Internet services have become an important class of driving applications for scalable and quality aware distributed computing technologies. Service differentiation is to provide different quality levels to satisfy requirements of Internet services by allocating resources to classified user requests in a biased way. In this research project, the investigators take an analytical and organized approach to examine resource management techniques for quantitative service differentiation in popular multi-tier server clusters. Multi-tier clusters provide new computing opportunities but also challenges such as cross-tier performance dependencies and concurrency limits. The project designs sound differentiation models that consider both system-wide resource allocation optimization and user-side fair service quality provisioning. It provides service differentiation and system performance analysis from a new important metric, slowdown, defined as the ratio of a job's delay to its service time. The deliverables are innovative resource allocation approaches for predictable and controllable service differentiation and performance isolation on multi-tier server clusters, based on the foundations of queuing theory and control theory. The significance of the research is to provide quality aware Internet computing techniques. The impacts lie in renewing the understandings of computer system performance from the unique slowdown perspective and in quality control for scalability and dependability enhancements of the distributed systems. Potential applications include an artifact against flash-crowds traffic. The research results will be disseminated to the public as published technical reports. The project will help the society develop quality aware applications and salable computing technologies for popular Internet services.
