117 research outputs found

    YUMA – An AI Planning Agent for Composing IT Services from Infrastructure-as-Code Specifications

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    Infrastructure-as-code enables cloud architects to automate IT service delivery by specifying IT services through machine-readable definition files. To allow for a reusability of the infrastructure-as-code specifications, cloud architects specify IT services as compositions of sub-processes. As the AI planning agents for automated IT service composition proposed by prior research fall short in the infrastructure-as-code context, we design a search-based problem-solving agent named YUMA according to a design science research process to fill this research gap. YUMA holds a search tree reflecting the state space and transition model. It includes an algorithm for building the search tree and two algorithms for determining the minimum composition plan. The underlying IT service composition problem is explicated for the infrastructure-as-code context and formulated as a search problem. The results of the demonstration and evaluation show that YUMA fulfills the requirements necessary to solve this problem and digitizes an important task of cloud architects

    Scalable Automatic Service Composition using Genetic Algorithms

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    A composition of simple web services, each dedicated to performing a specific sub- task involved, proves to be a more competitive solution than an equivalent atomic web service for a complex requirement comprised of several sub-tasks. Composite services have been extensively researched and perfected in many aspects for over two decades, owing to benefits such as component re-usability, broader options for composition requesters, and the liberty to specialize for component providers. However, most studies in this field must acknowledge that each web service has a limited context in which it can successfully perform its tasks, the boundaries defined by the internal constraints imposed on the service by its providers. The restricted context-spaces of all such component services define the contextual boundaries of the composite service as a whole when used in a composition, making internal constraints an essential factor in composite service functionality. Due to their limited exposure, no systems have yet been proposed on the large-scale solution repository to cater to the specific verification of internal constraints imposed on components of a composite service. In this thesis, we propose a scalable automatic service composition capable of not only automatically constructing context-aware composite web services with internal constraints positioned for optimal resource utilization but also validating the generated compositions on a large-scale solution repository using the General Intensional Programming System (GIPSY) as a time- and cost-efficient simulation/execution environment

    Automated design of population-based algorithms: a case study in vehicle routing

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    Metaheuristics have been extensively studied to solve constraint combinatorial optimisation problems such as vehicle routing problems. Most existing algorithms require considerable human effort and different kinds of expertise in algorithm design. These manually designed algorithms are discarded after solving the specific instances. It is highly desirable to automate the design of search algorithms, thus to solve problem instances effectively with less human intervention. This thesis develops a novel general search framework to formulate in a unified way a range of population-based algorithms. Within this framework, generic algorithmic components such as selection heuristics on the population and evolution operators are defined, and can be composed using machine learning to generate effective search algorithms automatically. This unified framework aims to serve as the basis to analyse algorithmic components, generating effective search algorithms for complex combinatorial optimisation problems. Three key research issues within the general search framework are identified: automated design of evolution operators, of selection heuristics, and of both. To accurately describe the search space of algorithm design as a new task for machine learning, this thesis identifies new key features, namely search-dependent and instance-dependent features. These features are identified to assist effective algorithm design. With these features, a set of state-of-the-art reinforcement learning techniques, such as deep Q-network based and proximal policy optimisation based models and maximum entropy mechanisms have been developed to intelligently select and combine appropriate evolution operators and selection heuristics during different stages of the optimisation process. The effectiveness and generality of these algorithms automatically designed within the proposed general search framework are validated comprehensively across different capacitated vehicle routing problem with time windows benchmark instances. This thesis contributes to making a key step towards automated algorithm design with a general framework supporting fundamental analysis by effective machine learning

    Cloud Service Selection System Approach based on QoS Model: A Systematic Review

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    The Internet of Things (IoT) has received a lot of interest from researchers recently. IoT is seen as a component of the Internet of Things, which will include billions of intelligent, talkative "things" in the coming decades. IoT is a diverse, multi-layer, wide-area network composed of a number of network links. The detection of services and on-demand supply are difficult in such networks, which are comprised of a variety of resource-limited devices. The growth of service computing-related fields will be aided by the development of new IoT services. Therefore, Cloud service composition provides significant services by integrating the single services. Because of the fast spread of cloud services and their different Quality of Service (QoS), identifying necessary tasks and putting together a service model that includes specific performance assurances has become a major technological problem that has caused widespread concern. Various strategies are used in the composition of services i.e., Clustering, Fuzzy, Deep Learning, Particle Swarm Optimization, Cuckoo Search Algorithm and so on. Researchers have made significant efforts in this field, and computational intelligence approaches are thought to be useful in tackling such challenges. Even though, no systematic research on this topic has been done with specific attention to computational intelligence. Therefore, this publication provides a thorough overview of QoS-aware web service composition, with QoS models and approaches to finding future aspects

    Cybersecurity of Digital Service Chains

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    This open access book presents the main scientific results from the H2020 GUARD project. The GUARD project aims at filling the current technological gap between software management paradigms and cybersecurity models, the latter still lacking orchestration and agility to effectively address the dynamicity of the former. This book provides a comprehensive review of the main concepts, architectures, algorithms, and non-technical aspects developed during three years of investigation; the description of the Smart Mobility use case developed at the end of the project gives a practical example of how the GUARD platform and related technologies can be deployed in practical scenarios. We expect the book to be interesting for the broad group of researchers, engineers, and professionals daily experiencing the inadequacy of outdated cybersecurity models for modern computing environments and cyber-physical systems

    Participative Urban Health and Healthy Aging in the Age of AI

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    This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

    Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

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    This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

    Actas del XXIV Workshop de Investigadores en Ciencias de la Computación: WICC 2022

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    Compilación de las ponencias presentadas en el XXIV Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Mendoza en abril de 2022.Red de Universidades con Carreras en Informátic

    Recent Advances in Social Data and Artificial Intelligence 2019

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    The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

    12th EASN International Conference on "Innovation in Aviation & Space for opening New Horizons"

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    Epoxy resins show a combination of thermal stability, good mechanical performance, and durability, which make these materials suitable for many applications in the Aerospace industry. Different types of curing agents can be utilized for curing epoxy systems. The use of aliphatic amines as curing agent is preferable over the toxic aromatic ones, though their incorporation increases the flammability of the resin. Recently, we have developed different hybrid strategies, where the sol-gel technique has been exploited in combination with two DOPO-based flame retardants and other synergists or the use of humic acid and ammonium polyphosphate to achieve non-dripping V-0 classification in UL 94 vertical flame spread tests, with low phosphorous loadings (e.g., 1-2 wt%). These strategies improved the flame retardancy of the epoxy matrix, without any detrimental impact on the mechanical and thermal properties of the composites. Finally, the formation of a hybrid silica-epoxy network accounted for the establishment of tailored interphases, due to a better dispersion of more polar additives in the hydrophobic resin
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