Autonomic QoS-Aware Resource Management in Grid Computing Using Online Performance Models

As Grid Computing increasingly enters the commercial domain, performance and Quality of Service (QoS) issues are becoming a major concern. The inherent complexity, heterogeneity and dynamics of Grid computing environments pose some challenges in managing their capacity to ensure that QoS requirements are continuously met. In this paper, an approach to autonomic QoS-aware resource management in Grid computing based on online performance models is proposed. The paper presents a novel methodology for designing autonomic QoS-aware resource managers that have the capability to predict the performance of the Grid components they manage and allocate resources in such a way that service level agreements are honored. The goal is to make the Grid middleware self-configurable and adaptable to changes in the system environment and workload. The approach is subjected to an extensive experimental evaluation in the context of a real-world Grid environment and its effectiveness, practicality and performance are demonstrated.