Hausi A. Muller, O'Brien, William, Klein, Mark H. et al. · KiltHub Repository · 2018
This report examines selected aspects of autonomic computing and explores some of the strengths and weaknesses of that technology. It also makes connections between autonomic computing and current work in several initiatives at the Software Engineering Institute. Furthermore, it describes the potential and impact of autonomic computing for Department of Defense (DoD) systems and outlines some of the challenges for the DoD as it moves to exploit autonomic computing technology.
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Edin Arnautovic, Hermann Kaindl, Jürgen Falb · 2009
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Ahmed Banafa · River Publishers eBooks · 2023
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Mueller, Hausi A., O'Brien, Liam, Klein, Mark et al. · Defense Technical Information Center (DTIC) · 2006
This report examines selected aspects of autonomic computing, explores some of its strengths and weaknesses, and outlines some of the current research projects being undertaken in this area. It also makes connections between this area and current work in several initiatives at the Carnegie-Mellon (trademark) Software Engineering Institute (SEI), namely the Predictable Assembly from Certifiable Components and Software Architecture Technology (SAT) Initiatives. Several pieces of work being undertaken in these initiatives have connections to autonomic computing. Furthermore, the report describes the potential and impact of autonomic computing for Department of Defense (DoD) systems, and outlines some of the challenges for the DoD as it moves to exploit autonomic computing technology. The following autonomic computing projects are profiled: Unity Project and Autonomic Computing Toolkit; Information Economies Project; KX Project (Columbia University); Rainbow Project (Carnegie-Mellon University); ROC Project (University of California Berkeley/Stanford); DEAS Project (Universities of Victoria and Toronto, Canada); Autonomia Project (University of Arizona); AutoMate Project (Rutgers University); AMUSE Project (University of Glasgow and Imperial College, UK); Astrolabe Project (Cornell University). At the end of the report is an extensive bibliography, which shows how quickly the autonomic computing field has grown since 2001. The bibliography contains a number of documents that would be good starting points for newcomers to the field of autonomic computing.
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Ruhlig, K. · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · 2008
Als Vorbild für das Autonomic Computing dient das autonome Nervensystem, das unbewusste Körperfunktionen kontrolliert, wie z.B. die Atmung oder den Herzschlag. Entsprechend sollen mit Hilfe dieser Technologie ebenfalls bestimmte Funktionen eines Computersystems selbsttätig ohne äußeren Eingriff ausgeführt werden. Computer sollen dadurch die Fähigkeit erhalten, sich selbst zu konfigurieren, zu optimieren, zu "heilen", und zu schützen.
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Roy Sterritt, David F. Bantz · 2014
The overall goal of this research is to improve the ‘environment awareness ’ aspect of personal autonomic computing. Personal Computing offers unique challenges for self-management due to its multi-equipment, multi-situation, and multi-user nature. The aim is to develop a support architecture for multi-platform working, based on autonomic computing concepts and techniques. Of particular interest is collaboration among personal systems to take a shared responsibility for environment awareness. Concepts mirroring human mechanisms, such as 'reflex reactions' and the use of 'vital signs ' to assess operational health, are used in designing and implementing the personal computing architecture. A proof of concept self-healing tool is considered and lessons learned used for the requirements specification of the community-based environment awareness prototype environment—PAC-
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Amar Ramdane-Chérif · IGI Global eBooks · 2011
Cognitive approach through the neural network (NN) paradigm is a critical discipline that will help bring about autonomic computing (AC). NN-related research, some involving new ways to apply control theory and control laws, can provide insight into how to run complex systems that optimize to their environments. NN is one kind of AC systems that can embody human cognitive powers and can adapt, learn, and take over certain functions previously performed by humans. In recent years, artificial neural networks have received a great deal of attention for their ability to perform nonlinear mappings. In trajectory control of robotic devices, neural networks provide a fast method of autonomously learning the relation between a set of output states and a set of input states. In this chapter, we apply the cognitive approach to solve position controller problems using an inverse geometrical model. In order to control a robot manipulator in the accomplishment of a task, trajectory planning is required in advance or in real time. The desired trajectory is usually described in Cartesian coordinates and needs to be converted to joint space for the purpose of analyzing and controlling the system behavior. In this chapter, we use a memory neural network (MNN) to solve the optimization problem concerning the inverse of the direct geometrical model of the redundant manipulator when subject to constraints. Our approach offers substantially better accuracy, avoids the computation of the inverse or pseudoinverse Jacobian matrix, and does not produce problems such as singularity, redundancy, and considerably increased computational complexity. Request access from your librarian to read this chapter's full text.
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Manish Parashar, Salim Hariri · 2004
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Amar Ramdane-Chérif · International Journal of Cognitive Informatics and Natural Intelligence · 2007
Cognitive approach through the neural network “NN” paradigm is a critical discipline that will help bring about autonomic computing “AC.” NN-related research, some involving new ways to apply control theory and control laws, can provide insight into how to run complex systems that optimize to their environments. NN is one kind of AC system that can embody human cog-nitive powers and that can adapt, learn, and take over certain functions previously performed by humans. In recent years, artificial neural networks have received a great deal of attention for their ability to perform nonlinear mappings. In trajectory control of robotic devices, neural networks provide a fast method of autonomously learning the relation between a set of output states and a set of input states. In this article, we apply the cognitive approach to solve position controller problems using an inverse geometrical model. In order to control a robot manipulator in the accomplishment of a task, trajectory planning is required in advance or in real time. The desired trajectory is usually described in Cartesian coordinates and needs to be converted to joint space for the purpose of analyzing and controlling the system behavior. In this article, we use a memory neural network (MNN) to solve the optimization problem concerning the inverse of the direct geometrical model of the redundant manipulator when subject to constraints. Our approach offers substantially better accuracy, avoids the computation of the inverse or pseudo-inverse Jacobian matrix, and does not produce problems such as singularity, redundancy, and considerably increased computational complexity.
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Philippe Lalanda, Julie A. McCann, Ada Diaconescu · Undergraduate topics in computer science · 2013
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Thaddeus Eze · 2022
The concept of autonomic computing seeks to reduce the complexity of pervasively ubiquitous system management and maintenance by shifting the responsibility for low-level tasks from humans to the system while allowing humans to concentrate on high-level tasks. This is achieved by building self-managing systems that are generally capable of self-configuring, self-healing, self-optimising, and self-protecting. Trustworthy autonomic computing technologies are being applied in datacentre and cloud management, smart cities and autonomous systems including driverless cars. However, there are still significant challenges to achieving trustworthiness. This book covers challenges and solutions in autonomic computing trustworthiness from methods and techniques to achieve consistent and reliable system self-management. Researchers, developers and users need to be confident that an autonomic self-managing system will remain correct in the face of any possible contexts and environmental inputs. The book is aimed at researchers in autonomic computing, autonomics and trustworthy autonomics. This will be a go-to book for foundational knowledge, proof of concepts and novel trustworthy autonomic techniques and approaches. It will be useful to lecturers and students of autonomic computing, autonomics and multi-agent systems who need an easy-to-use text with sample codes, exercises, use-case demonstrations. This is also an ideal tutorial guide for independent study with simple and well documented diagrams to explain techniques and processes.
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Amar Ramdane-Chérif · IGI Global eBooks · 2011
Cognitive approach through the neural network (NN) paradigm is a critical discipline that will help bring about autonomic computing (AC). NN-related research, some involving new ways to apply control theory and control laws, can provide insight into how to run complex systems that optimize to their environments. NN is one kind of AC systems that can embody human cognitive powers and can adapt, learn, and take over certain functions previously performed by humans. In recent years, artificial neural networks have received a great deal of attention for their ability to perform nonlinear mappings. In trajectory control of robotic devices, neural networks provide a fast method of autonomously learning the relation between a set of output states and a set of input states. In this chapter, we apply the cognitive approach to solve position controller problems using an inverse geometrical model. In order to control a robot manipulator in the accomplishment of a task, trajectory planning is required in advance or in real time. The desired trajectory is usually described in Cartesian coordinates and needs to be converted to joint space for the purpose of analyzing and controlling the system behavior. In this chapter, we use a memory neural network (MNN) to solve the optimization problem concerning the inverse of the direct geometrical model of the redundant manipulator when subject to constraints. Our approach offers substantially better accuracy, avoids the computation of the inverse or pseudoinverse Jacobian matrix, and does not produce problems such as singularity, redundancy, and considerably increased computational complexity.
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Thaddeus Eze · 2022
Trustworthy autonomic computing (TAC) looks beyond the basics of successfully achieving an autonomic system to establishing basis for trusting that the autonomic system does 'what it says it will do'. This chapter introduces the trustworthy autonomic architecture (TrAArch) and covers the following: The importance of TAC; The differences between TAC and Trusted Computing; How a TrAArch can be designed; Overview of the TrAArch framework
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Manish Parashar · 2006
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Furrer, Frank J., Püschel, Georg · Qucosa (Saxon State and University Library Dresden) · 2018
In algorithmic computing, the program follows a predefined set of rules – the algorithm. The analyst/designer of the program analyzes the intended tasks of the program, defines the rules for its expected behaviour and programs the implementation. The creators of algorithmic software must therefore foresee, identify and implement all possible cases for its behaviour in the future application! However, what if the problem is not fully defined? Or the environment is uncertain? What if situations are too complex to be predicted? Or the environment is changing dynamically? In many such cases algorithmic computing fails. In such situations, the software needs an additional degree of freedom: Autonomy! Autonomy allows software to adapt to partially defined problems, to uncertain or dynamically changing environments and to situations that are too complex to be predicted. As more and more applications – such as autonomous cars and planes, adaptive power grid management, survivable networks, and many more – fall into this category, a gradual switch from algorithmic computing to autonomic computing takes place. Autonomic computing has become an important software engineering discipline with a rich literature, an active research community, and a growing number of applications.:Introduction 5 1 A Process Data Based Autonomic Optimization of Energy Efficiency in Manufacturing Processes, Daniel Höschele 9 2 Eine autonome Optimierung der Stabilität von Produktionsprozessen auf Basis von Prozessdaten, Richard Horn 25 3 Assuring Safety in Autonomous Systems, Christian Rose 41 4 MAPE-K in der Praxis - Grundlage für eine mögliche automatische Ressourcenzuweisung, in der Cloud Michael Schneider 59
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Jianhua, He · Cronfa (Swansea University) · 2009
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Tom De Wolf, Tom Holvoet, Tom De Wolf et al. · 2006
When engineering different decentralised autonomic computing solutions for a certain application there is a lack of a common and usable evaluation and comparison approach. Useful metrics and measurements are mostly domain-specific and are hard to generalise. This paper proposes to use radar charts which allow to apply these domain-specific metrics in an overall evaluation and comparison approach. As such, multiple solutions can be compared with each other and evaluated along multiple dimensions that are important for evaluating decentralised autonomic systems. The approach is illustrated in mobile ad-hoc network management.
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Sterritt, Roy · Ulster University Research Portal (Ulster University) · 2025
This paper summarizes a session of the track “AI and Autonomic Computing”. The research work deals with the general question ‘can Autonomic Computing benefit from or be a significant part of the latest A.I. incarnation – Generative AI, as it moves towards Artificial General Intelligence (AGI)?’
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Rajiv Ramnath · 2008
No abstract available.
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Ahmed Banafa · River Publishers eBooks · 2024
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