Abstract Industrial processes often involve the generation—and the analysis—of multivariate time series data, which poses several challenges from the anomaly detection perspective. In addition to the need to detect previously unseen anomalies, the high dimensionality of industrial datasets introduces the complexity of simultaneously analyzing multiple features and their interactions. Finally, industrial datasets are typically highly imbalanced, with minimal information on anomalous processes. To address these issues, we propose a novel anomaly detection framework that introduces two embedding models, based on Time2Vec and Discrete Wavelet Transforms, leveraging their capabilities to represent multivariate time series as vectors while capturing and preserving temporal dependencies and combining them with several classifiers to enhance the overall performance of anomaly detection. We tested our solution using a publicly available benchmark dataset and a real industrial use case, particularly data collected from a Bonfiglioli gear manufacturing plant. The results demonstrate that, unlike traditional reconstruction-based autoencoders, which often struggle with sporadic noise, our embedding-based solutions maintain high performance across various noise conditions.

The manufacturing process of frozen dairy desserts, such as ice cream and gelato, is very sensitive to human errors in ingredient preparation: even minor variations in the ingredient mix can lead to quality issues and material waste. To become more sustainable, next generation ice cream making machines need to implement intelligent and adaptive processes that are both efficient and forgiving of human mistakes in mixture preparations. Toward that goal, we developed Hard-O-Tronic AI-driven (HOT-AI), a novel edge AI solution specifically designed for Carpigiani’s ice cream making machines. Leveraging the innovative multimilestone classification methodology, HOT-AI performs inference at multiple stages—or milestones—during the ice cream making process, with increasing accuracy over time. This enables HOT-AI to take corrective actions by adapting the preparation process accordingly, thus improving batch-to-batch uniformity, minimizing ingredient waste, and enhancing production efficiency, cost-effectiveness, and sustainability. HOT-AI has been successfully validated under real production conditions, and its large-scale implementation is planned across Carpigiani Group machines.

The growing adoption of Artificial Intelligence (AI) in network and service management demands extensive, diverse, and high-fidelity datasets for training and evaluation. However, collecting real-world network data at scale often faces significant challenges, including privacy concerns, operational constraints, and the rarity of certain events or conditions. Generative AI offers a promising solution by synthesizing realistic data that mirrors complex network dynamics and user behavior.In many application domains-such as mobile connectivity, cybersecurity, and disaster recovery-realism is not only defined by accurate replication of structural features (e.g., connectivity graphs), but also by the ability to model how these features evolve over time. Capturing these temporal dynamics is critical to ensure that AI models trained on synthetic data can generalize effectively to real-world scenarios. One effective approach to this challenge is to transform raw graph data into a compact latent representation, which can then be processed by a temporal generative model. This two-stage framework enables the learning of both structural and temporal characteristics of the underlying system, offering a more comprehensive generative pipeline.Building on previous work that employed Time-series Generative Adversarial Networks (TimeGAN) for this purpose, this paper explores an alternative temporal generative model: DoppelGANger. By integrating DoppelGANger into the graph generation pipeline, we aim to assess whether it can more accurately capture the dynamics of evolving graph structures. Furthermore, we introduce a more rigorous and detailed evaluation of the generated data by comparing decoded synthetic graph sequences against their real-world counterparts using distribution-aware and graph-structural metrics. These metrics provide a clearer picture of the quality and fidelity of the generated data, highlighting key differences between the TimeGAN and DoppelGANger approaches.

The increasing complexity and increasing demands of IT applications, especially in federated multi-cluster environments, pose significant challenges for service orchestration. To address these, Zero-Touch Service Management (ZSM) and intent-based management paradigms are gaining traction, allowing users to specify high-level goals rather than low-level configurations. However, current intent-driven approaches often rely on rigid Domain Specific Languages (DSLs) or graphic user interfaces, limiting expressiveness and usability. In this work, we propose a neurosymbolic intent-based platform that leverages Large Language Models (LLMs) for natural language intent ingestion and Answer Set Programming (ASP), a declarative programming paradigm used for solving complex combinatorial problems. The system translates natural language descriptions of microservice requirements into structured policies, enabling explainable service-to-cluster matching across federated Kubernetes environments. We validate our approach through experiments that evaluate both the syntactic correctness and efficiency of various LLMs in intent translation, as well as the computational time of the symbolic placement algorithm.

Machine Learning (ML) is becoming increasingly vital in various Big Data applications within Industry 5.0, such as predictive maintenance, zero-defect manufacturing, and process or supply chain optimization. However, the dynamic and high-stakes nature of manufacturing necessitates continuous monitoring, periodic reevaluation, and potential retraining of ML models to ensure they remain accurate and aligned with the evolving operational context. This paper presents a Machine Learning Operations (MLOps) platform tailored for Industry 5.0 applications, taking advantage of the whole Compute Continuum, from the edge to the cloud, designed to be scalable and resource-efficient. Built on Kubernetes and the Kubeflow framework, the platform enables seamless management of the entire ML lifecycle, from model development to deployment, across multi-cluster environments. Using Bonfiglioli’s real-world anomaly detection use case, the platform demonstrated its ability to support robust, low-latency ML inference services under varying workloads, even on resource-constrained edge devices. Experimental results confirm the platform’s efficiency, scalability, and practical applicability in addressing zero-defect and zero-waste manufacturing requirements.

Recent advancements in Machine Learning (ML) and MLOps for Industry 5.0 have significantly boosted productivity in manufacturing by enabling predictive maintenance and optimizing industrial workflows. However, implementing ML applications in real-world industrial environments presents several challenges, including limited access to labeled data, stringent privacy requirements, and the decentralized nature of industrial data. An effective solution for distributed learning with unlabeled data is essential to address these issues. In this paper, we introduce FedEdge-Learn, a novel Federated Learning framework tailored for Industry 5.0 applications. It focuses on unsupervised K-means clustering enhanced by globally shared data. Our approach safeguards data privacy other than accelerating the onboarding of new machines by leveraging the globally trained model. We validate our framework using both public datasets and real-world industrial data, demonstrating its effectiveness in real-world scenarios. The results show how, with our framework, the K-means algorithm is effective in federated settings, without a significant performance decrease compared to the centralized case.

Softwarized networking solutions are a key enabler for effective and efficient communications in natural disaster recovery scenarios. However, the design development of reliable and robust softwarization solutions in this context is hampered by the scarcity of reference datasets which accurately capture the real-world behavior – and variability – of those environments. This paper presents a method for synthetic generation of sequences of graphs using state-of-the-art Graph Neural Networks (GNNs) and Time-series Generative Adversarial Networks (TimeGAN). By leveraging available real-world data, the proposed approach generates synthetic datasets that closely replicate the features and connectivity patterns found in actual scenarios. These synthetic datasets not only support the training of AI models but also enable testing and evaluation of solutions across different but similar scenarios. Preliminary results using the Anglova scenario show that our solution accurately captures spatio-temporal behaviour in disrupted networks, making it a powerful tool for developing and validating systems in fields where access to real-world data is limited, enhancing their generalizability and reliability.

Kubernetes (K8S) is a widely used orchestration solution that helps manage complex IT applications by providing mechanisms for autoscaling, health checking, cluster formation, and replication, which are essential to deploy and manage the multitude of connected microservices. However, they may suffer in case of unexpected faults which can severely change the underlying computing infrastructure and lead to service outages, highlighting the need for resilient solutions capable of mitigating the adverse effects of faults. To address this, the TELKA sched-uler integrates Chaos Engineering (CE), Reinforcement Learning (RL), and Digital Twin (DT) to reallocate K8S pods evicted due to unexpected faults. While TELKA showed promising results in reallocating evicted pods, its preliminary implementations suffered from scalability issues, as the RL agent could only effectively operate on scenarios with the same number of nodes seen during training. To overcome this limitation, this paper improves TELKA by incorporating a neural network architecture called Deep Sets (DS), which can generalize the operation of TELKA on different numbers of nodes. Experimental results not only demonstrate the validity of the improved TELKA but also show how it can be used to identify good operating conditions.

The Compute Continuum (CC) represents a set of computing resources residing from remote cloud datacenters to dedicated hardware at the edge of the network. Modern applications based on the composition of several microservices can strongly benefit from tools that realize optimal deployment based on the characteristics of each microservice, the availability of computing resources across the CC, the end-users latency, and pricing perspectives. To realize such goals, there is the need for sophisticated solutions capable of efficiently exploring a large space of potential configurations. In this regard, Reinforcement Learning (RL) and Computational Intelligence (CI) techniques represent valuable approaches. However, one of the critical challenges remains the adaption of these deployments to highly dynamic scenarios such as the CC. When the availability of the computing resources changes, there is the need to re-optimize the deployment efficiently. This calls for solutions with reactive or proactive capabilities to deal with the dynamicity of these ecosystems. The work considers a CC-inspired scenario by exploring hybridization techniques that combine CI and RL to devise a hot restart approach for metaheuristics and assess a new deployment solution efficiently. Preliminary results show the soundness of the proposed hybridization methods in handling severe system changes.

With the advent of containerization technologies, microservices have revolutionized application deployment by converting old monolithic software into a group of loosely coupled containers, aiming to offer greater flexibility and improve operational efficiency. This transition made applications more complex, consisting of tens to hundreds of microservices. Designing effective orchestration mechanisms remains a crucial challenge, especially for emerging distributed cloud paradigms such as the Compute Continuum (CC). Orchestration across multiple clusters is still not extensively explored in the literature since most works consider single-cluster scenarios. In the CC scenario, the orchestrator must decide the optimal locations for each microservice, deciding whether instances are deployed altogether or placed across different clusters, significantly increasing orchestration complexity. This paper addresses orchestration in a containerized CC environment by studying a Reinforcement Learning (RL) approach for efficient microservice deployment in Kubernetes (K8s) clusters, a widely adopted container orchestration platform. This work demonstrates the effectiveness of RL in achieving near-optimal deployment schemes under dynamic conditions, where network latency and resource capacity fluctuate. We extensively evaluate a multi-objective reward function that aims to minimize overall latency, reduce deployment costs, and promote fair distribution of microservice instances, and we compare it against typical heuristic-based approaches. The results from an implemented OpenAI Gym framework, named as HephaestusForge, show that RL algorithms achieve minimal rejection rates (as low as 0.002%, 90x less than the baseline Karmada scheduler). Cost-aware strategies result in lower deployment costs (2.5 units), and latency-aware functions achieve lower latency (268–290 ms), improving by 1.5x and 1.3x, respectively, over the best-performing baselines. HephaestusForge is available in a public open-source repository, allowing researchers to validate their own placement algorithms. This study also highlights the adaptability of the DeepSets (DS) neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. The DS neural network can handle inputs and outputs as arbitrarily sized sets, enabling the RL algorithm to learn a policy not bound to a fixed number of clusters.

In the aftermath of natural disasters, Human Assistance & Disaster Recovery (HADR) operations have to deal with disrupted communication networks and constrained resources. Such harsh conditions make high-communication-overhead ML approaches — either centralized or distributed — impractical, thus hindering the adoption of AI solutions to implement a critical function for HADR operations: building accurate and up-to-date situational awareness. To address this issue we developed Roaming Machine Learning (RoamML), a novel Distributed Continual Learning Framework designed for HADR operations and based on the premise that moving an ML model is more efficient and robust than either large dataset transfers or frequent model parameter updates. RoamML deploys a mobile AI agent that incrementally train models across network nodes containing yet unprocessed data; at each stop, the agent initiate a local training phase to update its internal ML model parameters. To prioritize the processing of strategically valuable data, RoamML Agents follow a navigation system based upon the concept of Data Gravity, leveraging Multi-Criteria Decision Making techniques to simultaneously consider many objectives for Agent routing optimization, including model learning efficiency and network resource utilization, while seamlessly blending subjective insights from expert judgments with objective metrics derived from quantifiable data to determine each next hop. We conducted extensive experiments to evaluate RoamML, demonstrating the framework’s efficiency to train ML models under highly dynamic, resource-constrained environments. RoamML achieves similar performance to centralized ML training under ideal network conditions and outperforms it in a more realistic scenario with reduced network resources, ultimately saving up to 75% in bandwidth utilization across all experiments. • Human Assistance & Disaster Recovery (HADR) requires accurate situational awareness. • Most distributed ML approaches assume a stable network and are unsuited for HADR. • Approaches based on distributed AI agents and continual learning are more resilient. • Data Gravity represents a solid foundational concept for agent routing optimization. • Data Gravity and MCDM allow to prioritize the processing of critical datasets.

Orchestrating microservice applications deployed on a federation of globally distributed Kubernetes clusters is a challenging and multifaceted optimization problem. It is not only computationally hard, but also requires balancing a delicate trade-off between competing performance metrics, such as latency, deployment cost, and service interruption frequency. Classical approaches in the literature merge multiple objectives into a single one via, e.g., linear combinations. However, in practice, it is complex to express a priori a quantitative preference between heterogeneous objectives, let alone with simple linear combinations. This paper adopts a more comprehensive approach leveraging proper Multi-Objective Optimization (MOO), with the goal of producing multiple solutions from the Pareto Front (PF). Therefore, the orchestrator can inspect a posteriori all possible "optimal" trade-offs and decide on the strategy that best fits their operating requirements. To solve the MOO problem, this paper adopts state-of-the-art Multi-Objective Evolutionary Algorithms and shows their effectiveness in solving the MOO problem. Illustrative results highlight the practical benefits of a MOO formulation, providing several tens of nondominated solutions and evenly covering the objectives’ space.

Machine learning offers a promising avenue for improving the efficiency and effectiveness of decision-making in disaster recovery and relief efforts. These operations face significant hurdles due to the large volumes of data, intermittent connectivity, and infrastructure limitations. In this paper, we present the RoamML Platform, a sophisticated modular implementation of the RoamML framework, designed specifically to address these challenges and enable efficient distributed machine learning. We advocate for a foundational principle that "the transmission of the ML model itself is usually more efficient than the costly transfer of large datasets", leading to a more adaptable training regime. The platform orchestrates the activities of the RoamML model along with its related metadata, collectively referred to as the "RoamML Agent", while faithfully observing the Data Gravity principle to guarantee thorough model training. We extensively validated the platform through a simulated disaster recovery scenario employing the Mininet-WiFi emulator. Our results highlight the benefits of integrating the RoamML framework, including enhanced ML performance and significant bandwidth savings.

Chaos engineering is the discipline of injecting computing and network faults, such as increased network latency and unavailability of computing nodes, into an IT system to help developers in identifying problems that could arise in a production environment and tackle them. Several tools have emerged to ease the application of chaos engineering to complex IT systems, leveraging microservice and container-based applications deployed on Kubernetes. However, applying of such tools requires several phases to be put into practice, from defining a steady state to establishing an effective response plan if something goes wrong. To ease the application of chaos engineering in improving the resilience of Kubernetes applications, this work presents a smart scheduler for Kubernetes called TELKA: a Twin-Enhanced Learning for Kubernetes Applications, which combines chaos engineering, Digital Twin (DT), and Reinforcement Learning (RL) methodologies to mitigate the effects of computing and network faults. Instead of interacting directly with the physical Kubernetes application, TELKA learns by interacting with a digital twin, thus reducing the learning time and the operation costs related to the application of chaos engineering. Experiment results compare TELKA with other approaches to show its effectiveness in mitigating the adverse effects of injected faults.

Machine Learning (ML) plays an increasingly important role in many Big Data applications in Industry 5.0: predictive maintenance, zero defect manufacturing, process and/or supply chain optimization, etc. However, the dynamic and high-stakes nature of the manufacturing environment requires ML models to be maintained through continuous monitoring, periodical reevaluation, and possible retraining to ensure they remain accurate and relevant to the actual context. In addition, to match the desired performance (as well as security and safety) requirements ML models need to be executed in different locations along the edge-to-Cloud continuum (and possibly migrated in case of need), on dedicated serving runtimes that suit the specific needs of the use case. To address these issues, we realized an MLOps platform that is capable of managing ML models through their entire lifecycle and enabling their deployment in different ML serving runtimes. More specifically, the initial experimental evaluation presented in the paper focuses on Bento Yatai and TorchServe serving runtimes. It demonstrates that our platform is capable of effectively running ML models on both runtimes and provides a comparative evaluation at both the quantitative and qualitative levels.

Within the Industry 4.0 vision, digital twins (DTs) have gained great attention as a promising approach to improve remote monitoring and control by means of virtual representations of physical objects. However, while DTs are becoming more and more sophisticated and even adopted for mission-critical applications, their resilience assessment has not received the required consideration yet. This article originally proposes chaos engineering to assess and improve the resilience of DTs by testing multiple aspects of industrial environments in a coordinated, automated, and replicable manner. First, the article discusses why and how chaos engineering is promising to improve the resilience of DTs. Then, it identifies and introduces a set of chaos engineering profiles specifically designed to take into account the many aspects an industrial environment is composed of. Finally, it shows the feasibility of assessing the resilience of a proof-of-concept DT through a testbed based on widely-adopted, open-source tools.

Kubernetes is a well-known orchestration and management solution for complex and large-scale service architectures in the Cloud Continuum. While it provides very valuable functions from the operation perspective, the high number of control loops it implements significantly enlarges the already wide space of configuration parameters and policies to consider for management purposes. We argue that optimizing complex Kubernetes deployments considering a multi-cloud and edge computing environment would significantly benefit from a Digital Twin approach, enabling an accurate virtual representation of a Kubernetes application to optimize its deployment and management policies. Towards that goal, this work illustrates the design of KubeTwin, a framework to implement Digital Twins of Kubernetes deployments. Furthermore, we present a validation of KubeTwin in a Multi-access Edge Computing (MEC) scenario, which shows its soundness in reenacting realistic Digital Twins of complex and highly distributed Kubernetes deployments. We believe that KubeTwin can provide useful guidance to the research community working in this field.

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

Traditional industrial networks were characterized by flat topologies, where industrial equipment exchanged a limited number of messages. In sharp contrast, modern manufacturing plants are evolving towards articulated environments generating an ever-increasing amount of network traffic. Such emerging environments prevent the adoption of traditional solutions based on end-to-end dispatching of few messages in reliable networks with bandwidth availability much greater than needed. This leads to the need to adopt proper message management strategies as close as possible to industrial equipment to avoid overwhelming the industrial network with non-mission-critical traffic at the expense of mission-critical one. This paper originally proposes edge-powered in-network processing to i) transparently manage messages sent by industrial equipment, ii) support a broad spectrum of message management strategies, ranging from efficient header-based solutions to expressive content-based ones, and iii) fulfill the application-dependent requirements demanded by industrial environments nowadays. Achieved performance results based on a proof-of-concept prototype demonstrate that the proposed solution efficiently provides content-based message management at the edge, even considering edge nodes with limited hardware capabilities.

Smart metering represents an interesting field, where IoT can bring huge benefits to collect and analyze water and environmental data. However, it presents well-known issues such as the presence of high heterogeneity at the hardware, software, and network layers. This is even amplified by the vendors’ tendency to adopt proprietary solutions and different low-power wireless communication protocols for IoT sensors and metering devices. Realizing an interoperable platform for the collection and analysis of smart Water and environmental data metering thus becomes a complex task, that needs to address multiple requirements at several levels, starting from the collection of data on the field (IoT sensors, smart meters) and its processing on Cloud Computing platforms. The Water 4.0 project, which involves a collaboration between universities and private companies, including Dipietro Group, aims at addressing these challenges. This paper presents the comprehensive smart environmental data metering solution that we realized within Water 4.0, that enables data collection, data-processing, and Over-The-Air (OTA) for IoT devices, with the ultimate goal of reducing water losses and monitoring water quality.

Fog and Edge Computing aim to deliver low-latency, immersive, and powerful services by processing information close to both devices and users. This is well suited for IoT applications in Smart City, where IoT gateways, Cloudlets, Base Stations, and other computational nodes can process (part of) the data generated by the multitude of IoT sensors directly at the edge of the network. However, the implementation of Fog and Edge Computing is challenging because it requires to deal with a (limited number of) constrained devices, dynamic services’ requirements, and heterogeneous network conditions. Differently from the Cloud, where computational resources are supposed to be unlimited, Fog and Edge services should be capable to adapt to scarce and constrained resources and deal with the deluge of IoT data. To facilitate the adoption of Fog and Edge Computing this work proposes middleware solutions that leverage Value-of-Information (VoI) as interesting criterion to select only the most valuable piece of information for processing and dissemination and to scale computational workload in an automated fashion.

Testing the resiliency of complex IT services deployed in hybrid Cloud scenarios is a challenging task that requires expensive and possibly destructive operations. An interesting approach lies in Chaos Engineering, a set of practices to test the resiliency of software systems running in a production environment. However, Chaos Engineering is an expensive practice that requires the setup of complicated operations that further increase the complexity of management operations. To reduce this complexity, Chaos Engineering can benefit from the adoption of non-destructive approaches such as the definition of realistic digital twins. A digital twin is a virtual replica of a real-system on which experimenting with management configurations. This paper embraces this research avenue by extending our previous efforts to integrate Chaos Engineering techniques into an IT services management framework called ChaosTwin. ChaosTwin leverages novel methodologies and tools capable of identifying and promptly react to unexpected failures. Finally, to implement autonomous fault management, ChaosTwin defines scaling and migration policies that can quickly explore for more resilient placements of software components in case of system failures. We believe that ChaosTwin can provide useful guidance to service providers in finding cost-effective service configurations capable of minimizing the negative effects of unpredictable events.

The industrial internet of things has radically modified industrial environments, not only enabling novel services but also dramatically increasing the amount of generated traffic. Nowadays, a major concern within industrial plants is to support network-intensive services, such as real-time remote vibration monitoring of autonomous guided vehicles, while ensuring the prompt and reliable delivery of mission-critical safety-related messages among machines and the control room. To this purpose, we present a novel solution jointly orchestrating content-based message management and traffic flow steering: the former enables edge-powered in-network processing modules to process packet payloads as they traverse the industrial backbone, the latter supports dynamic (re)routing of traffic flows towards such processing modules. In particular, we exploit software-defined networking for flexible traffic flow (re)routing and Kubernetes for dynamic deployment on edge nodes of in-network processing modules for content-based message management. As demonstrated by performance results based on our working proof-of-concept prototype, our solution efficiently allows to manage industrial traffic flows in a coordinated fashion, by considering requirements of concurrently running industrial applications and the current state of the overall topology.

The advent of the Internet of Things (IoT) and its spread in industrial environments has changed production lines by dramatically fostering the dynamicity of data sharing and the connectivity of machines. However, such increased flexibility (also pushed by the adoption of edge devices) must not negatively affect the security and safety of industrial environments. The proposed solution adopts the blockchain to securely store in distributed ledgers topology information and access rules, maximizing the cyber resiliency of industrial networks. Topology information and access rules are stored and queried in a completely distributed manner, ensuring data availability even in case a centralized controller is temporarily down or the network partitioned. Moreover, blockchain consensus algorithms foster a participative validation of topology information, to ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a nonrepudiable manner. Finally, the adoption of configurable edge gateways allows to take prompt countermeasures in case potential threats are identified, by activating access rules stored in ledgers in a secure and distributed manner. In addition to solution design guidelines and architectural considerations, the article also presents performance results achieved with our CyberChain working prototype, with the goal of not only demonstrating the feasibility of the proposed solution but also its suitability in industrial environments.

The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.

Chaos Engineering represents an interesting software engineering methodology to improve the resilience of a complex IT system operating in a live production environment by injecting simulated faults, observing the system reaction, and devising mitigating solutions. However, Chaos Engineering is an expensive practice with a high setup and operation overhead and it often focuses on the evaluation of the system behavior from a relatively narrow technical perspective instead of a more comprehensive business level one. To enlarge the audience of Chaos Engineering there is the need for novel solutions that can give service providers the tools to deal with the deployment and testing of complex IT services. To fill this gap, this paper presents ChaosTwin, a novel solution exploring an innovative approach to apply Chaos Engineering to a digital-twin, i.e., a virtual representation of a physical object or a system. By creating realistic digital twin of an IT service, injecting faults on the digital twin and evaluating how different service configuration and fault management strategies would perform from a business level perspective, ChaosTwin provides useful guidance to service providers in finding cost-effective service configurations that can minimize the negative effects of unpredictable events. Experimental results, collected from the evaluation of a realistic case study, demonstrate how ChaosTwin is capable of minimizing both the associated costs and the effects of injected Chaos faults.

Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange protocols, and the different network technologies and addressing schema toward the machines. To address those issues, the paper proposes a distributed support based on a Message Oriented Middleware (MOM) and a Software Defined Network (SDN) control plane that coordinate to enable semantic routing by also allowing traffic differentiation as well as in-network processing at intermediate network nodes. Seminal results, collected in realistic industrial settings, confirm the feasibility of our proposal.

Service fabric management in Fog Computing is a challenging task, which has to deal with a complex and resource scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management for Fog Computing applications. First, we formalize the VoI concept and discuss its adoption in Fog Computing environments. Then, we propose a formal model that aims at maximizing the allocation of Fog services from a value-based perspective. To overcome the complexity of this model, we present two possible approaches (simulation-based optimization and a model approximation) and we compare them by adopting Evolutionary Algorithms (EAs) as optimization techniques. Experimental results prove the validity of both models in finding resource allocation solutions capable of minimizing network latency and maximizing the utility for the end-users of Fog Computing services. Finally, we show how the results of the approximated model can be adopted as a first approximated approach for resource management of Fog Computing services.

The advent of the Internet of Things (IoT) together with its spread in industrial environments have changed pro-duction lines, by dramatically fostering the dynamicity of data sharing and the openness of machines. However, the increased flexibility and openness of the industrial environment (also pushed by the adoption of Edge devices) must not negatively affect the security and safety of production lines and its opera-tional processes. In fact, opening industrial environments towards the Internet and increasing interactions among machines may represent a security threat, if not properly managed. The paper originally proposes the adoption of the Blockchain to securely store in distributed ledgers topology information and access rules, with the primary goal of maximizing the cyber-resiliency of industrial networks. In this manner, it is possible to store and query topology information and security access rules in a completely distributed manner, ensuring data availability even in case a centralized control point is temporarily down or the network partitioned. Moreover, Blockchain consensus algorithms can be used to foster a participative validation of topology information, to reciprocally ensure the identity of interacting machines/nodes, to securely distribute topology information and commands in a privacy-preserving manner, and to trace any past modification in a non-repudiable manner.

In modern society, food safety is becoming more and more important. The adoption of appropriate practices, such as the ones defined in the HACCP system, during food production, handling, preparation, and storage can reasonably guarantee food safety. However, it is not easy to apply HACCP methodologies in an automatic form, thus hindering its use in industrial machines. To solve this problem, the paper presents a novel solution adopting Internet of Things (IoT) and Blockchain technologies in the ice cream production process to automate the enforcement of HACCP directives. The new Carpigiani ice cream making machines exploit IoT for the automation of data gathering (in particular the temperature, that is of particular concern for dairy products) and a Blockchain solution for a tamper-proof and non-repudiable distributed storage of HACCP sensitive production data.

The widespread adoption of information and communication technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably reference architecture model industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, the ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state of the art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive on-the-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.

Tactical edge networks provide one of the most challenging environments for communications, which significantly complicates the development of efficient and robust information dissemination solutions. In our previous work, we found that exploiting highly mobile nodes, such as Unmanned Air Vehicles, with cyclic mobility patterns, as message ferries can significantly improve the performance of information dissemination solutions. However, our experience demonstrated that robust forecasting mechanisms are essential in order to withstand frequent changes in the mobility patterns of message ferrying nodes. This paper presents an extension of the adaptive node presence forecasting component developed for DisService, a Peer-to-peer information dissemination system, that provides estimates of tolerance and accuracy of node mobility forecasts. We tested the extended forecasting mechanism in a simulation environment and found that it can lead to significant improvements in the timeliness and reliability of information dissemination.

As IT services grow more and more complicated, and their management becomes increasingly challenging, IT support organizations assume an essential role to ensure the delivery of Service Level Objectives. What-if scenario analysis represents a very effective tool for the performance optimization of IT support organizations, as it enables an iterative and customized performance optimization process. The problem of accurately modeling IT support organizations requires the development of sophisticated models as well as dedicated parameter inference techniques and tools. This paper presents a multiple-priority queuing model suited for the reenactment of IT support groups developed from the analysis of empirical evidence, as well as a powerful method to infer the model parameters. We applied our model to reenact the behavior of a real life IT support group with our Symian simulator. The results demonstrate that multiple-priority queuing models can reproduce real life IT support groups with a high degree of accuracy.

IT services are getting increasingly complicated, and require IT support organization to manage them. IT support organization are in charge of the incident management process and represent mission-critical structures whose performance needs to be frequently assessed and optimized. State-of-the-art research in the performance optimization of IT support organization proposes user-driven performance assessment and optimization processes based on what-if scenario analysis tools that implement sophisticated IT support organization models. This manuscript instead represents a preliminary study of a different kind of optimization processes, of the closed-loop type, that try to autonomously identify optimal IT support organization configurations according to inputs provided by the user. This paper discusses the development challenges in realizing decision support tools for closed-loop optimization processes and presents a prototype system. The preliminary evaluation of our tool demonstrates that closed-loop processes might be impractical as reference tools but can effectively complement and extend human-driven ones.

IT support organizations are in charge of restoring normal operations after IT service disruptions, among other tasks. Such organizations can be complex systems, with a large network of interacting support groups subject to complex management policies. The performance assessment and optimization of IT support organizations is an extremely challenging task that requires considering organization-specific structure, behavior, and business-level objectives. This paper presents Symian-Web, a decision support tool that enables IT managers to both assess and improve IT support organization performance by using what-if scenario analysis. Symian-Web features advanced information visualization concepts and metaphors, allowing for precise and timely assessment of IT support organization performance, and facilitating their redesign. Symian-Web is realized as a cloud computing-based Web application, thus enabling the tool to take advantage of the on-demand computational capabilities provided by the cloud.

In modern manufacturing, the integration of ICT in the maintenance process, led to the development of e-maintenance, that automates management operations. E-maintenance, that initially interested only large plant machinery, is now becoming affordable for mass-produced equipment, thanks to the recent advances in ICT. This paper presents Teorema, an innovative e-maintenance solution for Carpigiani ice cream machines, which provides several services: remote monitoring of machines, automatic notification of malfunctions, diagnostics and prognostics functions, remote assistance interventions, and automated reporting of production data. Teorema is already in production and it is significantly improving the after-sale service to Carpigiani customers.

Business-driven IT management (BDIM) aims at ensuring successful alignment of business and IT through thorough understanding of the impact of IT on business processes and business results, and vice versa. This thesis reviews the state of the art of BDIM research and advances it by contributing a comprehensive BDIM solution for the process of IT incident management. The solution can be used as a template for applying the BDIM methodology to other IT service management processes. The work presented in this dissertation resulted in three patent applications and is at the core of the HP IT Analytic™ product (formerly HP DecisionCenter™). This thesis was defended on March 2009.

Business-driven IT management (BDIM) is a recent research effort to drive IT management decisions from a business perspective by considering business indicators such as profit, cost, and customer experience. BDIM studies complicated decision making processes, dealing with the relationship between the IT function and the business value it generates. The present paper aims at stimulating the discussion on decision making theory and practice within the BDIM research community, in order to develop a better understanding of the theoretical background and consequently improve current tools and practices. To this end, the paper analyzes the most challenging aspects of decision making in BDIM and proposes a few discussion topics that could be of interest for future research studies.

There is great interest in building an accurate theoretical model of IT support organizations, for several purposes such as optimal workforce allocation and what-if scenario analysis. However, the complexity of real-life IT support organizations makes it extremely hard to model their organizational, structural and behavioral processes. While the adoption of stationary stochastic processes to model incident arrivals and of first-come-first-served GI/G/N queues to model support groups permits to reproduce with good enough fidelity the organization-wide behavior, this approach does not always accurately capture the internal dynamics of the organization. This paper presents an experimental analysis of transaction logs from a real-life IT support organization, provided to us by the Outsourcing Services Division of HP. The statistical analysis of transactional logs allows us to make some interesting considerations that can be used to build a more accurate model of the organization, as well as to gain useful experience in the modeling process.

Incident Management is the process through which IT support organizations manage to restore normal service operation after a service disruption. The complexity of real-life enterprise-class IT support organizations makes it extremely hard to understand the impact of organizational, structural and behavioral components on the performance of the currently adopted incident management strategy and, consequently, which actions could improve it. This paper presents SYMIAN, a decision support tool for the performance improvement of the incident management function in IT support organizations. SYMIAN simulates the effect of corrective measures before their actual implementation, enabling time, effort, and cost saving. To this end, SYMIAN models the IT support organization as an open queuing network, thereby enabling the evaluation of both the system-wide dynamics as well as the behavior of the individual organization components and their interactions. Experimental results show the SYMIAN effectiveness in the performance analysis and tuning of the incident management process for real-life IT support organizations.

Service disruptions can have a considerable impact on business operations of IT support organizations, thus calling for the implementation of efficient incident management and service restoration processes. The evaluation and improvement of incident management strategies currently in place, in order to minimize the business-impact of major service disruptions, is a very arduous task which goes beyond the optimization with respect to IT-level metrics. This paper presents HANNIBAL, a decision support tool for the business impact analysis and improvement of the incident management process. HANNIBAL evaluates possible strategies for an IT support organization to deal with major service disruptions. HANNIBAL then selects the strategy with the best alignment to the business objectives. Experimental results collected from the HANNIBAL application to a realistic case study show that business impact-driven optimization outperforms traditional performance-driven optimization.

Taking advantage of the benefits of modern networking, a growing number of users are exhibiting mobile behavior. As they roam between different network localities, they access the Internet and the Web exploiting both wired and wireless communications and using several heterogeneous devices. Mobile users want to access their subscribed services anywhere, anytime, and want to preserve their currently opened service sessions as they roam between different network localities or switch between different devices. Mobile userspsila requirements call for novel middlewares to provide support for mobility on top of the traditional Internet infrastructure. In this context, we have developed Mockets, a communication middleware specifically designed to address the challenges of wireless networks and mobile computing. In particular, Mockets supports session mobility in terms of seamless handover for preservation of end-to-end connectivity in spite of node mobility, automatic detection and exploitation of best available connectivity, and migration of service session endpoints from one node to another.

The widespread diffusion of mobile computing calls for novel services capable of providing results that depend on both the current physical position of users (location) and the logical set of accessible resources, subscribed services, preferences, and requirements (context). Leaving the burden of location/context management to applications complicates service design and development. In addition, traditional middleware solutions tend to hide location/context visibility to the application level and are not suitable for supporting novel adaptive services for mobile computing scenarios. The article proposes a flexible middleware for the development and deployment of location/context-aware services for heterogeneous data access in the Internet. A primary design choice is to exploit a high-level policy framework to simplify the specification of services that the middleware dynamically adapts to the client location/context. In addition, the middleware adopts the mobile agent technology to effectively support autonomous, asynchronous, and local access to data resources, and is particularly suitable for temporarily disconnected clients. The article also presents the case study of a museum guide assistant service that provides visitors with location/context-dependent artistic data. The case study points out the flexibility and usability of the proposed middleware that permits automatic service reconfiguration with no impact on the implementation of the application logic.

The full and seamless integration of wireless devices with traditional fixed networks is more and more important to foster the mobile and ubiquitous access to the Internet. In particular, the heterogeneity and resource limitations of wireless devices motivate novel support infrastructures that can facilitate the wired-wireless integration and can provide service tailoring depending on client characteristics. The paper presents an application-level portable middleware, called ubiQoS, for QoS-enabled audio streaming to Bluetooth clients. ubiQoS exploits support proxies for QoS tailoring and for managing the QoS over the last segment of the audio distribution path towards the clients, by using different types of Bluetooth links. Proxies execute at the wired-wireless network edges and can even migrate to follow the device movements, where and when needed. The reported experimental results show the feasibility of the application-level approach in the challenging case of QoS-enabled audio streaming to resource-limited Bluetooth devices.

The ultimate goal of mobile and ubiquitous Internet accessibility is not only the seamless integration of wireless devices with traditional fixed networks but also the dynamic differentiation of quality of service (QoS) levels depending on client characteristics. In this context, the paper presents the provisioning of audio streaming with different QoS levels in the application-level ubiQoS middleware. In particular, it focuses on how ubiQoS manages the QoS over the last segment of the audio distribution path towards Bluetooth clients by allocating different types of Bluetooth communication channels (unicast connection-oriented or broadcast connectionless) depending on the differentiated QoS requirements of different user classes. To this purpose, we have developed a library that extends the JSR82 standard with the support of active slave broadcast, thus simplifying the Java-based management of Bluetooth communications. The reported experimental results show the feasibility of our application-level middleware approach in the challenging case of audio streaming with differentiated QoS to resource-limited Bluetooth devices.

The convergence between the Internet and telecommunication systems promotes an integrated scenario characterized by different flavors of mobility. Users can connect to the network from ubiquitous points of attachment and wireless portable devices can roam by maintaining continuous connectivity. Novel middleware technologies based on code mobility has the potential to enhance service provisioning to mobile users/devices. However, code mobility adds complexity to the design of applications and calls for new approaches for the programming of code mobility strategies. Separation between mobility and computational concerns is crucial to reduce the complexity of code mobility control and to favor rapid mobile code-based service prototyping, run-time configuration and maintenance. To achieve the needed degree of separation of concerns the paper advocates the adoption of policies and proposes a policy-based framework for dynamic code mobility management. In addition, the paper explores a reflective-based approach to mobility control and compares policy with reflective-based programming solutions to point out the main differences and lessons learned.

The provisioning of Web services over the wireless Internet introduces novel challenging issues for service design and implementation: from user/terminal mobility during service execution, to wide heterogeneity of portable access devices and unpredictable modifications in accessible resources. In this scenario, there are frequent provision-time changes in the context, defined as the logical set of accessible resources depending on client location, access terminal capabilities, and system/service management policies. The development of context-dependent services requires novel middlewares with full context visibility. We propose a middleware for context-aware resource management, called CARMEN, capable of supporting the automatic reconfiguration of wireless Internet services in response to context changes without any intervention on the service logic. CARMEN determines the context on the basis of metadata, which include declarative management policies and profiles for user preferences, terminal capabilities, and resource characteristics. In addition, CARMEN exploits the mobile agent technology to implement mobile middleware components that follow the provision-time movement of clients to support locally their customized service access. The proposed middleware shows how metadata and mobile agents can favor component reusability and automatic service reconfiguration, by reducing the development/ deployment complexity.

The control and management of Web-based service quality require the extension of the Internet infrastructure with monitoring functions to ascertain dynamically the state of networked resources. We describe the design and implementation of the Monitoring Application Programming Interface (MAPI), a Java-based tool for the on-line monitoring of Internet heterogeneous resources, which provides monitoring indicators at different levels of abstraction. At the application level, it instruments the Java Virtual Machine (JVM) to notify several different types of events triggered during the execution of Java applications, e.g. object allocation and method calls. At the kernel level, MAPI inspects system-specific information generally hidden by the JVM, e.g. CPU usage and incoming network packets, by integrating with Simple Network Management Protocol agents and platform-dependent monitoring modules. MAPI is the core part of a portable tool for distributed monitoring, control and management in the Internet environment. The tool is implemented in terms of mobile agents that move close to the monitored resources to enforce distributed management policies autonomously, with a significant reduction in both reaction time and traffic overhead.

Mobile code (MC) technologies provide appealing solutions for the development of Internet applications. For instance, Java technology facilitates dynamic loading of application code from remote servers on to heterogeneous clients distributed all over the Internet. However, executing foreign code that has been loaded from the network raises significant security concerns which limit the diffusion of these technologies. Substantial work has already been done to provide security solutions for protecting both hosting nodes and MC. For example, the Java security architecture evolved from a rigid sandbox model to a more flexible solution where downloaded code can perform any kind of operation, depending on its source location and signature. However, the most widespread security solutions for MC platforms today do not support the sophisticated security policies required in modern inter-organisational environments. This requires expressive languages to specify the policy and flexible mechanisms for policy implementation which cater for code mobility. This paper shows how access control policies for MC-based applications can be specified in a concise and declarative language called Ponder, and how these policies can be implemented within the Java security architecture.

The telecommunication and the Internet scenarios have pointed out the possibility of accessing resources and services while moving in open distributed global systems. Mobility should allow users to access services and to maintain their preferred working environment independently of their current point of attachment, and has motivated the investigation of new models and solutions. The mobile agent technology is intrinsically suitable to describe, model and implement mobility. The paper describes how a mobile agent framework, called SOMA, can provide an infrastructure to support not only the traditional concepts of terminal and user mobility but also the mobility of resources in general. SOMA permits terminal mobility by introducing the mobile place abstraction that represents a mobile host for agent execution, and user mobility by supporting the virtual home environment service. SOMA supports resource mobility via the resource discovery service that can preserve client/server relationships among SOMA resources and users independently of current positions. The paper also gives experimental results about the costs associated with the main mechanisms for supporting terminal, user, and resource mobility.

Technological and human factors have contributed to increase the complexity of the network management problem. Heterogeneity and globalization of network resources, on one hand, have increased user expectations for flexible and easy-to-use environments; on the other hand, they have suggested entirely novel ways to face the management problem. Several research efforts recognize the need for integrated solutions to manage both network resources and services in open, global, and untrusted environments. In addition, these solutions should permit the coexistence of different management models and should interoperate with legacy systems. In the paper, we define a general architecture based on a distributed processing environment (DFE) that offers a large set of facilities to the application level. We have developed the MESIS management environment shaped after the above architecture and its DPE facilities with mobile agents technology. MESIS handles, in a uniform way, both resources and services, and focuses on two crucial properties: interoperability to overcome heterogeneity, and security to grant users safe and protected operations. The Agent Interoperability Facility supports compliance with CORBA-based management systems and with MASIF agent platforms. The Agent Security Facility provides authentication, integrity, privacy, authorization, and secure interoperation with CORBA systems.

The Mobile Agent (MA) paradigm seems to be a promising technology for developing applications in open, distributed and heterogeneous environments, such as the Internet. Mobile agents can overcome some of the limits of the traditional client/server model and can easily integrate with the Web to improve application accessibility. Many application areas, such as electronic commerce, mobile computing, network management and information retrieval can benefit from the application of the MA technology. However, a wider diffusion of MA is currently limited by the lack of a comprehensive security framework. Answering to the requirement of protection for both execution sites and mobile agents can boost the acceptance of the MA paradigm in the Internet environment. The paper describes an MA environment, called Secure and Open Mobile Agent (SOMA), that is based on a thorough security model and provides a wide range of tools and mechanisms to build and enforce flexible security policies. In particular, we focus on the problem of how mobile agents can be protected from malicious behavior of execution sites and we propose a distributed multiple-hops integrity protocol for mobile agent protection, fully integrated in SOMA.

The Mobile Agent technology is suitable for applications in open, distributed and heterogeneous environments such as the Internet and the Web, because it can overcome some limits of traditional approaches. The paper describes a Secure and Open Mobile Agent (SOMA) programming environment with two main design objectives that are security and interoperability. On the one hand SOMA is based on a thorough security model and provides a wide range of tools and mechanisms to build and enforce flexible security policies. On the other hand, the SOMA framework can interoperate with different application components designed with different programming styles. SOMA grants interoperability by closely considering compliance with CORBA, the most diffused standard in the area of Object-Oriented components. SOMA has been adopted as a platform to develop several distributed applications in the area of network and systems management, CSCW, and distributed and heterogeneous information systems.

Monitoring tools are necessary components in the support of distributed applications and can be used to provide dependability, debugging and testing, to enhance the performance and to make possible the run-time steering of applications. These tools are needed to exploit in the best way all the available high performance computing resources of a heterogeneous environment. The paper describes HOLMES, an on-line monitoring system designed to support dynamic management of resources that requires run-time measurement. HOLMES identifies the evolving system state and provides the necessary information to any dynamic policy to assign resources by following application evolution. HOLMES makes possible to control and steer an application even distributed across heterogeneous architectures, from parallel machines to clusters of workstations and PCs.