Autonomic Computing Research Papers

Autonomic Computing Research Papers-56
The paper selection will be carried out during the peer review process as well as at the conference presentation stage.Submitted papers must not be under consideration by any other journal or publication.Please refer to the Paper Submission GUIDE before submitting your paper. Montaquila, Luigi Pavone 7) Optimizing the Probabilistic Neural Network Training Algorithm for Multi-Class Identification Abdelhadi Lotfi, Abdelkader Benyettou 8) Road Vehicle Recognition Using Magnetic Sensing Feature Extraction and Classification Xiao Chen, Xiaoying Kong, Min Xu 9) Affective Robots: Evaluation of Automatic Emotion Recognition Approaches on a Humanoid Robot towards Emotionally Intelligent Machines Silvia Santano Guillén, Luigi Lo Iacono, Christian Meder 10) Early Diagnosis of Alzheimer's Disease Using a Combination of Images Processing and Brain Signals E.

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A number of selected high-impact full text papers will also be considered for the special journal issues.

All submitted papers will have the opportunity to be considered for this Special Journal Issue.

In autonomic systems, resources and applications are managed to maximize performance and minimize cost, while maintaining predictable and reliable behavior in the face of varying workloads, failures, and malicious threats.

Achieving self-management requires and motivates research that spans a wide variety of scientific and engineering disciplines, including distributed systems, artificial intelligence, machine learning, control theory, optimization, planning, decision theory, user interface design, data management, software engineering, emergent behavior analysis, bio-inspired computing etc.

The conference seeks novel research advances on science and engineering from both academia and industries, concerning all aspects of autonomic computing, including but not limited to the following research topics: Foundations ◾Fundamental theory of autonomic computing ◾Algorithms, such as artificial intelligence, machine learning, control theory, operation research, probability and stochastic processes, queueing theory, rule-based systems, biological-inspired techniques, and socially-inspired techniques ◾Formal models and analysis of self-management, emergent behavior, uncertainty, self-organization, self-awareness, and trustworthiness Resource Management in Data Centers ◾Hypervisors, operating systems, middleware, and platforms for self-managing data centers and cloud infrastructures ◾Sensing and computing/storage/networking/power/cooling resource adaptation ◾Autonomic components, such as multi-core servers, storage, networking, and hardware accelerators ◾Applications and case studies of end-to-end design and implementation of systems for resource and performance management Autonomic Cloud Computing ◾Self-managing cloud services ◾Autonomic cloud applications and services ◾Autonomic virtual cloud resources and services ◾Cloud workload characterization and prediction ◾Monitoring, modeling and analysis of cloud resources and services ◾Anomaly behavior analysis of autonomic systems and services ◾Autonomic aspects of combining cloud computing with fog and edge computing Cyber-Physical Systems (CPS) and Internet of Things (Io T) ◾System architectures, services, middleware, and protocols for CPS and Io T ◾Energy, real-time, and mobility management ◾Design principles, methodologies, and tools for CPS and Io T ◾Self-organization under severe resource constraints ◾Applications and case studies of autonomic CPS and Io T Autonomics for Extreme Scales ◾Large scale autonomic systems ◾Self-optimizing and self-healing at peta-computing scale ◾Self-managing middleware and tools for extreme scales ◾Experiences in autonomic systems and applications at extreme scales (peta/exa-computing) Self-Organization and Organic Computing ◾Self-organization principles and organic computing principles borrowed from systems theory, control theory, game theory, decision theory, social theories, biological theories, etc.

◾Self-organization, emergent behavior, decentralized control, individual and social/organizational learning, scalability, robustness, goal- and norm-governed behavior, online self-integration for trustworthy self-organizing and organic systems ◾Infrastructures and architectures for self-organizing systems and organic computing systems ◾Applications and case studies for self-organization and organic computing Emerging Computing Paradigms: Cognitive Computing, Self-Aware Computing ◾Advanced learning for cognitive computing such as hyperparameter tuning, meta-cognitive learning, self-regulatory learning, consciousness and cognition in learning, collaborative / competitive learning, and online / sequential learning ◾Architectures, control, algorithmic approaches, instrumentation, and infrastructure for cognitive computing and self-aware systems ◾Cognitive computing and self-awareness in heterogeneous and decentralized systems ◾Applications and case studies for social networks, big data systems, deep learning systems, games, and artificial assistants, cognitive robots, and systems with self-awareness and self-expression Software Engineering for Autonomic Computing Systems: Architecture, Specifications, Assurances ◾Design methodology, frameworks, principles, infrastructures, and tools for development and assurances for autonomic computing systems ◾System architectures, services, components and platforms broadly applicable for autonomic computing system engineering ◾Goal specification and policies, modeling of service-level agreements, behavior enforcement, IT governance, and business-driven IT management ◾Applications and case studies for software engineering approaches for autonomic computing systems In addition to fundamental results, ICAC is also interested in applications and experiences with prototyped or deployed systems solving real-world problems in science, engineering, business, or society.

It also provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Autonomic Computing.

Prospective authors are kindly encouraged to contribute to and help shape the conference through submissions of their research abstracts, papers and e-posters.

At least one author of each accepted paper is expected to attend the conference.

Each paper should be submitted in one of the following two categories, to which different acceptance criteria are to be applied: ◾Full paper, limited to 10 pages including references (double column, IEEE format) ◾Short paper, limited to 6 pages including references (double column, IEEE format) Full papers are expected to report new scientific or engineering results, sharing experiences, measurements, use case studies, and appropriate quantitative evaluation if at all possible.


Comments Autonomic Computing Research Papers

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