then to feed the sensing mechanism. Spectral analysis is delivered to estimate power density peaks and corresponding frequencies. Decision making phase brings together power thresholding technique and GSM control channel decoding to identify idle and busy channels. Experiments are evaluated using detection and false alarm probabilities emulated via Receiver Operating Characteristic (ROC) curves. Obtained performances show better detection accuracy and robustness against variant noise/fading effects, when using our mechanism compared to Energy Detection (ED) based ones as Welch method, and Beamforming based ones as Minimum Variance Distortionless Response (MVDR) method. Occupancy results exhibit considerable potential of secondary use in GSM based primary network.

In assistive technology scenarios, this paper investigates how wearable devices may be used as smart solutions for accessing computers. We focus our attention on a smartwatch available on the consumer market; by using an open source middleware, based on low cost embedded systems, we propose to develop custom apps to process data from smartwatch's resources (e.g., sensors) and send them to a Linux single board computer. The latter component plays a critical role because it converts received data into raw input signals for a connected computer by emulating a standard mouse or a keyboard. Since the process requires no software installation on the target device, the user with a disability benefits from a single personalized equipment to control a wide range of computer devices, including traditional personal computers, tablets and smartphones. Additionally, the embedded system accesses the Internet and it takes advantage of the container-based virtualization technology to customize on demand its behaviour according to user's needs and preferences.

Smart Cities are the perfect ecosystem where IoT technology could be applied. In particular, to allow a direct interaction with objets scattered over a city (e.g, parking slots, light posts, gates) a powerful and scalable architecture has to be envisioned. In this paper, using Bluetooth Low Energy as underlying technology, we extend the concept of Personal Area Networks to include smart city objects so that a user can easily interact with them in a geolocalized and real time manner. A prototype implementing a smart gate has been developed in our University Campus in order to demonstrate the feasibility of the proposed approach.

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.

Nowadays, visual object recognition is one of the key applications for computer vision and deep learning techniques. With the recent development in mobile computing technology, many deep learning framework software support Personal Digital Assistant systems, i.e., smart phones or tablets, allowing developers to conceive innovative applications. In this work, we intend to employ such ICT strategies with the aim of supporting the tourism in an art city: for these reasons, we propose to provide tourists with a mobile application in order to better explore artistic heritage within an urban environment by using just their smartphone's camera. The software solution is based on Google TensorFlow, an innovative deep learning framework mainly designed for pattern recognition tasks. The paper presents our design choices and an early performance evaluation.

Cloud computing is becoming a relatively mature paradigm in the ICT landscape. In light of the growing appetite for resources and service levels on par with user expectations, multi-cloud scenarios are becoming the next frontier in the usage of distributed datacenters for private and hybrid Cloud scenarios. Application deployment in particular is a noteworthy feature to be evaluated as microservices become mainstream in adoption. Especially so when considered jointly with orchestration services; indeed OpenStack, as the most widely adopted Cloud middleware among the OpenSource community, features an orchestration subsystem, and may orchestrate the deployment of applications and services. In this work the authors will describe an architecture, developed within the H2020 BEACON project, for a standardized approach to orchestrated application deployment in multi-Cloud OpenStack- based setups, with TOSCA providing the specifications.

Summary Nowadays, remote desktop technologies are widely used to control remote computers. In this context, more and more Desktop as a Service (DaaS) solutions that allow users to directly control Virtual Machines through HTML5 web applications are emerging. However, currently, DaaS is at an early stage because several issues have to be addressed. In this paper, we aim to provide software architects a 360‐degree study on the major web‐based remote desktop client systems for the development of next generation DaaS solutions in both OpenStack‐based private and public Clouds. In particular, we compare and test different native and customized web‐based remote desktop solutions, specifically focusing on audio and video redirection performances in both Cloud environments. Exhaustive experiments highlight, from an objective point of view, the behavior of different DaaS solutions, providing useful hints to software architects and developers. Copyright © 2017 John Wiley & Sons, Ltd.

As embedded and mobile systems grow pervasive in people lives and expand their reach, paradigms related to mobile crowdsensing (MCS) are going to play an ever more prominent role. Innovative methodologies and applications able to unlock such a huge potential are required. A domain where MCS really fits is mobility and transportation. However, for a full exploitation of this paradigm in intelligent transportation systems (ITS), distributed and self-management capabilities for the involved nodes-vehicles have to be provided. This paper is a first step in this direction, laying out an optimization system by exploiting feedback-driven patterns in a distributed-opportunistic way. In this sense, collective intelligence and stigmergic, swarm-based paradigms are adapted to an innovative decentralized MCS pattern toward new approaches in ITS. Their effectiveness is demonstrated through a traffic engineering case study, where route planning services are designed according to the proposed approach and then modeled and evaluated by Markovian agents.

OpenStack is one of the major open-source solutions for creating and managing Infrastructure as a Service (IaaS) cloud providers. In this paper, we explore how the Horizon dashboard allows us to access virtual machines via a web-based remote desktop client. Besides the default remote desktop clients (based on the noVNC software), we propose to integrate in the OpenStack dashboard other alternative solutions testing both audio and video redirection feature. We consider different protocols including Virtual Network Computing (VNC) and Remote Desktop Protocol (RDP), while our experiments aim to evaluate which the best solution currently available on the market is, also providing indications to developers on how to extend the OpenStack dashboard in order to provide the best user experience from an objective point of view.

Google Cloud Vision is an image recognition technology that allows us to remotely process the content of an image and to retrieve its main features. By using specialized REST API, called Google Cloud Vision API, developers exploit such a technology within their own applications. Currently, this tool is in limited preview and its services are accessible for trusted tester users only. From a developer's perspective, in this paper, we intend to use such software resources in order to achieve assistive technology solutions for people with disabilities. Specifically, we investigate some potential benefits of Cloud Vision tool towards the development of applications for users who are blind.

The advent of both Cloud computing and Internet of Things (IoT) is changing the way of conceiving information and communication systems. Generally, we talk about IoT Cloud to indicate a new type of distributed system consisting of a set of smart objects, e.g., single board computers running Linux- based operating systems, interconnected with a remote Cloud infrastructure, platform, or software through the Internet and able to provide IoT as a Service (IoTaaS). In this context, container-based virtualization is a lightweight alternative to the hypervisor-based approach that can be adopted on smart objects, for enhancing the IoT Cloud service provisioning. In particular, considering different IoT application scenarios, container-based virtualization allows IoT Cloud providers to deploy and customize in a flexible fashion pieces of software on smart objects. In this paper, we explore the container-based virtualization on smart objects in the perspective of a IoT Cloud scenarios analyzing its advantages and performances.

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.

Nowadays, the combination between Cloud computing and Internet of Things (IoT) is pursuing new levels of efficiency in delivering services, representing a tempting business opportunity for IT operators of increasing their revenues. However, security is considered as one of the major factors that slows down the rapid and large scale adoption and deployment of both IoT and Cloud computing. In this paper, considering such an IoT Cloud scenario, we present an architectural model and several use cases that allow different types of users to access IoT devices.

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

Cloud federation is an emerging topic towards new dynamic scenarios in smart ecosystems, where new more flexible energy management strategies are needed than the traditional, in order to optimize mobility and energy efficiency. In this paper we focus both on cloud federation and energy efficiency to enforce a dynamic energy management strategy for the whole ecosystem, in order to reduce carbon dioxide emissions. More specifically, starting from our two-step approach, we evaluate a cloud federation ecosystem by moving computational resources among federated cloud DCs in order to maintain, for a forecast period, the related workload at the best Green Destination (GD) powered by renewable energy sources. To this end we present and discuss two simulated scenarios, and their experimental results, thus to proving the goodness of our approach. Moreover, an energy comparison between the transfer and the maintenance phases for the computational workload is made.

Cloud federation offers new business models to enforce more flexible energy management strategies. Independent Cloud providers are exclusively bounded to the specific energy supplier powering its Data Centers. The situation radically change if we consider a federation of cooperating Cloud providers. In such a context a proper migration of virtual machines among providers can lead to a global energy cost-saving strategy. In this paper, we present an approach to reduce energy cost in a federated Cloud ecosystem. More specifically, we propose an algorithm that allows providers to determine a map of possible destinations for cost-evaluation. Furthermore, we introduce an additional algorithm to determine the optimum energy cost migration path, and, consequently, the best Cloud Data Center where virtual machines should be migrated in order to push down energy costs.

In this paper we investigate the applicability of the federation among several Cloud platform, demonstrating that a federated environment provides evident benefits despite the costs for the setup and maintainance of the federation itself. Also, we propose a new solution able to manage resource allocation in federated Clouds where resource requests occur in a dynamic way. We adopt such a solution to setup distributed Hadoop nodes of virtual clusters for the parallel MapReduce processing of large data sets. To increase their capabilities, Cloud Providers establish a federation relationship, making the Hadoop-based Cloud platforms much more performing than in the isolate case, adding a further level of parallelization in service provisioning. The results analyzed in the referece use case, that is a video transcoding using the MapReduce paradigm in a federated fashion, show how the federation costs in terms of delays and overhead are low in comparison with the service provisioning costs, and also highlight how federation makes the offered Cloud service more streamlined and fast.

Following the as-a-service philosophy, a cloud service provider offers computing utilities in the form of virtual resources instantiated on top of a physical infrastructure. In order to meet business requirements still providing high-quality services, performance evaluation needs to be carefully carried out with the aim of optimizing data center utilization and increasing user satisfaction. In this context, power efficiency plays a critical role pushing service providers towards the application of innovative green strategies. In this paper, we present an analytical framework, based on stochastic reward nets, that allows to evaluate different resource allocation policies in a green cloud. A use case is shown in order to illustrate the approach, modeling scattering and saturation allocation policies and comparing them to a purely physical data center scenario. A validation of the proposed model against the CloudSim framework is presented and several numerical results are provided, demonstrating the effectiveness of the approach as a powerful tool for a cloud service provider to perform well-informed decisions about the resource allocation policies to be enforced.

Nowadays, Assistive Technology (AT) systems are closely tied to the devices that they control. Considering a smart environment where a person with a disability needs to interact with multiple devices, the user is forced to rely on AT software tools available on the each used platform. Therefore, computer skills are required to adjust any different computing environment configuration according to the user's needs and preferences. To address such issues, in this paper, we discuss the usage of embedded systems able to interface sensors and existing AT software tools running on user's personal equipments, in order to natively interact with many platforms. Thus, our work aims to decouple AT software tools from the accessed computer systems, allowing us to control various kinds of computer systems, even those that do not provide any AT features, by using just a personal assistive equipment.

The advent of the cloud computing paradigm offers different ways both to sell services and to exploit external computational resources according to a pay-per-use economic model. Cloud computing offers more and more business opportunities, and thanks to the concept of virtualization, different types of cost-effective cloud-based services have been rising. There is another perspective which represents a further business opportunity for small/medium providers which hold physical datacenters, known as Cloud federation. The cloud computing ecosystem includes hundreds of independent and heterogeneous clouds. Most of such cloud platforms can be considered as “islands in the ocean of the cloud computing” and do not present any form of federation. A possible future alternative scenario is represented by the promotion of cooperation among small/medium cloud providers, thus enabling the sharing of computational and storage resources. Federation of clouds can happen at the three typical levels of abstraction, i.e., infrastructure, platform, and software as a service (IaaS, PaaS, and SaaS). In this paper, we provide an analysis of the requirement for the establishment of an IaaS Cloud federation.

The transition to cloud computing offers a large number of benefits, such as lower capital costs and a highly agile environment. Yet, the development of software engineering practices has not kept pace with this change. Moreover, the design and runtime behavior of cloud based services and the underlying cloud infrastructure are largely decoupled from one another.This paper describes the innovative concepts being developed by CloudWave to utilize the principles of DevOps to create an execution analytics cloud infrastructure where, through the use of programmable monitoring and online data abstraction, much more relevant information for the optimization of the ecosystem is obtained. Required optimizations are subsequently negotiated between the applications and the cloud infrastructure to obtain coordinated adaption of the ecosystem. Additionally, the project is developing the technology for a Feedback Driven Development Standard Development Kit which will utilize the data gathered through execution analytics to supply developers with a powerful mechanism to shorten application development cycles.

Hadoop is widely used in many application scenarios, where a massive computation is required. The Hadoop framework is an open-source distributed computing system adopting the Map-Reduce paradigm for data processing, which is gaining more and more popularity. Indeed, recently, many Big Data solutions benefited from the Hadoop framework. One of the main issues in using Hadoop is related to its impossibility to dynamically scale and re-configure the environment, e.g, adding/removing nodes in a cluster for an efficient resource usage. This paper presents a new approach to dynamically setup Hadoop using a Message Oriented Middleware for Cloud computing (MOM4C), in order to make the system much more suitable to Cloud providers' requirements.

This paper deals with Cloud federation issues, where Clouds are both providers and clients of virtual resource at the same time. Specifically, we have designed a solution based on a XMPP communication platform, which allows to set up federated environments and to easily manage virtual shared resources. Heterogeneous and dynamic Clouds can interact in near-real time, making available their own resources according to specific agreement policies. We are thinking to implement the proposed solution using the Hadoop framework, a well-known Cloud Map-Reduce middleware designed to offer different types of services. Here, we analyze the basic and useful elements necessary to making up federated clouds. By mean of Cloud federation, two or more Hadoop clusters can be connected in order to increase service scalability, overcoming some technical limitations of the Hadoop framework itself.

Many current endeavors dealing with IoT and Cloud topics suggest leveraging sensing resources as mere data producers whereas the authors follow a Sensing and Actuation as a Service (SAaaS) approach. Among core ideas of SAaaS there's the enablement of an Infrastructure-oriented (i.e. IaaS-like) provisioning model for sensors and actuators alike. Such model matches technological requirements and constraints arising within certain application domains inspired by research about Future Internet topics. Aim of this paper is to explain why adopting the SAaaS paradigm for pervasively available and geographically distributed IT infrastructure is key, especially when field deployment of custom functionality is needed. Smart devices featuring always-on connectivity are the backbone of the IoT vision, and core building blocks of such infrastructure. Cloud-powered monitoring of urban areas and public facilities are among the most compelling scenarios to be found in IoT, and we are here focusing on these scenarios for two use cases. Actors, workflows and activities are described here in order to explain the main interactions and follow through outlining the use cases.

The key idea behind this work is the development of smart data centers able to monitor, control, and manage themselves through advanced analytics and management policies, collecting and analyzing real time data on their behavior. They also make adjustments to interdependent components across the physical infrastructure, in order to address changing business and technology needs. To this aim, we have designed and implemented a new framework for supporting green computing in the management of Cloud data centers. Through heterogeneous sensing devices deployed in the system, the framework is able to know the working state of the data center and to activate specific energy saving policies. We have evaluated the proposed solution through experiments on a real testbed implemented at the University of Messina. Experiments show interesting results thus proving real benefits deriving from the adoption of the proposed framework.

Smart environments represent a relatively uncharted ICT territory where plenty of sensor and actuator devices can be enrolled on-demand in order to realize high value-added services. A few application scenarios, such as Smart Cities, have already been explored. However, in order to finally enable such a paradigm, several issues have to be dealt with. In particular, from a developer perspective the high degree of heterogeneity for devices (ranging from cheap sensors to smart phones) could represent a hurdle for software design. In this paper, we present an innovative architecture that aims at providing a common reference platform for repurposing of devices i.e., reshaping their operational behavior for emergent and unforeseen requirements. Thanks to its modular and plugin based design, the proposed architecture is poised to ease implementation of both low-level (e.g., device discovery, code compilation, binary deployment) and high-level (e.g., service composition, data management) duties. We present the general architecture, then focusing on device-side aspects, while also providing two simple use cases that demonstrate the suitability of the proposed approach.

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.

This special issue deals with open problems related to Cloud computing assessment, and the interest raised in the scientific community is confirmed by the high quality of the papers received, which were selected after a review process started on July 2012 and ended on February 2013. Some relevant numbers are: 43 total submissions, nine papers accepted (approximately 0.2 acceptance ratio) after three rounds of reviews by more than 190 reviewers involved in the process. We would like to thank the authors of all the submitted papers for the high quality of their scientific contributions, and the prompt and timely reaction to the continual requests from the editors. We also thank all reviewers for their dedication and timeless efforts, which allowed us to select very high quality papers and respect the strict deadlines we have imposed. Different nonfunctional properties are considered in the special issue, mainly investigating Clouds behavior from the different perspectives of security and performance. For this reason we decided to split the special issue into two parts: the first that deals with security-related aspects (part I), and the second related to performance aspects (part II) of Cloud systems. Part I of this special issue addresses security aspects of Cloud computing such as trustworthiness, privacy, security vulnerabilities, countermeasures and threats to both physical (hardware) and logical (software, applications, data) architecture and infrastructure.

The articles in this special section focus on the measuremnet and analysis of cloud computing applications.

Nowadays, a typical scenario in the panorama of cloud computing includes an IaaS cloud provider offering on-demand VM hosting services to its clients. In this field, famous examples of large scale commercial providers are Amazon and Rackspace. However, how to arrange analogous providers with open source tools is not totally clear. Moreover, the integration between an IaaS cloud middleware with third party legacy software systems, today, represents a difficult task to accomplish. CLEVER is an open source cloud IaaS middleware allowing the allocation and management of VMs. In this paper, through the development of a REST interface, we discuss how a CLEVER-based cloud provider can be integrated with third party systems, hence satisfying their VM allocation requests.

Cloud computing is among the hottest trends in ICT, aiming at providing on-demand computing and storage resources with guarantees on the quality of service. A limit of current Cloud implementations is the absence of mechanisms to effectively manage inputs from the physical world. Our idea is to move towards a pervasive Cloud, providing facilities and solutions able to interact with the surrounding environment enabling development of new and value added services. In this vision also mobile devices, such as PDAs, usually equipped with several sensors and actuators, have to be included in the overall picture. Mobile devices and their respective owners can decide whether, how and when to contribute to the Cloud, thus introducing further unknowns. In order to deal with all such issues, in this paper we propose a solution that gives way to the Sensing and Actuation as a Service (SAaaS) paradigm, a step towards the creation of a Cloud of sensors and actuators. This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the SAaaS. An ad-hoc architecture and some preliminary background on this challenging vision are provided and discussed.

Nowadays, the cloud computing ecosystem includes hundreds of independent and heterogeneous clouds. Most of such cloud platforms can be considered as “islands in the ocean of the cloud computing” and do not present any form of federation. At the same time several clouds are beginning to use the cloud-based services of other clouds, but there is still a long way to go toward the establishment of a worldwide ecosystem including thousands of cooperation federated clouds. This paper aims to investigate the existing cloud middleware solutions able to address all the potential issues involved in these new cloud scenarios. In particular, the CLEVER cloud middleware will be analyzed, highlighting its design and features, and explaining the motivations that allow us to consider it suitable to the different phases of the evolution of federated cloud computing.

Nowadays, the cloud computing ecosystem is more and more distributed and heterogeneous. Cloud service providers begin to build their services using cloud-based services offered by other service providers. This raises several issues due to integration between services and provider themselves. In this paper, we propose a practice addressing such a concern in a "Vertical Supply Chain" scenario of distributed clouds.

Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. Clouds may demand huge amount of energy if adequate management policies are not put in place. Optimization strategies are needed in order to allocate, migrate, consolidate virtual machines and manage the switch on/switch off period of a data center. In this paper, we present a modeling approach based on Stochastic reward nets to investigate the more convenient strategies to manage a federation of Clouds, having in mind the final goal to reduce the overall energy consumption. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm.

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.

The near future evolution of the cloud computing can be hypothesized in three subsequent stages: stage 1 "Monolithic" (now), cloud services are based on independent proprietary architectures; stage 2 "Vertical Supply Chain", cloud providers will leverage cloud services from other providers; stage 3 "Horizontal Federation", smaller, medium, and large cloud providers will federate themselves to gain economies of scale and an enlargement of their capabilities. Currently, the major clouds are planning the transition to the stage 2, but how to achieve the stage 3 is unclear because some architectural limitations have to be overcome. In this paper, considering a general cloud architecture, we highlight such limitations and propose some enhancements which add new federation capabilities. In order to address such concerns we propose a solution based on the Cross-Cloud Federation Manager, a new component placeable inside the cloud architectures, allowing a cloud to establish the federation with other clouds according to a three-phase model: discovery, match-making and authentication.

Cloud environments offer a variety of concrete and abstracted entities which need to be identified. An example of cloud environment is the European project RESERVOIR, which similarly to other platforms characterized by a high level of dynamism, needs to identify and resolve resources. RESERVOIR has to manage allocation, deallocation and migration of virtual machines from an execution context to another. Such tasks could trigger identity and name alterations; in addition, a virtual machine may hold one or more names, identifiers, and representations in various execution environments. Nowadays, the Internet is using the Domain Name System (DNS), that is not suitable to such emerging scenarios. This paper aims to explore several RESERVOIR use cases, demonstrating how a flexible cloud naming system allows organizations to simplify the management of their assets deployed into the cloud.

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.

Nowadays, new and sophisticated technologies are quickly developing, taking advantage of the growing necessity of the companies to reduce operating costs, administrative time and to improve their productivity. Automatic Vehicle Monitoring (AVM) systems are one of the promising application fields where mobile technologies can be successfully adopted with specific care toward the definition of new services in the PTS (Public Transport Service) area.. The ability of an AVM system to control the street traffic represents a very important value for an advanced fleet management system. This paper proposes an AVM software platform, which is characterized by some innovative features able to manage multiple heterogeneous devices for fleet management and to monitor the street traffic. The architectural choices the system is based on are discussed and motivated. Specifically, this paper presents a case study of the implementation of the proposed system in the public transportation system of the city of Messina.

A cloud-enabled virtual environment called CLEVER is proposed in this paper. CLEVER simplifies the access management of private/hybrid clouds and provides simple and easily accessible interfaces to interact with different “interconnected” clouds, deploy Virtual Machines and perform load balancing through migration. The concept of interface is also exploited for integrating security, contextualization, VM disk image management and federation functionalities made available from higher level software components. Due to its pluggable design, CLEVER is able to grant high scalability, modularity and flexibility in the middleware architecture, while fault tolerance requirements are also satisfied. A prototype version of CLEVER has been developed to implement and test its main features, as discussed in the final part the paper.

Cloud computing is a highly dynamic environment where resources can be composed with other ones to provide many kinds of services to clients. In such scenario naming and resource location become critical issues and the existing Domain Name System (DNS), considered alone, is not able to address the new emerging problems. A cloud environment offers a variety of concrete and abstracted entities which need to be identified, whose states can frequently change: a virtual resource, could be allocated, deallocated or moved from a context to another. Moreover, a cloud entity could hold one or more names, identifiers, and representations in various cloud contexts where name alterations could frequently occur. In such environment, the management and integration of independent cloud name spaces is then becoming more compelling. This paper aims to propose a cloud naming system able to address such problems, providing an implementation practice in a cloud federation use case.

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 networks are a very challenging communication technology since their ability to be set everywhere and whenever. Among the several types of wireless systems, a new class of networks is gradually emerging: Wireless Mesh Networks (WMNs). A WMN is a distributed communication infrastructure organized in a mesh topology, which handles multi-hops connections and is capable of provide dependable services by dynamically updating and optimizing communications. This paper presents a new cross-layer architecture for supporting QoS in WMNs. It integrates the DiffServ paradigm with the admission control signaling and a multipath routing to route QoS traffic along reserved paths, while making use of alternative paths for Best Effort load. Performance measurements, based on simulative techniques are carried out to test the reliability of the proposed system.

A wide variety of new sensing technologies exist nowadays which allow remote monitoring and data acquisition also in quasi real-time conditions. Navigation's on-board monitoring systems are one of the promising application fields, which are quickly developing new and sophisticated applications, taking advantage from the decreasing cost of sensor devices. The development of technologies with increasing performances and able to communicate, at the same time, also in critical conditions with a different type of sensors is therefore necessary. This paper proposes a software platform to manage sensor devices which provides an integrated data processing of data detected from multiple heterogeneous devices. The middleware the platform is based on is presented and the architectural choices are discussed and motivated. Specifically, this paper presents a case study where the discussed software solutions are applied to parameters monitoring in naval field.

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.

Grid computing represents a new challenge for enterprises looking for computing and storage resources. Business grid is the new frontier that, with the wide adoption of the notion of services, is paving the way through a real shift of the grid computing paradigm from the scientific to the commercial sector. This imposes among the others the solution of security issues, as well as the development of reliable mechanisms to access data. In this paper we address the problem of data fault tolerance in Grid through the adoption of the redundant residue number system. From each file to be stored, a set of n elements is generated which are allocated to n + r different containers. If a disrupting event occurs, the r new elements will allow a correct reconstruction of the original file. The user will autonomously select both the fragmentation number and the degree of replication, that best fit his/her needs.

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.

Predominantly used in scientific contexts, Grid Computing imposes constraints related to the underlying infrastructure, forcing in many cases users to rethink or rewrite their applications. Inspired by Amazonpsilas EC2 model, this paper presents a virtualization based system (GliteVM) that allows users to address this issue allowing them to execute any existing application in a gLite based Grid infrastructure without changing their current work setting and with a limited effort. The proposed solution can also have a wide and deep impact on the enterprise world, playing an important role in a variety of areas (from innovations to e-commerce, from social networks to research) where requirements of scalability, high-availability and security are expected.

Dependability evaluation is an important often-mandatory step in designing and analyzing (critical) systems. Introducing control and/or computing devices to automate processes increases the system complexity, with an impact on the overall dependability. This occurs as a consequence of interferences, dependencies, and other similar effects that cannot be adequately managed through formalisms such as reliability block diagrams (RBDs), fault trees (FTs), and reliability graphs (RGs), since the statistical independence assumption is not satisfied. In addition, more enhanced notations such as dynamic FTs (DFTs) might not be adequate to represent all the behavioral aspects of dynamic systems. To overcome these problems, we developed a new formalism derived from RBD: the dynamic RBD (DRBD). DRBD exploits the concept of dependence as the building block to represent dynamic behaviors, allowing us to compose the dependencies and adequately manage the arising conflicts by means of a priority algorithm. In this paper, we explain how we can use the DRBD notation by specifying a practical methodology. Starting from the system knowledge, the proposed methodology drives to the overall system reliability evaluation through the entire phases of modeling and analysis. Such a technique is applied to an example taken from the literature, consisting of a distributed computing system.

Starting from 1990s and until now, Grid computing has been mainly used in scientific laboratories. Only in the last few years, it is evolving into a business-innovating technology that is driving commercial adoption. In this paper, we describe GridVideo, a Grid-based multimedia application for the distributed tailoring and streaming of media files. The objective is to show, starting from a real experience, how Grid technologies can be used for the development of nonscientific applications. Relevant performance aspects are analyzed, regarding both user-oriented (in terms of responsiveness) and provider-oriented (in terms of system efficiency) requirements. Different multimedia data dissemination strategies have been analyzed and an innovative technique, based on the Fibonacci series, is proposed. To respond to the stringent quality-of-service (QoS) requirements, typical of soft real-time applications, a reservation-based architecture is presented. Such architecture is able to manage the Grid resource allocation, thus enabling the provisioning of advanced services with different QoS levels. Technical and practical problems encountered during the development are discussed, and a thorough performance evaluation of the developed prototype is presented.

It is a pleasure for me to welcome you at the Workshop on Dependable Parallel and Network -Centric Systems (DPDNS08) in Miami , Florida. DPDNS workshops have grown in quality and attendance over the past years to become an international forum where attendees can address and discuss all the issues related to the converging fields of dependable computers and communications.

Wireless ad hoc networks experienced a great diffusion thanks to their simple deployment everywhere and whenever needed. Already used for entertainment applications (e.g., instant messaging) or for specific domains (e.g., military) we think such technology is mature enough to support also real-time services provision.Real time applications pose several challenges, in particular issues such as limited bandwidth, unreliable channels, topology evolutions and power consumption make quality of service (QoS) management a mandatory task to be addressed. In this paper, we propose a general QoS system that makes use of differentiated services strategies by exploiting the concept of virtual backbones. Our key idea is to use an auto-configured virtual backbone to set up a differentiated service area in order to opportunely manage QoS flows. Also, thanks to a new distributed call admission control algorithm, it avoids the arise of overloading situations. Performance measurements, based on simulative techniques and carried out to test the feasibility of the proposed system, are finally presented.

Dependability evaluation is an important, often indispensable, step in (critical) systems design and analysis processes. The introduction of control and/or computing systems to automate processes increases the overall system complexity and therefore has an impact in terms of dependability. Moreover it is of interest to evaluate redundancy and maintenance policies. In those cases it is not possible to recur to notations as reliability block diagrams (RBD), fault trees (FT) or reliability graphs (RG) to represent the system, since the statistical independence assumption is not satisfied. Also more enhanced formalisms as dynamic FT (DFT) could result not adequate to the goal. To overcome those problems we developed a new formalism derived from RBD: the dynamic RBD (DRBD). In this paper we explain how to use the DRBD notation in system modeling and analysis, coming inside a methodology that, starting from the system structure, drives to the overall system availability evaluation following modeling and analysis phases. To do this we use an example drawn from literature consisting of a multiprocessor distributed computing system, also comparing our approach with the DFT one.

Providing an efficient localization system is one of the most important goals to be pursued if an efficient utilization of sensor networks has to be addressed. This paper proposes a novel localization system based on Kohonen 's self organizing maps (SOMs), able to provide some Artificial Intelligence features to sensor nodes. A SOM is a particular neural network that learns to classify data without any supervision. In each sensor node, a SOM is implemented to evaluate the sensor node position, using a very little amount of storage and computing resources. In a scenario where thousands of sensor nodes are placed, this system evaluates the position of each sensor in a distributed manner, assuming a very little percentage of nodes knowing their actual position.

To provide an efficient Call Admission Control (CAC) is one of the most important goals to be pursued if an efficient allocation of network resources to roaming and new users has to be addressed. Hierarchical network architectures are a common choice to build Broadband Wireless Access (BWA) networks able to provide added value services with different QoS levels. In a scenario where mobile hosts access the network via a wireless channel and are gated to the Internet through a hierarchical structure, this work aims to evidence the importance of an accurate design of the hierarchical network topology to obtain an efficient CAC process.

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 "colony" 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 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 "Slime Mold". 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 in clusters towards the sink nodes using the gradient concept and reaching high levels of autonomy. We analyze the proposed protocol to examine the performances and the adaptation properties using simulation techniques.

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.

Gateway node provides connectivity to the nodes inside MANET. Based upon the route to gateway node found during discovery phase; source node relies on intermediate nodes to forward packets to the gateway node. We have identified a vulnerability of the gateway discovery phase where adversaries can forcibly introduce routes having wormholes exploiting the routing race conditions. We have termed such routes having wormholes as the pseudo routes. The pseudo route attack is a more generalized form of the wormhole attack where worm-hole start and end points can be any two nodes in the route and even multiple wormholes can be present in the same route. We have proposed an end-to-end approach using localization information to detect and prevent pseudo route attacks. Simulation results are presented to show the impact of pseudo route attack under various scenarios as well as for performance of the proposed protocol. We found that attack impact is more when the gateway is at the corner of the network

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

Abstract Very popular mobile devices such as cellular phones, laptops, and handheld and tablet PCs are becoming more and more powerful. One of today's trends is to access multimedia services from this sort of device. Their low capacity in terms of storage, and their high inclination to wireless connection, suggest the use of streaming techniques instead of media download. Mobile devices are, at present, dramatically unhomogeneous for visualization and audio reproduction characteristics. Consequently, today multimedia delivery systems are very customized for some device profiles or for wireless connection types. Transcoding techniques are used in order to adapt the same media source to the multiple hardware profiles. Client‐based transcoding techniques show their weakness in high energy consumption, and server‐based transcoding techniques suffer from scalability problems. In this paper we propose a scalable distributed multimedia server based on the Grid computing paradigm, which can adapt media content to the device profile and/or to the connection link. Copyright © 2005 John Wiley & Sons, Ltd.

Data dissemination is used in mobile ad hoc networks (MANETs) for activities like issuing commands, propagating alarms, and configuring nodes. Existing forms of data dissemination are based upon push or pull approaches. We present a novel data dissemination approach that supports push and pull approaches as well as direct node-to-node dissemination method. Our proposal is to develop a highly connected core based on two-hop neighbor information to assist in dissemination. We propose some algorithms for selecting a subset of network's nodes to perform data dissemination on behalf of the complete network. The core structure is characterized by its ability to work with limited neighbor information, connectivity maintenance, high connectivity with other features related to node degree. This property helps to lessen the control overhead as piggybacking can be used to gain partial neighbor information. We provide simulation results showing the performances of the proposed scheme

Mobile networks are requested to provide several added value services with different QoS levels. An efficient call admission control (CAC) is one of the strategic components that a network management policy requires, which defines how to allocate network resources to roaming and new users. We assume a hierarchical network architecture constituted of several cellular IP regions which may communicate each other accessing the Internet through a Mobile IP protocol. In such scenario, we demonstrate the importance of CAC not only in the ingress wireless points, but in the wired network too. An integrated approach to CAC is then proposed whose advantages are shown through simulation analysis.

Resource reservation in wired networks is a relatively mature area, with some available solutions already on the market. Due to the increasingly growing diffusion of mobile terminals and services, the study of the issues concerning the management of mobility in IP-based networks is also arising increasing interest. The combination of both issues is however still at its initial definition. This work is based on the idea of combining mobile architectures with the available techniques for resource reservation. In particular, we have created a cellular IP-based architecture, which can assure some agreed levels of QoS (for instance, in terms of data throughput) according to the IP layer. We present the proposed architecture, describe its implementation and show some experimental results for its validation.

We present the Aipac protocol, which allows nodes in an ad hoc network to configure their own IP address automatically. Due to the mobility of nodes, different networks can overlap, and nodes with the same address can accidentally get in contact. Our protocol offers an innovative management of such merging of networks. In fact, it makes use of the features of reactive routing protocol to minimize the configuration and maintenance of unique address traffic. Moreover, it implements a gradual merging procedure that causes the merging process of networks according to their evolution. That allows to avoid to generate a huge amount of traffic on the wireless channels. We used the ns2 simulator to show that the protocol works correctly, and so as to analyze its performance.

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.

Wireless devices are becoming very popular and powerful to be commonly adapted to access distributed services. More and more sophisticated applications are being developed and a very promising market is quickly being established where mobile users might access multimedia data while roaming from a cell to another, anytime and everywhere. Such scenario requires a strong infrastructure in order to adequately manage all the different issues related with high-level service provisioning. QoS represents one of the most crucial issues as it involves many different aspects and directly impacts the user satisfaction. In order to achieve this target, we propose an architecture, which allows mobile devices to access advanced services available in the wired part of the network. This architecture, based on mobile agents technology, assumes the presence of a VOD virtual server. The strategy adopted is to hide all the basic mechanisms inside a middleware layer that provides necessary interfaces to allow a simple interaction between the user on the one side and the network and the distributed services on the other. The overall architecture, that is currently being implemented and tested, has been used as an experimental environment to provide mobile users with a new service to access MPEG-4 flows.

Since its appearance, mobile agent technology has been considered as a new and flexible paradigm for distributed application development. However because of code mobility, this paradigm produces new threats to security too, which are of central importance when proposing this technology for commercial applications. We show the security services which have been specifically designed and developed for the MAP agent system. Early performance measurements show how the overhead needed by the security mechanisms introduced can be acceptable if compared with the functionalities provided.

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.

We propose an integrated solution for QoS management in mobile wireless networks that can be dynamically adapted to user requirements and resource availability. Due to the strong variability of the environment under examination, we propose to use mobile agents as the underlying enabling technology. We propose a proactive approach that tries to satisfy the user requirements by utilizing advance resource reservations, and a reactive approach which tries to negotiate network resources when significative changes in the demand of resources arise. We also propose a combination of the two approaches in an integrated hybrid system that combines a mobility predictive advanced bandwidth reservation scheme with a call admission control and resource reconfiguration strategy to support flexible QoS management.

Wireless communication systems are becoming the new enabling technology for accessing, sharing and processing data. It is easy to foresee that in the near future users will access the Internet through wireless PDA, while roaming from one place to another. Mobile ad-hoc networks (MANET) are peer-to-peer wireless networks that do not rely on the presence of a wired interconnection infrastructure. Apart from more traditional applications in the military field as well as in disaster recovery and emergency situations, MANET may represent an interesting possibility for personal communications and interaction among mobile users. We identify some possible fields of application for such wireless systems, identifying advantages and drawbacks. We also present a prototype application where MANETs are used in order to provide a distributed image processing service.

Agent programming technology has emerged as a flexible and complementary way to manage resources of distributed systems due to the increased flexibility in adapting to the dynamically changing requirements of such systems. A very promising application of this technology is related to the control of forthcoming networking systems which will represent a competitive marketplace with a multitude of vendors, operators and customers. Thus, new reference models have to be investigated in order to better satisfy users' requirements in a framework where resource allocation is provided under the control of different and often competing stakeholders (users, network providers, service providers, etc.). We believe that autonomy is one of the features that will characterize the behavior of agents in such environment: autonomous choices will be taken as the result of coordination among different cooperating software entities. Following this direction, we describe the efficient integration and adoption of mobile agents and genetic algorithms in the implementation of a valuable strategy for the development of effective market based routes for brokering purposes in the future multioperator network marketplace. The proposed genetic algorithm provides a kind of stochastic algorithm searching process in order to identify optimal resource allocation strategies. The agent-based network management approach represents an underlying framework and structure for the multioperator network model, and can be used to facilitate the collection and dissemination of the required management data, as well as the efficient and distributed operation of the algorithm. We also present some numerical results to assess the performance and operation effectiveness of our approach, by applying it in some test case scenarios.

An agent based approach is investigated to build a framework where resource allocation is provided under the control of different and often competing stake-holders (users, network providers, service providers, etc.). This paper also describes the efficient integration and adoption of mobile agents and genetic algorithms in the implementation of an effective strategy for the development of effective market based routes for brokering purposes in the future multi-operator network marketplace. The agent-based network management approach represents an underlying framework and structure for the multi-operator network model, and can be used to collect all the required management data. The proposed genetic algorithm provides a kind of stochastic algorithm searching process in order to identify optimal resource allocation strategies.

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 authors present the design and the implementation of a distributed media service provider for the Internet. The overall architecture consists of several remote video servers whose resources are merged into a logically unique and more powerful service provider. The basic abstraction layer needed to achieve inter-server resource coordination and user-server interoperability has been realized through the use of OMG's CORBA and SUN's Java facilities. The current implementation of the video servers is based on the DAVID video server developed at the University of Catania (http://sun195.iit.unict.it/david).

A previous development in the field of telecommunication networks has enabled the evolution of distributed systems, and has favoured the development of a new generation of applications based on the use of mobile agents. In fact, during the last few years, several agent platforms have been created; however the architectures and implementation of such platforms are very different, and this makes their interaction difficult. In order to favour the interaction among differently implemented platforms, the Object Management Group (OMG) has proposed the Mobile Agent System Interoperability Facility (MASIF) standard, which includes some definitions and interfaces which all platforms have to comply with. We present the MAP platform for developing and managing mobile agents; it complies with MASIF in order to communicate and interact with similar platforms provided that they comply with the mentioned standard. Besides, we present a mechanism, which has been implemented in the MAP, which extends MASIF's functionalities so to enable the interaction among agents and agent systems operating in different regions.

Network programmability seems to be a promising solution to network management and quality of service (QoS) control. Software mobile-agents technology is boosting the evolution toward application-level control of network functionalities. Code may be deployed in the network dynamically and on demand for the benefit of applications or application classes. Agents support a dynamic distribution of control and management functions across networks, thus increasing flexibility and efficiency. We propose to use mobile-agent technology to overcome some of the problems inherent in current Internet technology. We focus our attention to QoS monitoring, being locally significant in network subdomains, and realize a QoS management strategy in response to variations of user, customer of application requirements, and of the network state. We describe our experience and the results obtained from our testbed, where software agents are instantiated, executed, migrated, and suspended in order to implement flexible QoS management in IP networks.

Petri nets represent a useful tool for performance, dependability, and performability analysis of complex systems. Their modeling power can be increased even more if nonexponentially distributed events are considered. However, the inclusion of nonexponential distributions destroys the memoryless property and requires to specify how the marking process is conditioned upon its past history. We consider, in particular, the class of stochastic Petri nets whose marking process can be mapped into a Markov regenerative process. An adequate mathematical framework is developed to deal with the considered class of Markov Regenerative Stochastic Petri Nets (MRSPN). A unified approach for the solution of MRSPNs where different preemption policies can be defined in the same model is presented. The solution is provided both in steady-state and in transient condition. An example concludes the paper.

In this paper a prototype of a distributed video-on-demand server is presented. The technical aspects regarding the realization of this kind of service provider are dealt with, paying particular attention to exploiting the architecture the system is based on. The proposed system easily allows, in any broadband local network environment, the realization of a low-cost/high-performance distributed video server, through the clustering of the local workstations. The system has been implemented and tested, and the results of its performance and scalability are presented in the final sections of this paper.

Originally, the Internet delivered best-effort service quality with respect to end-to-end delay. Recently, extensions such as RSVP have been proposed to provide guaranteed real-time services as well. Unfortunately, network resources, such as routers, do not yet fully, support RSVP reservation protocols so that guarantees cannot truly be given. We suggest to follow the paradigm of open programmable networks for a more complete QoS provisioning. Reservation gaps or tunnels are dynamically closed by means of a software agent approach that is flexibly deployed for an application oriented QoS support. Agents are dynamically located to such tunnels in order to monitor the tunnels, to provide feedback information in case of QoS violations, and to decide on possible compensating measures to be taken.

As distributed multimedia applications become more widely diffused, systems for quality of service QoS management are increasingly essential. In this paper we present an architecture for distributed QoS management based on software agents. We discuss the advantages of using agent technology in a problem inherently distributed and complex such as QoS management. Besides introducing several negotiation and coordination techniques among agents as fundamental properties for QoS management, it is argued in favor of decision making based on analytical and simulative modeling techniques. Furthermore, design and implementation issues are discussed in detail and our Java-based platform for agent-based QoS management is introduced.

Preventive maintenance of operational software systems, a novel technique for software fault tolerance, is used specifically to counteract the phenomenon of software "aging". However, it incurs some overhead. The necessity to do preventive maintenance, not only in general purpose software systems of mass use, but also in safety-critical and highly available systems, clearly indicates the need to follow an analysis based approach to determine the optimal times to perform preventive maintenance. In this paper, we present an analytical model of a software system which serves transactions. Due to aging, not only the service rate of the software decreases with time, but also the software itself experiences crash/hang failures which result in its unavailability. Two policies for preventive maintenance are modeled and expressions for resulting steady state availability, probability that an arriving transaction is lost and an upper bound on the expected response time of a transition are derived. Numerical examples are presented to illustrate the applicability of the models.

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.

In a client-server type system, the server software is required to run continuously for very long periods. Due to repeated and potentially faulty usage by many clients, such software "ages" with time and eventually fails. (Huang et al., 1995) proposed a technique called "software rejuvenation" in which the software is periodically stopped and then restarted in a "robust" state after proper maintenance. This "renewal" of software prevents (or at least postpones) the crash failure. As the time lost (or the cost incurred) due to the software failure is typically more than the time lost (or the cost incurred) due to rejuvenation, the technique reduces the expected unavailability of the software. We present a quantitative analysis of software rejuvenation. The behavior of the system is represented through a Markov Regenerative Stochastic Petri Net (MRSPN) model which is solved both for steady state as well as transient conditions. We provide a closed-form analytical solution for the steady state expected down time (and the expected cost incurred) due to system unavailability. We also evaluate the optimal rejuvenation interval which minimizes the expected unavailability of the software.

Non-Markovian models allow us to capture a very wide range of circumstances in which it is necessary to model phenomena whose times to occurrence is not exponentially distributed. Events such as timeouts in a protocol, service times at a machine performing the same task on each part, and memory access or instruction execution in a low-level h/w or s/w model, have durations which are constant or with a very low variance. Phase-type distributions can be used to approximate a non-exponential, but they increase the size of the state space.The analysis of stochastic systems with non-exponential timing is of increasing interest in the literature and requires the development of suitable modeling tools. Recently, some effort has been devoted to generalize the concept of Stochastic Petri Nets (SPN), by allowing the firing times to be generally distributed.A particular case of non-Markovian SPN, is the class of Deterministic and SPN (DSPN) [1]. A DSPN is a non-Markovian SPN where, in each marking, at most one transition is allowed to have a deterministic firing time with enabling memory policy.A new class of stochastic Petri nets has recently been defined [2, 3] by generalizing the deterministic firing times of the DSPN to generally distributed firing times. The underlying stochastic process for these classes of Petri nets is a Markov Regenerative Process (MRGP). This observation has opened a very fertile line of research aimed at the definition of solvable classes of models whose underlying marking process is an MRGP, and therefore referred to as Markov Regenerative Stochastic Petri Nets (MRSPN).Some of the results in this filed will be described in the session. In particular, Ciardo investigates stochastic confusion by defining the selection probability for transitions attempting to fire at the same time. German introduces the "method of supplementary variables" for the derivation of state equations describing the transient behavior of the marking process. Puliafito describes how, under some constraints, concurrent enabling of several generally distributed timed transitions is allowed. Bobbio and Telek discuss how age memory policy can be included to capture preemptive mechanisms of the resume (prs) type.

We concentrate on the architectural issues of parallelizing I/O access in a disk array system by means of definition of a new, particularly flexible architecture, called partial dynamic declustering, which is fault-tolerant and offers higher levels of performance and reliability than the solutions normally used. A simulation analysis highlights the efficiency of the proposed solution in balancing the file system workload and demonstrates its validity in both cases of unbalanced loads and expansion of the system. Particular attention is also paid to the definition of analytical models, based on stochastic reward nets, in order to analyze the performance and reliability of the system. The response time distribution function is evaluated and a specific performance analysis with varying degrees of declustering and workload is carried out.>

Continuous monitoring of a computer network performance is probably the only solution which allows prompt identification of anomalous functioning conditions and knowledge of parameters on which to base speedy, effective recovery interventions. A number of tools for performance management already exist, but their effectiveness is limited as they are essentially inserted inside owner network management solutions. In the paper we describe the realization of PMt, a platform for the development of performance management applications for the control and real-time management of a heterogeneous computer network. PMt imposes an object-oriented view of the distributed system, defines a performance management design methodology which adapts well to really distributed system management and provides the user with a set of tools and services to assist him both in the design of new performance management applications and in actual management of the whole system.>

With a view to improving the performance and the fault tolerance of mass storage units, this paper concentrates on the architectural issues of parallelizing I/O access and a disk array system by means of definition of a new, particularly flexible architecture, called Partial Dynamic Declustering, which is fault-tolerant and offers higher levels of performance and reliability than the solutions normally used. A fast distributed algorithm based on a dynamic structure and usable for the implementation of an efficient I/O subsystem manager is proposed. Particular attention is also paid to the definition of analytical models based on Stochastic Reward Petri nets in order to analyze the performance and reliability of the system proposed.>

In this paper we present a design for a general-purpose fuzzy processor, the core of which is based on an analog-numerical approach combining the inherent advantages of analog and digital implementations, above all as regards noise margins. The architectural model proposed was chosen in such a way as to obtain a processor capable of working with a considerable degree of parallelism. The internal structure of the processor is organized as a cascade of pipeline stages which perform parallel execution of the processes into which each inference can be decomposed. A particular feature of the project is the definition of a 'fuzzy-gate', which executes elementary fuzzy computations, on which construction of the whole core of the processor is based. Designed using CMOS technology, the core can be integrated into a single chip and can easily be extended. The performance obtainable, in the order of 50 Mega fuzzy rules per second, is of a considerable level.>

The paper provides a systematic approach to modelling and assessing gracefully degrading systems, exploiting the formalism offered by Petri Nets. The procedure followed is based on the definition of some fundamental modules, the composition of which allows a complete model of the system to be obtained. The model can then be solved by computer simulation or analytically by means of a class of Petri Nets to obtain interesting performance parameters. The paper examines a number of real cases and describes the results obtained.

The bandwidth balancing method (BBM) introduced into the latest versions of the DQDB protocol is discussed. An extended analysis is presented of the behavior of the protocol with the BBM incorporated, assuming that more than one priority level is active in the network. The results are oriented towards evaluation of the effect the BBM has on access delay and bandwidth sharing in overload conditions.>

Topics related to the service quality of a network made up of interconnected LANs are discussed. As the size of a network increases, so do reconfiguration times, thus reducing service availability, increasing frame loss, and leading to performance degradation. This degradation may be substantial if circumstances requiring reconfiguration occur frequently. Two types of networks are compared by assessing the parameters which directly affect the quality of service of the network. The first uses media access control (MAC) bridges based on the standard spanning tree algorithm; the second uses parallel MAC bridges, by means of which the network can be organized into islands, thus making reconfiguration selective. In both cases the bridged LAN is modeled according to the schemes suggested by systems reliability theory, which allows assessment of performance parameters directly affecting service quality, such as the mean time to failure (MTTF), performability, and availability.>