| Rapid advancement of communication technology has changed the landscape of computing. New models of computing, such as business-on-demand, Web services, peer-to-peer networks, and Grid computing have emerged to harness distributed computing and network resources to provide powerful services. In these new computing platforms resources are shared, and likely are remote and out of the user's control. Consequently, resource availability to each user varies largely from time to time, due to resource sharing, system configuration change, potential software or hardware failure, and other factors beyond the control of a user. This non-deterministic nature of the resource availability raises an outstanding challenge to these newly-emerged models of computing: how to support Quality of Service (QoS) to meet a user's demand? In this dissertation, we conduct a through study of QoS of distributed computing, especially on Grid computing where the requirement of distributed sharing and coordination goes to the extreme. We propose a software solution based on advanced performance modeling, resource management and monitoring, and task allocation and scheduling techniques for system support of QoS in shared network environments. We develop and build different models to evaluate the impact of various resource availabilities and parallel processing on application performance, such as the effect of local jobs on the remote application's performance, the effect of resource reservation on local jobs' performance, and the impact of system failure on the application's execution time. To support the application of these novel performance models in practice, we implement effective and adaptive performance measurement mechanisms to dynamically monitor resource and application QoS. Based on resource sharing policies and application perspectives of QoS, we design and develop different task allocation strategies and scheduling algorithms to optimize the application completion time or application failure probability. To verify the efficiency of these advanced performance modeling, task scheduling, and resource management mechanisms, we develop and implement a prototype of QoS system, named Grid Harvest Service (GHS). This prototype software system is designed to provide system support of QoS in the most challenging distributed computing environment, Grid computing. It provides end-users with QoS guaranteed service automatically based on the underlying QoS policies. |
