The CAN protocol, widely used in vehicles, lacks authentication and encryption, making it prone to spoofing, injection, and denial-of-service attacks. This work proposes a detection method based on physical layer signal analysis and unsupervised learning. A custom testbed of eight Arduino nodes with MCP2515 transceivers emulates nominal and attack traffic. Differential voltage signals$(\Delta V=\mathbf{CAN}_{-}\mathbf{H}-\mathbf{CAN}_{-}\mathbf{L})$. are locally captured, segmented, and used to train a lightweight autoencoder. Implemented in TensorFlow, the model achieves 98% accuracy and 93% recall on unauthorized data, and 85% accuracy and 87% recall on spoofed traffic, with 24 ms inference time. The results obtained show that physical layer signals enable efficient and embedded-friendly CAN intrusion detection.

Modern automotive systems are evolving into distributed cyber-physical platforms that demand secure, dynamic, and portable software deployment across heterogeneous embedded devices. Traditional embedded approaches, based on static firmware images and device-specific toolchains, struggle to meet these requirements at scale. This paper presents a Kubernetes-based orchestration architecture for deploying WebAssembly (Wasm) modules on resource-constrained microcontroller units (MCUs) in automotive cyber-physical systems (CPSs). Our orchestration framework manages the lifecycle of Wasm applications across heterogeneous MCU networks and addresses key automotive requirements including isolated execution through Wasm sandboxing and centralized management of distributed embedded systems. The proposed solution enables dynamic vehicle customization, over-the-air (OTA) updates and collaborative computation scenarios envisioned in next-generation automotive platforms.

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.

Industrial Control Systems (ICSs) are increasingly vulnerable to cyber threats due to their reliance on legacy protocols like Modbus TCP/IP, which lack built-in security mechanisms. Despite these risks, replacing or upgrading ICS components remains costly and impractical for many critical infrastructures, such as manufacturing, power generation, and transportation. This highlights the urgent need for security solutions that enhance protection without requiring disruptive system overhauls. Building on our previous work, this paper introduces a decentralized security framework based on dedicated proxies that manage cryptographic operations for legacy devices and facilitate secure communication. The architecture leverages Decentralized Identifiers (DIDs) for node identity management, storing DID Documents containing post-quantum public keys in a Distributed Hash Table (DHT). The DHT, composed of proxy nodes, is specifically modified to function as a Verifiable Data Registry (VDR), ensuring data integrity and availability. To support authorization, Verifiable Credentials (VCs) are issued by an operator-controlled Issuer Node, activated solely during new device installations, or maintenance operations. The proposed solution eliminates reliance on a central authority, enhances communication security against quantum threats, and improves resilience through decentralized identity management. Performance evaluations on both physical testbeds and simulated environments analyze handshake latency and system efficiency. Results demonstrate that our approach effectively secures legacy ICSs with an acceptable operational impact, paving the way for more robust and future-proof industrial networks.

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.

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.

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.

Nowadays, the study of nucleic acids (DNA/RNA) has become a digital science thanks to the advent of modern massive parallel sequencing technologies, better known with the acronym NGS standing for next-generation sequencing, and to the availability of a vast amount of genetic data easily accessible from publicly available databases. Due to the quantity and complexity of such data, its processing requires strong computer science knowledge and skills. This background includes topics such as programming and scripting languages, command-line interfaces, low-level data management tools, which are not always part of the toolbox of molecular biologists and geneticists. The need to adapt to entirely new IT tools and workflows slow down even the more experienced researchers, thus dedicated and customizable GUIs would be much more preferable and conducive. In this paper, we tackle this issue by proposing a preliminary architecture for a framework providing the following benefits: i) it supports the post-NGS analysis process definition phase (commonly called pipeline definition) via a graphical dashboard designed with NodeRED; ii) it automatically deploys the workflows on top of a cluster of computational resources, according to the Function-as-a-Service paradigm, i.e., treating each step of the pipeline as a function to be executed within Linux-based containers, pre-configured with all the necessary dependencies; iii) it runs such containers taking care automatically of resource load balancing. Finally, the framework is thought to include human feedback in the loop, thanks to the availability of a smart notification system, allowing the researcher to monitor the workflows and make any decision needed for its continuation.

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.

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).

Due to their intrinsic resource constraints, the mobile Internet of Things (IoT) devices are not able to provide intensive services by just relying on their own facilities. Fog Computing effectively helps overcome this hurdle. Indeed, it extends the Cloud toward the network edge, distributing resources and services of computing, storage, and networking close to the end devices. This topological proximity is the key enabler of several advantages that are essential in many emerging ICT domains. Nonetheless, the mobility of an IoT device compromises such benefits as it increases the topological distance to the serving Fog node. Therefore, the Fog service has to be migrated in order to be always close enough to the served IoT device. We name thisCompanion Fog Computing(CFC), since the Fog service behaves as a "companion" of the correspondent application on the mobile device. In this paper, we present a Fog Computing Platform that performs stateful container (i.e., Fog service) migrations in order to enable CFC. Specifically, we introduce a CFC model from which we derive a reference architecture comprising all the functionalities required in a platform to make migration decisions and carry them out. Moreover, we demonstrate the soundness of the proposed reference architecture by discussing a proof-of-concept implementation based on the Stack4Things (S4T) platform, and we report a set of conducted experiments to show the feasibility of stateful container migrations.

As for the Cloud, essential features of Fog Computing are both virtualization and the capability to migrate virtual environments among nodes. In this paper, we thoroughly report both the state-of-the-art virtualization and migration techniques and their available implementations. In particular, we investigate the aptness of such technologies for a specific layer of the Fog hierarchy, namely the network edge. Indeed, this layer presents some characteristics that distinguish it from the Cloud so that a virtualization or migration technique that represents a good compromise for the Cloud might not be likewise suitable for the edge.

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In comparison to datacenter-oriented Cloud middleware, the administrator and the owner of the infrastructure are not one and the same. This translates into the requirement to support delegation-enabled authorization. In this paper, the authors investigate an authorization and delegation model for the IoT-Cloud based on blockchain technology. In particular, the scheme is implemented in the form of smart contracts over the Ethereum platform. Indeed, this approach represents an enhancement, over a function previously designed in a centralized fashion, by enabling the user to audit authorization operations and inspect how access control is actually performed, without blindly trusting the Cloud as a proxy for access to resources.

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.

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.

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.

To kickstart the process of morphing Messina into a "smart" city, an explicit mission for the crowdfunded #SmartME project, it is essential to set up an infrastructure of smart devices embedding sensors and actuators, to be scattered all over the urban area. An horizontal framework coupled with the Fog computing approach, by moving logic toward the "extreme" edge of the Internet where data needs to be quickly elaborated, decisions made, and actions performed, is a suitable solution for data- intensive services with time-bound constraints as those usually required by citizens. This is especially true in the context of IoT and Smart City where thousands of smart objects, vehicles, mobiles, people interact to provide innovative services. We thus designed Stack4Things as an OpenStack-based framework spanning the Infrastructure-as-a-Service and Platform-as-a-Service layers. We present some of the core Stack4Things functionalities implementing a Fog computing approach towards a run- time "rewireable" Smart City paradigm, by outlining node management and contextualization mechanisms, also describing its usage in terms of already supported and developed verticals, as well as a specific example related to environmental data collection through #SmartME.

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.

Cloud monitoring is one important aspect for effective cloud management. Currently, cloud monitoring solutions can be classified into three groups: the ones not considering multiple layers or real time data analysis, the ones considering multiple layers but not real time data analysis, and the ones only considering real time data analysis. However, all these solutions fail to provide frameworks able to combine together monitoring in multiple layers and data stream analysis for detecting situations where multiple management actions are applicable in different layers of the cloud environment. This paper addresses this gap and proposes the Ceiloesper framework. Such a framework extends the OpenStack Ceilometer technology with Esper CEP and enables collection and analysis of information according to the principles defined in the 3-D cloud monitoring model, proposed in a previous work. The main contributions of this paper are: (i) the definition of the concept of Situation of Interest (SoI) leading to multiple management actions; (ii) the Ceiloesper architecture for a monitoring solution combining traditional monitoring elements with CEP; (iii) extensions to the Ceilometer OpenStack technology. We tested the Ceiloesper framework on a scenario based on the Wordpress application and the experimental results show its effectiveness.

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

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.

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.

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

In a large Infrastructure-as-a-Service (IaaS) cloud, component failures are quite common. Such failures may lead to occasional system downtime and eventual violation of Service Level Agreements (SLAs) on the cloud service availability. The availability analysis of the underlying infrastructure is useful to the service provider to design a system capable of providing a defined SLA, as well as to evaluate the capabilities of an existing one. This paper presents a scalable, stochastic model-driven approach to quantify the availability of a large-scale IaaS cloud, where failures are typically dealt with through migration of physical machines among three pools: hot (running), warm (turned on, but not ready), and cold (turned off). Since monolithic models do not scale for large systems, we use an interacting Markov chain based approach to demonstrate the reduction in the complexity of analysis and the solution time. The three pools are modeled by interacting sub-models. Dependencies among them are resolved using fixed-point iteration, for which existence of a solution is proved. The analytic-numeric solutions obtained from the proposed approach and from the monolithic model are compared. We show that the errors introduced by interacting sub-models are insignificant and that our approach can handle very large size IaaS clouds. The simulative solution is also considered for the proposed model, and solution time of the methods are compared.

From an enterprise perspective, one key motivation to transform the traditional IT management into Cloud is the cost reduction of the hosted services. In an Infrastructure-as-a-Service (IaaS) Cloud, virtual machine (VM) instances share the physical machines (PMs) in the provider's data center. With large number of PMs, providers can maintain low cost of service downtime at the expense of higher infrastructure and other operational costs (e.g., power consumption and cooling costs). Hence, determining the optimal PM capacity requirements that minimize the overall cost is of interest. In this paper, we show how a cost analysis and optimization framework can be developed using stochastic availability and performance models of an IaaS Cloud. Specifically, we study two cost minimization problems to address the capacity planning in an IaaS Cloud: (1) what is the optimal number of PMs that minimizes the total cost of ownership for a given downtime requirement set by service level agreements? and, (2) is it more economical to use cheaper but less reliable PMs or to use costlier but more reliable PMs for insuring the same availability characteristics? We use simulated annealing, a well-known stochastic search algorithm, to solve these optimization problems. Results from our analysis show that the optimal solutions are found within reasonable time.

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.

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

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

The right setup of a testbed is important for enabling the rapid development and deployment of cloud services, in particular all the times the testbed hosts more concurrent interactions. This work shows how to configure an environment where more developers insist on it. In particular what tools are indispensable for managing compelling distributed cloud environments. We looked at mature Open Source solutions, performing their smoothed integration. The paper describes all parts necessary for accomplishing complex scenarios, even in a bounded perimeter as the VISION Cloud EU project offering. VISION Cloud represents the reference architecture dealing with Cloud Storage, and our work attempts to provide solutions applied on VISION, but suitable for any type of framework.

High availability is one of the key characteristics of Infrastructure-as-a-Service (IaaS) cloud. In this paper, we show a scalable method for availability analysis of large scale IaaS cloud using analytic models. To reduce the complexity of analysis and the solution time, we use an interacting Markov chain based approach. The construction and the solution of the Markov chains is facilitated by the use of a high-level Petri net based paradigm known as stochastic reward net (SRN). Overall solution is composed by iteration over individual SRN sub-model solutions. Dependencies among the sub-models are resolved using fixed-point iteration, for which existence of a solution is proved. We compare the solution obtained from the interacting sub-models with a monolithic model and show that errors introduced by decomposition are insignificant. Additionally, we provide closed form solutions of the sub-models and show that our approach can handle very large size IaaS 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.

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

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

Cloud based services may experience changes - internal, external, large, small - at any time. Predicting and quantifying the effects on the quality-of-service during and after a change are important in the resiliency assessment of a cloud based service. In this paper, we quantify the resiliency of infrastructure-as-a-service (IaaS) cloud when subject to changes in demand and available capacity. Using a stochastic reward net based model for provisioning and servicing requests in a IaaS cloud, we quantify the resiliency of IaaS cloud w.r.t. two key performance measures - job rejection rate and provisioning response delay.