Abstract
In Grid computing environments, the availability, performance, and state of resources, applications, services, and data undergo continuous changes during the life cycle of an application. Uncertainty is a fact in Grid environments, which is triggered by multiple factors, including: (1) failures, (2) dynamism, (3) incomplete global knowledge, and (4) heterogeneity. Unfortunately, the existing Grid management methods, tools, and application composition techniques are inadequate to handle these resource, application and environment behaviors. The aforementioned characteristics impose serious requirements on the Grid programming and runtime systems if they wish to deliver efficient performance to scientific and commercial applications. To overcome the above challenges, the Grid programming and runtime systems must become autonomic or selfโmanaging in accordance with the highโlevel behavior specified by system administrators. Autonomic systems are inspired by biological systems that deal with similar challenges of complexity, dynamism, heterogeneity, and uncertainty. To this end, we propose a comprehensive taxonomy that characterizes and classifies different software components and highโlevel methods that are required for autonomic management of applications in Grids. We also survey several representative Grid computing systems that have been developed by various leading research groups in the academia and industry. The taxonomy not only highlights the similarities and differences of stateโofโtheโart technologies utilized in autonomic application management from the perspective of Grid computing, but also identifies the areas that require further research initiatives. We believe that this taxonomy and its mapping to relevant systems would be highly useful for academicโ and industryโbased researchers, who are engaged in the design of Autonomic Grid and more recently, Cloud computing systems. Copyright ยฉ 2011 John Wiley & Sons, Ltd.