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Journal articles on the topic 'Autonomic computing models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

ZambranoMéndez, Leandro, Alejandro Rosete Suarez, and Humberto Díaz Pando. "A Review of the Autonomic Computing Models and Applications." International Journal of Computer Applications 94, no. 4 (2014): 14–18. http://dx.doi.org/10.5120/16330-5605.

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2

Huebscher, Markus C., and Julie A. McCann. "A survey of autonomic computing—degrees, models, and applications." ACM Computing Surveys 40, no. 3 (2008): 1–28. http://dx.doi.org/10.1145/1380584.1380585.

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3

Abdulrazak, Bessam, Patrice Roy, Charles Gouin-Vallerand, Yacine Belala, and Sylvain Giroux. "Micro Context-Awareness for Autonomic Pervasive Computing." International Journal of Business Data Communications and Networking 7, no. 2 (2011): 48–68. http://dx.doi.org/10.4018/jbdcn.2011040104.

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Context-aware software provides adapted services to users or other software components. On the other hand, Autonomic Pervasive Computing uses context to reduce the complexity of pervasive system utilization, management and maintenance. This paper describes two context-awareness models, the macro and micro approaches, that define and integrate contextual views of individual pervasive components (micro level) and global knowledge of the system (macro level), and provides a more detailed overview of a micro Context-aware programming model for open smart space problems. These models are presented

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4

Eberbach, Eugene. "State of the Art of Information Technology Computing Models for Autonomic Cloud Computing †." Proceedings 1, no. 3 (2017): 190. http://dx.doi.org/10.3390/10.3390/is4si-2017-04028.

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5

Eberbach, Eugene. "State of the Art of Information Technology Computing Models for Autonomic Cloud Computing." Proceedings 1, no. 3 (2017): 190. http://dx.doi.org/10.3390/is4si-2017-04028.

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6

Sharma, Dolly, Shailendra Singh, and Mamta Mittal. "Trust Models in Grid Computing: A Review." Recent Patents on Engineering 13, no. 2 (2019): 94–100. http://dx.doi.org/10.2174/1872212112666180427143757.

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Background: Grid computing relates to a pool of resources to be shared by users in Grid Environment. Security of the resources from users and vice-versa is a significant issue. This is where the notion of trust comes into existence. A number of researchers have proposed models for evaluation of trust in grid computing, but they fail to spot one or the other parameters for trust evaluation. The essence of trust models in grid computing is that they offer autonomic trust management. An autonomic trust model has been patented by Z. Yan and C. Prehofer in 2009. Another patent was published by Anna

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7

Cetina, Carlos, Pau Giner, Joan Fons, and Vicente Pelechano. "Autonomic Computing through Reuse of Variability Models at Runtime: The Case of Smart Homes." Computer 42, no. 10 (2009): 37–43. http://dx.doi.org/10.1109/mc.2009.309.

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8

de Souza, Euzébio D., and Eduardo José Lima II. "Autonomic Computing in a Biomimetic Algorithm for Robots Dedicated to Rehabilitation of Ankle." International Journal of Grid and High Performance Computing 9, no. 1 (2017): 48–60. http://dx.doi.org/10.4018/ijghpc.2017010105.

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Human mobility is the key element of everyday life, its reduction or loss deeply affects daily activities. In assisted rehabilitation, robotic devices have focuses on the biomechanics of motor control. However, biomechanics does not study the neurological and physiological processes related to normal gait. Biomimetics combined with biomechanics, can generate a more efficient stimulation of the motor cortex and the locomotor system. The highest efficiency obtained through torque generation models, based on the physiological response of muscles and bones to reaction forces, together with control

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9

Gallagher, Shaun, Daniel D. Hutto, Jan Slaby, and Jonathan Cole. "The brain as part of an enactive system." Behavioral and Brain Sciences 36, no. 4 (2013): 421–22. http://dx.doi.org/10.1017/s0140525x12002105.

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AbstractThe notion of an enactive system requires thinking about the brain in a way that is different from the standard computational-representational models. In evolutionary terms, the brain does what it does and is the way that it is, across some scale of variations, because it is part of a living body with hands that can reach and grasp in certain limited ways, eyes structured to focus, an autonomic system, an upright posture, etc. coping with specific kinds of environments, and with other people. Changes to any of the bodily, environmental, or intersubjective conditions elicit responses fr

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10

Çag̃layan, M. Ufuk. "EROL GELENBE: A CAREER IN MULTI-DISCIPLINARY PROBABILITY MODELS." Probability in the Engineering and Informational Sciences 30, no. 3 (2016): 308–25. http://dx.doi.org/10.1017/s0269964816000024.

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We focus on Erol Gelenbe's scientific and technical contributions to probability models in the computer and information sciences, but limit our survey to the last fifteen years. We start with a brief overview of his work as a single author, as well as his work in collaboration with over 200 co-authors. We discuss some of his recent and innovative work regarding a new probability model that represents Intermittent Energy Sources for Computing and Communications, introducingEnergy Packet Networkswhich are a probabilistic representation of the flow, storage and consumption of electrical energy at

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11

Marín-Morales, Javier, Juan Luis Higuera-Trujillo, Jaime Guixeres, Carmen Llinares, Mariano Alcañiz, and Gaetano Valenza. "Heart rate variability analysis for the assessment of immersive emotional arousal using virtual reality: Comparing real and virtual scenarios." PLOS ONE 16, no. 7 (2021): e0254098. http://dx.doi.org/10.1371/journal.pone.0254098.

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Many affective computing studies have developed automatic emotion recognition models, mostly using emotional images, audio and videos. In recent years, virtual reality (VR) has been also used as a method to elicit emotions in laboratory environments. However, there is still a need to analyse the validity of VR in order to extrapolate the results it produces and to assess the similarities and differences in physiological responses provoked by real and virtual environments. We investigated the cardiovascular oscillations of 60 participants during a free exploration of a real museum and its virtu

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12

Kawaji, Shigeyasu, and Tetsuo Sawaragi. "Special Issue on Intelligent Control in Coming New Generation." Journal of Robotics and Mechatronics 12, no. 6 (2000): 603–4. http://dx.doi.org/10.20965/jrm.2000.p0603.

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In the early 1970s, a concept of intelligent control was proposed by Fu, and since then the advancement of control technologies as a migrate of control theory, artificial intelligence and operations research has been actively attempted. The breakthrough of this concept was to integrate a human judgment and a concept of value as well as management theory into conventional control theoretic approaches, and synthesize these as artificial intelligence. A number of unconventional control techniques have evolved, offering solutions to many difficult control problems in industry and manufacturing. Sa

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13

Arockiam, L., S. Monikandan, and G. Parthasarathy. "Cloud Computing: A Survey." International Journal of Computer and Communication Technology, January 2017, 21–28. http://dx.doi.org/10.47893/ijcct.2017.1393.

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Cloud computing provides huge computing services to the business for improving the organizational growth. Basic requirement needed for this technology is Internet but provides higher capability when compared to the Internet. Cloud computing is a combination of computation, software, data access and also provides storage services. In Cloud, storage of data and the location of stored data are not known to the user. Cloud computing adopts the concept of virtualization, service oriented architecture, autonomic, and utility computing. The cloud has more advantages and easy to implement with any bus

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14

G, RAVI KUMAR, C. MUTHUSAMY, and A. VINAYA BABU. "SELF-MANAGING PERFORMANCE IN APPLICATION SERVERS – MODELLING AND DATA ARCHITECTURE." International Journal of Computer and Communication Technology, October 2016, 250–54. http://dx.doi.org/10.47893/ijcct.2016.1379.

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High performance is always a desired objective in computing systems. Managing performance through manual intervention is a well-known and obvious mechanism. The attempts to self-manage performance with minimal human intervention are predominant in the recent advances of research. Control Systems theory is playing a significant role in building such intelligent and autonomic computing systems. We are investigating in building Intelligent Application Servers by enabling control system as a first class feature in component software, at design time and runtime. In this direction, it is important t

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15

Mariage, Jean-Jacques. "LEARNING TO TEACH TO NEURAL NETWORKS HOW TO LEARN WELL WITH SOH, A SELF-OBSERVING HEURISTIC." International Journal of Computing, August 1, 2014, 58–65. http://dx.doi.org/10.47839/ijc.3.1.254.

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In this ongoing research, we present a Self-Observing Heuristic (SOH). SOH is a hybrid computing method. It roots in natural selection and optimization techniques to provide an environmentally driven evolutionary computation scheme, capable of autonomic cumulative learning. Our aim is to realize an adaptive learning system based on neo- Darwinian evolution of neural units. We proceed in two complementary directions. On one hand, we try to automati- cally compute the costly tuning phase of the configuration and learning parameters of neural networks (NNs). On the other hand, we use meiosis cell

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