Scope:

Most parallel and distributed systems become increasingly large and complex, thereby compounding many reliability problems. Those systems contain different types of software/hardware/network, cover a wide area, provide services to many users, and thus are exposed to many dangers such as accidental/deliberate faults, virus infections, malicious attacks, illegal intrusions, and natural disasters etc. As a result, too often computer systems fail, become compromised, or perform poorly. Therefore, it is difficult to analyze, evaluate and improve the reliability of those parallel and distributed systems. To improve the system reliability, the components of the system should have high reliability, and thus the software reliability, hardware reliability and network reliability should be studied and improved.

More importantly, the management and schedule of the system should also be well arranged for improving the fault-tolerance, reliability, performance and other behaviors. One of the most interesting methods is the Autonomic Management which offers a potential solution to these challenging research problems. 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 is 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.

This workshop is to bring together computer scientists, industrial engineers and researchers to discuss and exchange experimental or theoretical results, novel designs, work-in-progress, experience, case studies, and trend-setting ideas in the area of reliability and autonomic management of parallel and distributed systems.