CAC 2015 - 3rd IEEE International Conference on Cloud and Autonomic Computing CAC 2015

Enterprise-scale cloud platforms and services systems, present common and cross-cutting challenges in maximizing power efficiency and performance while maintaining predictable and reliable behavior, and at the same time responding appropriately to environmental and system changes such as hardware failures and varying workloads. Autonomic computing systems address the challenges in managing these environments by integrating monitoring, decision-processing and actuation capabilities to autonomously manage resources and applications based on high-level policies.
Research in cloud and autonomic computing spans a variety of areas, from computer systems, architecture, middleware services, databases and data-stores, and networks to machine learning and control theory. The purpose of the 3rd International Conference on Cloud and Autonomic Computing (CAC) is to bring together researchers and practitioners across these disciplines to address the multiple facets of self-management in computing systems and applications.
Papers are solicited on a broad array of topics of relevance to cloud and autonomic computing and their intersections, particularly those that bear on connections and relationships among different research areas or report on prototype systems or experiences. The goal is to confirm a premier international forum focused on the latest research, applications, and technologies aimed at making cloud and autonomic computing systems and services easy to design, to deploy and to implement, while achieving the simultaneous goals to be self-manageable, self-regulating and scalable with little involvement of human or system administrators.
Topics of interest include, but are not limited to:
Autonomic Cloud Computing
§ Self-managing cloud services
§ Autonomic resource and energy management in cloud computing
§ 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
Autonomics for Extreme Scales
§ Large scale autonomic systems
§ Self-optimizing and self-healing at petacomputing scale
§ Self-managing middleware and tools for extreme scales
§ Experiences in autonomic systems and applications at extreme scales (peta/exa-computing)
Autonomic Computing Foundations and Design Methods
§ Evaluation, validation and quality and correctness assessment of autonomic loops
§ Theoretical frameworks for modeling and analyzing autonomic computing systems, control and decision theory
§ Model-based design, software engineering, formal methods, testing, programming languages and environments support
§ Knowledge representation and visualization of behavior of autonomic systems and services
Autonomic Computing Systems, Tools and Applications
§ Self-protection techniques of computing systems, networks and applications
§ Stochastic analysis and prediction of autonomic systems and applications
§ Benchmarks and tools to evaluate and compare different architectures to implement autonomic cloud systems
§ High performance autonomic applications
§ Self-* applications in science and engineering
§ Self-* Human Machine Interface