Relevant bibliographies by topics / Autonomic computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Autonomic computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 22 June 2024

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Journal articles on the topic "Autonomic computing"

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Salehie, Mazeiar, and Ladan Tahvildari. "Autonomic computing." ACM SIGSOFT Software Engineering Notes 30, no. 4 (July 2005): 1–7. http://dx.doi.org/10.1145/1082983.1083082.

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Sterritt, Roy. "Autonomic computing." Innovations in Systems and Software Engineering 1, no. 1 (March 11, 2005): 79–88. http://dx.doi.org/10.1007/s11334-005-0001-5.

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CH, Bilal Hussain. "Cloud Intrusion and Autonomic Management in Autonomic Cloud Computing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 28–32. http://dx.doi.org/10.31142/ijtsrd18378.

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Ramdane-Cherif, Amar. "Toward Autonomic Computing." International Journal of Cognitive Informatics and Natural Intelligence 1, no. 2 (April 2007): 16–33. http://dx.doi.org/10.4018/jcini.2007040102.

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Bantz, D. F., C. Bisdikian, D. Challener, J. P. Karidis, S. Mastrianni, A. Mohindra, D. G. Shea, and M. Vanover. "Autonomic personal computing." IBM Systems Journal 42, no. 1 (2003): 165–76. http://dx.doi.org/10.1147/sj.421.0165.

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Cong Vinh, Phan. "Algebraically Autonomic Computing." Mobile Networks and Applications 21, no. 1 (May 26, 2015): 3–9. http://dx.doi.org/10.1007/s11036-015-0615-2.

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Chess, David M. "Security in autonomic computing." ACM SIGARCH Computer Architecture News 33, no. 1 (March 2005): 2–5. http://dx.doi.org/10.1145/1055626.1055628.

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TIANFIELD, H., and R. UNLAND. "Towards autonomic computing systems." Engineering Applications of Artificial Intelligence 17, no. 7 (October 2004): 689–99. http://dx.doi.org/10.1016/s0952-1976(04)00113-7.

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Hariri, Salim, Bithika Khargharia, Houping Chen, Jingmei Yang, Yeliang Zhang, Manish Parashar, and Hua Liu. "The Autonomic Computing Paradigm." Cluster Computing 9, no. 1 (January 2006): 5–17. http://dx.doi.org/10.1007/s10586-006-4893-0.

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Bahl Ritika Wason, Divya. "Autonomic Computing: Further Maturation in IT Industry." International Journal of Science and Research (IJSR) 1, no. 3 (March 5, 2012): 50–58. http://dx.doi.org/10.21275/ijsr12120349.

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Dissertations / Theses on the topic "Autonomic computing"

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Furrer, Frank J., and Georg Püschel. "From Algorithmic Computing to Autonomic Computing." Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A30773.

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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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Azzam, Adel R. "Survey of Autonomic Computing and Experiments on JMX-based Autonomic Features." ScholarWorks@UNO, 2016. http://scholarworks.uno.edu/td/2123.

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Autonomic Computing (AC) aims at solving the problem of managing the rapidly-growing complexity of Information Technology systems, by creating self-managing systems. In this thesis, we have surveyed the progress of the AC field, and studied the requirements, models and architectures of AC. The commonly recognized AC requirements are four properties - self-configuring, self-healing, self-optimizing, and self-protecting. The recommended software architecture is the MAPE-K model containing four modules, namely - monitor, analyze, plan and execute, as well as the knowledge repository. In the modern software marketplace, Java Management Extensions (JMX) has facilitated one function of the AC requirements - monitoring. Using JMX, we implemented a package that attempts to assist programming for AC features including socket management, logging, and recovery of distributed computation. In the experiments, we have not only realized the powerful Java capabilities that are unknown to many educators, we also illustrated the feasibility of learning AC in senior computer science courses.
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Scogland, Thomas R. "Runtime Adaptation for Autonomic Heterogeneous Computing." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71315.

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity.
Ph. D.
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Dudzik, Stefan Einhorn Jochen Schönleber Tim. "Untersuchung des IBM Autonomic Computing Toolkits." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11730082.

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Tunc, Cihan. "Autonomic Cloud Resource Management." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/347144.

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The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management. In this dissertation, we developed an autonomic management framework based on a novel data structure, AppFlow, used for modeling current and near-term future cloud application behavior. We have developed the following capabilities for the performance and power management of the cloud computing systems: 1) online modeling and characterizing the cloud application behavior and resource requirements; 2) predicting the application behavior to proactively optimize its operations at runtime; 3) a holistic optimization methodology for performance and power using number of cores, CPU frequency, and memory amount; and 4) an autonomic cloud management to support the dynamic change in VM configurations at runtime to simultaneously optimize multiple objectives including performance, power, availability, etc. We validated our approach using RUBiS benchmark (emulating eBay), on an IBM HS22 blade server. Our experimental results showed that our approach can lead to a significant reduction in power consumption upto 87% when compared to the static resource allocation strategy, 72% when compared to adaptive frequency scaling strategy, and 66% when compared to a multi-resource management strategy.
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Jacyno, Mariusz. "Self-organising agent communities for autonomic computing." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/143903/.

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Efficient resource management is one of key problems associated with large-scale distributed computational systems. Taking into account their increasing complexity, inherent distribution and dynamism, such systems are required to adjust and adapt resources market that is offered by them at run-time and with minimal cost. However, as observed by major IT vendors such as IBM, SUN or HP, the very nature of such systems prevents any reliable and efficient control over their functioning through human administration. For this reason, autonomic system architectures capable of regulating their own functioning are suggested as the alternative solution to looming software complexity crisis. Here, large-scale infrastructures are assumed to comprise myriads of autonomic elements, each acting, learning or evolving separately in response to interactions in their local environments. The self-regulation of the whole system, in turn, becomes a product of local adaptations and interactions between system elements. Although many researchers suggest the application of multi-agent systems that are suitable for realising this vision, not much is known about regulatory mechanisms that are capable to achieve efficient organisation within a system comprising a population of locally and autonomously interacting agents. To address this problem, the aim of the work presented in this thesis was to understand how global system control can emerge out of such local interactions of individual system elements and to develop decentralised decision control mechanisms that are capable to employ this bottom-up self-organisation in order to preserve efficient resource management in dynamic and unpredictable system functioning conditions. To do so, we have identified the study of complex natural systems and their self-organising properties as an area of research that may deliver novel control solutions within the context of autonomic computing. In such a setting, a central challenge for the construction of distributed computational systems was to develop an engineering methodology that can exploit self-organising principles observed in natural systems. This, in particular, required to identify conditions and local mechanisms that give rise to useful self-organisation of interacting elements into structures that support required system functionality. To achieve this, we proposed an autonomic system model exploiting self-organising algorithms and its thermodynamic interpretation, providing a general understanding of self-organising processes that need to be taken into account within artificial systems exploiting self-organisation.
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Jararweh, Yaser. "Autonomic Programming Paradigm for High Performance Computing." Diss., The University of Arizona, 2010. http://hdl.handle.net/10150/193527.

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The advances in computing and communication technologies and software tools have resulted in an explosive growth in networked applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. In a similar way, the underlying information infrastructure, e.g. the Internet, is large, complex, heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources. The combination of the two results in application development and management complexities that break current computing paradigms, which are based on static behaviors. As a result, applications, programming environments and information infrastructures are rapidly becoming fragile, unmanageable and insecure. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems. Autonomic programming paradigm is inspired by the human autonomic nervous system that handles complexity, uncertainties and abnormality. The overarching goal of the autonomic programming paradigm is to help building systems and applications capable of self-management. Firstly, we investigated the large-scale scientific computing applications which generally experience different execution phases at run time and each phase has different computational, communication and storage requirements as well as different physical characteristics. In this dissertation, we present Physics Aware Optimization (PAO) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Optimization Manager to exploit the PAO paradigm. On the other hand we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring server and networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means it can be self-managed component) by using our autonomic programming paradigm. Our POA technique for medical application yielded about 3X improvement of performance with 98.3% simulation accuracy compared to traditional techniques for performance optimization. Also, our Self-configuration management for power and performance management in GPU cluster demonstrated 53.7% power savings for CUDAworkload while maintaining the cluster performance within given acceptable thresholds.
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Omar, Wail M. "Self-management middleware services for autonomic grid computing." Thesis, Liverpool John Moores University, 2006. http://researchonline.ljmu.ac.uk/5784/.

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Rodrigues, Gabriel Siqueira. "Autonomic goal-driven deployment in heterogeneous computing environments." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/23185.

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Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.
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Vemos um crescente interesse em aplicações que devem contar com ambientes de computação heterogêneos, como a Internet das Coisas (IoT). Esses aplicativos são destinados a executar em uma ampla gama de dispositivos com diferentes recursos computacionais disponíveis. Para lidar com algum tipo de heterogeneidade, como dois tipos possíveis de processadores gráficos em um computador pessoal, podemos usar abordagens simples como um script que escolhe a biblioteca de software certa a ser copiada para uma pasta. Essas abordagens simples são centralizadas e criadas em tempo de design. Eles requerem um especialista ou equipe para controlar todo o espaço de variabilidade. Dessa forma, essas abordagens não são escaláveis para ambientes altamente heterogêneos. Em ambientes altamente heterogêneos, é difícil prever o ambiente computacional em tempo de projeto, implicando provavelmente indecidibilidade na configuração correta para cada ambiente. Em nosso trabalho, propomos GoalD: um método que permite a implantação autônoma de sistemas, refletindo sobre os objetivos do sistema e seu ambiente computacional. Por implantação autônoma, queremos dizer que o sistema é capaz de encontrar o conjunto correto de componentes para o ambiente computacional alvo, sem intervenção humana. Nós avaliamos nossa abordagem em um estudo de caso: conselheiro de estação de abastecimento, onde uma aplicação aconselha um motorista onde reabastecer / recarregar seu veículo. Nós projetamos a aplicação com variabilidade em nível de requisitos, arquitetura e implantação, o que pode permitir que a aplicação projetada seja executada em diferentes dispositivos. Para cenários com diferentes ambientes, foi possível planejar a implantação de forma autônoma. Além disso, a escalabilidade do algoritmo que planeja a implantação foi avaliada em um ambiente simulado. Os resultados mostram que usando a abordagem é possível planejar de forma autônoma a implantação de um sistema com milhares de componentes em poucos segundos.
We see a growing interest in computing applications that should rely on heterogeneous computing environments, like Internet of Things (IoT). Such applications are intended to execute in a broad range of devices with different available computing resources. In order to handle some kind of heterogeneity, such as two possible types of graphical processors in a desktop computer, we can use simple approaches as a script at deployment-time that chooses the right software library to be copied to a folder. These simple approaches are centralized and created at design-time. They require one specialist or team to control the entire space of variability. However, such approaches are not scalable to highly heterogeneous environments. In highly dynamic and heterogeneous environment it is hard to predict the computing environment at design-time, implying likely undecidability on the correct configuration for each environment at design-time. In our work, we propose GoalD: a method that allows autonomous deployment of systems by reflecting about the goals of the system and its computing environment. By autonomous deployment, we mean that the system can find the correct set of components, for the target computing environment, without human intervention. We evaluate our approach on the filling station advisor case study where an application advises a driver where to refuel/recharge its vehicle. We design the application with variability at requirements, architecture, and deployment, which can allow the designed application be executed in different devices. For scenarios with different environments, it was possible to plan the deployment autonomously. Additionally, the scalability of the algorithm that plan the deployment was evaluated in a simulated environment. Results show that using the approach it is possible to autonomously plan the deployment of a system with thousands of components in few seconds.
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Furrer, Frank J., and Georg Püschel. "Autonomic Computing: State of the Art - Promises - Impact." Technische Universität Dresden, 2016. https://tud.qucosa.de/id/qucosa%3A29925.

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Software has never been as important as today – and its impact on life, work and society is growing at an impressive rate. We are in the flow of a software-induced transformation of nearly all aspects of our way of life and work. The dependence on software has become almost total. Malfunctions and unavailability may threaten vital areas of our society, life and work at any time. The two massive challenges of software are one hand the complexity of the software and on the other hand the disruptive environment. Complexity of the software is a result of the size, the continuously growing functionality, the more complicated technology and the growing networking. The unfortunate consequence is that complexity leads to many problems in design, development, evolution and operation of software-systems, especially of large software-systems. All software-systems live in an environment. Many of today’s environments can be disruptive and cause severe problems for the systems and their users. Examples of disruptions are attacks, failures of partner systems or networks, faults in communications or malicious activities. Traditionally, both growing complexity and disruptions from the environment have been tackled by better and better software engineering. The development and operating processes are constantly being improved and more powerful engineering tools are introduced. For defending against disruptions, predictive methods – such as risk analysis or fault trees – are used. All this techniques are based on the ingenuity, experience and skills of the engineers! However, the growing complexity and the increasing intensity of possible disruptions from the environment make it more and more questionable, if people are really able to successfully cope with this raising challenge in the future. Already, serious research suggests that this is not the case anymore and that we need assistance from the software-systems themselves! Here enters “autonomic computing” – A promising branch of software science which enables software-systems with self-configuring, self-healing, self-optimization and self-protection capabilities. Autonomic computing systems are able to re-organize, optimize, defend and adapt themselves with no real-time human intervention. Autonomic computing relies on many branches of science – especially computer science, artificial intelligence, control theory, machine learning, multi-agent systems and more. Autonomic computing is an active research field which currently transfers many of its results into software engineering and many applications. This Hauptseminar offered the opportunity to learn about the fascinating technology “autonomic computing” and to do some personal research guided by a professor and assisted by the seminar peers.:Introduction 5 1 What Knowledge Does a Taxi Need? – Overview of Rule Based, Model Based and Reinforcement Learning Systems for Autonomic Computing (Anja Reusch) 11 2 Chancen und Risiken von Virtual Assistent Systemen (Felix Hanspach) 23 3 Evolution einer Microservice Architektur zu Autonomic Computing (Ilja Bauer) 37 4 Mögliche Einflüsse von autonomen Informationsdiensten auf ihre Nutzer (Jan Engelmohr) 49 5 The Benefits of Resolving the Trust Issues between Autonomic Computing Systems and their Users (Marc Kandler) 61
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Books on the topic "Autonomic computing"

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Lalanda, Philippe, Julie A. McCann, and Ada Diaconescu. Autonomic Computing. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5007-7.

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Autonomic computing. Upper Saddle River, N.J: Prentice Hall PTR, 2004.

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Zhang, Yan. Autonomic Computing and Networking. Boston, MA: Springer Science+Business Media, LLC, 2009.

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Xiao, Bin, Laurence T. Yang, Jianhua Ma, Christian Muller-Schloer, and Yu Hua, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73547-2.

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Xie, Bing, Juergen Branke, S. Masoud Sadjadi, Daqing Zhang, and Xingshe Zhou, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16576-4.

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Rong, Chunming, Martin Gilje Jaatun, Frode Eika Sandnes, Laurence T. Yang, and Jianhua Ma, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69295-9.

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González Nieto, Juan, Wolfgang Reif, Guojun Wang, and Jadwiga Indulska, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02704-8.

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Zhang, Yan, Laurence Tianruo Yang, and Mieso K. Denko, eds. Autonomic Computing and Networking. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-89828-5.

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Calero, Jose M. Alcaraz, Laurence T. Yang, Félix Gómez Mármol, Luis Javier García Villalba, Andy Xiaolin Li, and Yan Wang, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23496-5.

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Yang, Laurence T., Hai Jin, Jianhua Ma, and Theo Ungerer, eds. Autonomic and Trusted Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839569.

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Book chapters on the topic "Autonomic computing"

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Gilat, Dagan. "Autonomic Computing." In Disappearing Architecture, 32–40. Basel: Birkhäuser Basel, 2005. http://dx.doi.org/10.1007/3-7643-7674-0_4.

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Banafa, Ahmed. "Autonomic Computing." In Quantum Computing and Other Transformative Technologies, 53–55. New York: River Publishers, 2023. http://dx.doi.org/10.1201/9781003339175-14.

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Müller-Schloer, Christian, and Sven Tomforde. "Quantitative Organic Computing." In Autonomic Systems, 107–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68477-2_4.

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Müller-Schloer, Christian, and Sven Tomforde. "Building Organic Computing Systems." In Autonomic Systems, 171–258. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68477-2_5.

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Lalanda, Philippe, Julie A. McCann, and Ada Diaconescu. "Autonomic Computing Architectures." In Undergraduate Topics in Computer Science, 95–128. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5007-7_4.

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Ahmed, Shameem, Sheikh I. Ahamed, Moushumi Sharmin, and Chowdhury S. Hasan. "Self-healing for Autonomic Pervasive Computing." In Autonomic Communication, 285–307. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09753-4_11.

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Harroud, Hamid, and Ahmed Karmouch. "Implicit Context-Sensitive Mobile Computing Using Semantic Policies." In Autonomic Networking, 188–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11880905_16.

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Hina, Manolo Dulva, Chakib Tadj, Amar Ramdane-Cherif, and Nicole Lévy. "Autonomic Communication in Pervasive Multimodal Multimedia Computing System." In Autonomic Communication, 251–83. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-09753-4_10.

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Calinescu, Radu. "General-Purpose Autonomic Computing." In Autonomic Computing and Networking, 3–30. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-89828-5_1.

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Maier, Andreas. "Keynote Autonomic Computing Initiative." In Lecture Notes in Computer Science, 3. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24714-2_1.

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Conference papers on the topic "Autonomic computing"

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Salehie, Mazeiar, and Ladan Tahvildari. "Autonomic computing." In the 2005 workshop. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1083063.1083082.

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Kephart, Jeffrey O. "Autonomic computing." In the 8th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1998582.1998584.

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Cybenko, George, Vincent Berk, Ian Gregorio-de.souza, and Chad Behre. "Practical Autonomic Computing." In 30th Annual International Computer Software and Applications Conference (COMPSAC'06). IEEE, 2006. http://dx.doi.org/10.1109/compsac.2006.67.

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Gouin-Vallerand, Charles, Bessam Abdulrazak, Sylvain Giroux, and Mounir Mokhtari. "Toward autonomic pervasive computing." In the 10th International Conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1497308.1497440.

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RANGASWAMY, SHANTA. "AUTONOMIC COMPUTING- SCORE /EROCS." In Proceedings of the International Conference on ICSTE 2009. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814289986_0046.

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Mulcahy, James J., Shihong Huang, and Imad Mahgoub. "Autonomic computing and VANET." In SoutheastCon 2015. IEEE, 2015. http://dx.doi.org/10.1109/secon.2015.7132919.

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"Session details: Autonomic computing." In SAC07: The 2007 ACM Symposium on Applied Computing, edited by Umesh Bellur and Sheikh Iqbal Ahamed. New York, NY, USA: ACM, 2007. http://dx.doi.org/10.1145/3246490.

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Ramnath, Rajiv. "Session details: Autonomic computing." In SAC '08: The 2008 ACM Symposium on Applied Computing, edited by Umesh Bellur. New York, NY, USA: ACM, 2008. http://dx.doi.org/10.1145/3260555.

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Raymer, David, Sven van der Meer, and John Strassner. "From Autonomic Computing to Autonomic Networking: An Architectural Perspective." In 2008 5th IEEE Workshop on Engineering of Autonomic and Autonomous Systems (EASe 2008). IEEE, 2008. http://dx.doi.org/10.1109/ease.2008.26.

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Zhang, Donglei, Yu Pan, and Zhongzhi Shi. "Emotion modeling in autonomic computing." In 2009 8th IEEE International Conference on Cognitive Informatics (ICCI). IEEE, 2009. http://dx.doi.org/10.1109/coginf.2009.5250801.

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Reports on the topic "Autonomic computing"

1

Klein, Brandon. Abstracted, Modular, Ephemeral Autonomic Computing Systems Codified. Office of Scientific and Technical Information (OSTI), March 2021. http://dx.doi.org/10.2172/1772682.

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Karsai, Gabor, and Benoit Dawant. Autonomous Negotiating Teams and Model-Integrated Computing for Autonomic Logistics. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada430889.

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Lee, Hsien-Hsin S. TOWARD HIGHLY SECURE AND AUTONOMIC COMPUTING SYSTEMS: A HIERARCHICAL APPROACH. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/1068692.

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Maiden, Wendy M. DualTrust: A Trust Management Model for Swarm-Based Autonomic Computing Systems. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/1021296.

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