Large-scale systems, such as mega data centers, computer clouds, cyber-physical systems, Internet of Things, are becoming increasingly complex and burdensome for people to manage. Autonomic computing systems reduce this burden by managing their own behavior in accordance with high-level goals. 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 two premier conferences on autonomic computing, the IEEE International Conference on Autonomic Computing (ICAC) and the IEEE International Conference on Cloud and Autonomic Computing (ICCAC) will be unified into one from 2018 on. The first joint conference, the 15th IEEE International Conference on Autonomic Computing (ICAC 2018) will be held in Trento, Italy on September 3-7, 2018, within the context of the FAS* event federated with SASO 2018.

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 (IoT)

• System architectures, 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

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. Typical application areas for ICAC include but are not limited to autonomous robotics, cloud/fog/edge computing, cyber-physical systems, data centers, dependable computing, industrial internet / industry 4.0, internet of things, mobile computing, service-oriented systems, smart buildings, smart city, smart grid / energy management, smart factory, smart user interfaces, space applications, and traffic management.