The conference seeks latest research advances on science and engineering concerning all aspects of autonomic computing, including but not limited to the following main research topics:

• Foundations

• Fundamental science and theory of autonomic computing systems and feedback control for software, self-awareness and self-expression

• Algorithms, such as AI, machine learning, control theory, operations 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, trustworthiness

• Resource Management in Data Centers

• Hypervisors, operating systems, middleware, and platforms for self-managing data centers and cloud infrastructures

• Sensing, energy efficiency, and 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 management

• Cyber-Physical Systems (CPS) and Internet of Things (IoT)

• System architectures OS, services, middleware, and protocols for CPS and IoT

• Energy, real-time, and mobility management

• Design principles, methodologies, and tools for CPS and IoT

• Self-organization under severe resource constraints

• Applications and case studies of autonomic CPS and IoT

• 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 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