Computer systems of all types and sizes including cyber–physical systems, data centers/clouds, enterprise/office systems, and “Internet of Things,” are becoming increasingly complex and burdensome for human administrators and operators to manage. Autonomic computing systems reduce this burden by managing their own behavior in accordance with high–level goals specified by humans or other computing systems. In autonomic systems, resources and applications are managed with no or minimal human intervention 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 but not limited to artificial intelligence, bio–inspired computing, control theory, decision theory, distributed systems, emergent behavior analysis, machine learning, optimization, planning, software engineering, and user interface design.<br>The IEEE International Conference on Autonomic Computing (ICAC) has been the leading conference on autonomic computing since its inception in 2004. Continuing on its past successes, the 16th edition of ICAC will be held in Umea, Sweden during July, 2019, as part of the FAS* event federated with SASO 2019. 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:<br>Foundations<br>Fundamental theory of autonomic computing<br>Algorithms, artificial intelligence, biological–inspired techniques, control theory, machine learning, operation research, probability and stochastic processes, queueing theory, rule–based systems, and socially–inspired techniques<br>Formal models and analysis of self–management, emergent behavior, uncertainty, self–organization, self–awareness, and trustworthiness<br>Autonomic Cloud Computing<br>Self–managing cloud services<br>Cloud workload characterization and prediction<br>Hypervisors, operating systems, middleware, and platforms for self–managing data centers and cloud infrastructures<br>Monitoring, modeling and analysis of cloud resources and services<br>Autonomic aspects of combining cloud computing with fog and edge computing<br>Cyber–Physical Systems (CPS) and Internet of Things (IoT)<br>System architectures, services, middleware, and protocols for CPS and IoT<br>Energy, real–time, and mobility management<br>Design principles, methodologies, and tools for CPS and IoT<br>Self–organization under severe resource constraints<br>Applications and case studies of autonomic CPS and IoT<br>Sensing and computing/storage/networking/power/cooling resource adaptation<br>Self–Organization and Organic Computing<br>Self–organization principles and organic computing principles borrowed from systems theory, control theory, game theory, decision theory, social theories, biological theories, etc.<br>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<br>Infrastructures and architectures for self–organizing systems and organic computing systems<br>Applications and case studies for self–organization and organic computing<br>Emerging Computing Paradigms: Cognitive Computing, Self–Aware Computing<br>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<br>Architectures, control, algorithmic approaches, instrumentation, and infrastructure for cognitive computing and self–aware systems<br>Cognitive computing and self–awareness in heterogeneous and decentralized systems<br>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<br>Software Engineering for Autonomic Computing Systems: Architecture, Specifications, Assurances<br>Design methodology, frameworks, principles, infrastructures, and tools for development and assurances for autonomic computing systems<br>System architectures, services, components and platforms broadly applicable for autonomic computing system engineering<br>Goal specification and policies, modeling of service–level agreements, behavior enforcement, IT governance, and business–driven IT management<br><div>Applications and case studies for software engineering approaches for autonomic computing systems<br></div><div><br></div>
Abbrevation
ICAC19
City
Umea
Country
Sweden
Deadline Paper
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End Date
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