Autonomic Computing: Concepts, Architectures and Applications

1st IEEE International Conference on Autonomic Computing (ICAC)
New York, NY, USA,
16 May, 2004

Manish Parashar, TASSL, Rutgers University
Salim Hariri, HPDC, University of Arizona


The emergence of computational Grids and the potential for seamless aggregation, integration and interactions has made it possible to conceive a new generation of realistic, scientific and engineering simulations of complex physical phenomena. These applications will symbiotically and opportunistically combine computations, experiments, observations, and real-time data, and will provide important insights into complex systems. However, the phenomenon being modeled by these applications is inherently multi-phased, dynamic and heterogeneous (in time, space, and state) requiring very large numbers of software components and very dynamic compositions and interactions between these components. Furthermore, the underlying Grid infrastructure is similarly heterogeneous and dynamic. The combination of the two results in application development, configuration and management complexities that break current paradigms based on passive components and static compositions. Autonomic computing offers a potential solution. It is inspired by nature and biological systems (such as the autonomic nervous system) that have evolved to cope with the challenges of scale, complexity, heterogeneity and unpredictability by being decentralized, context aware, adaptive and resilient. This new era of computing driven by the convergence of biological and digital computing systems and is characterized by being self-defining, self-configuring, self-optimizing, self-protecting, self-healing, context aware and anticipatory.

The overall goal of this tutorial is to introduce Autonomic Grid Computing. In this tutorial we will outline the defining research issues, present the opportunities and challenges of Autonomic Computing. We will also review the current landscape of Autonomic Computing and present case studies of autonomic systems, applications and application development and execution environments.

This tutorial is primarily targeted towards researcher, practitioners, educators and students from academia and industry in the area of parallel and distributed computing. The tutorial will present material at all levels, including introductory and overview materials for beginners or novice readers, as well as in-depth research material for more advanced attendees.

The topics covered by the tutorial will include:

  • Autonomic Computing Overview: This part of the tutorial will present an overview of Autonomic Computing, its motivations, origins, characteristics, challenges and opportunities.

  • Autonomic Computing Issues and Approaches: This part of the tutorial will address the key research issues in developing autonomic systems and applications and will discuss potential paradigms, models and approaches and their theoretical underpinnings.

  • Autonomic Computing Landscape: This part of the tutorial will present key existing and emerging projects in industry and academia and discuss theirs focus and approach.

  • Autonomic Computing Case Studies: This part of the tutorial will present detailed case studies of autonomic systems, applications and application development and execution environments.

For more information about research activities and publication on Autonomic Computing at TASSL please see the Project AutoMate pages.

 1:00 - 2:30 Introduction to Autonomic Computing
2:30 - 3:00


3:00 - 4:30 Autonomic Computing Research
Autonomic Grid Applications
4:30 - 5:00 Research Challenges and Future Directions/Discussion