Autonomic Computational Science

Chair: Manish Parashar and Omer F. Rana

Program Committee Members

Recent years have seen significant strategic initiatives aimed at realizing national and global cyber infrastructures, which will enable seamless, secure, on-demand access to, and aggregation of, geographically distributed computing, communication and information resources. The result is a pervasive computational infrastructure that integrates computers, networks, data archives, instruments, observatories, experiments, and embedded sensors and actuators. This in turn has the potential for catalyzing new paradigms and practices in computational science and engineering - those that is information/data-driven and symbiotically and opportunistically combines computations, experiments, observations, and real-time information, thereby enabling users to model, manage, control, adapt and optimize virtually any realizable sub-system of interest.

However the ability of scientists to realize this potential is being severely hampered primarily due to the increasing complexity and dynamism of the computing environments. In fact, to be productive, scientists often have to comprehend and manage complex computing configurations, software tools and libraries as well as application parameters and behaviors. Autonomic computing concepts, which have been effectively used to manage and optimize enterprise systems and applications, provide promising concepts that can be used to address these challenges. Autonomic computing is inspired by biological systems, and aims at developing systems and application that can manage and optimize themselves using only high-level guidance or interference from users. Autonomic computing systems dynamically adapt to changes in accordance with business policies and objectives and take care of routine elements of management similar to the unconscious self-regulation behavior of biological systems.

The primary objective of this track is to investigate this intersection of autonomic computing and computational science and to provide a forum to discuss challenges; best practices, crosscutting solutions and application experiences.

Areas of interest for this track may be divided into two themes:

• Autonomic Computational Science: Applications
  • Fundamental science of self-managing systems: understanding, controlling, or exploiting emergent behavior, fault-tolerance, machine learning, control theory, predictive methods.
  • Support for autonomic decision-making under uncertainty: methods based on probability, decision and utility theories, techniques for bounding the effect of missing and/or incorrect information, trading time and space resources with certainty, fusing uncertain information of different kinds, real-time inference algorithms.
  • Application Case Studies: Earth Sciences, Healthcare, Automotive Industries, Manufacturing, Finance, Chemical Engineering, Traffic Management, Network Management.
• Autonomic Computational Science: Infrastructures
  • Software engineering principles and architectures for self-managing systems: based on interoperable Grid Services, agent-based systems, Web Services, model-based systems or novel paradigms such as biological, economic or social.
  • System-level technologies, middleware or services that entail interactions among two or more components of self-managing systems in standalone, distributed, cluster, and Grid computing environments (e.g., health monitoring, dependency analysis, problem localization or remediation, workload management, and provisioning).
  • Management of autonomic applications and infrastructure: such as specification and modeling of service-level agreements, negotiation/conversation support, behavior enforcement, etc., tie in with IT governance, and interaction with legacy systems.
  • Interfaces to autonomic systems: including user interfaces, interfaces for monitoring and controlling behavior, techniques for defining, distributing, and understanding policies.
  • Experiences with autonomic system or component prototypes: measurements, evaluations, or analyses of system behavior, user studies, experiences with large-scale deployments of self-managing systems or applications.