As the World Wide Web experiences increasing commercial and mission-critical use, server systems are expected to deliver high and predictable performance. The phenomenal improvement in microprocessor speeds, coupled with the deployment of clusters of commodity workstations has enabled server systems to meet the continually increasing performance demands in a cost-effective and scalable manner. However, as the volume, variety and sophistication of services offered by server systems increase, effective support for providing differentiated and predictable quality of service has also become important. For example, it is often desirable to differentiate between the resources allocated to virtual web sites hosted on a server system so as to provide predictable performance to individual sites, regardless of the load imposed upon others. Server systems lack adequate support for providing predictable performance to hosted services in terms of metrics that are meaningful to server applications, such as average throughput, response time etc. This is because conventional systems multiplex resources by dealing with system level metrics such as CPU/disk bandwidth, memory pages etc. Maintaining predictable levels of performance in application level metrics, therefore, requires a corresponding mapping to system level metrics that is not supported in conventional systems. High performance server systems based upon cluster-based architectures also lack adequate support for providing differentiated quality of service. This is because providing differentiated quality of service in a cluster-based server system requires global resource management not found in current clusters. This dissertation is concerned with support for both differentiated as well as predictable quality of service in server systems. The specific requirements of server applications and their interactions with the resource management facilities in the operating system software are studied. This leads to a concerted design of a resource management framework for providing effective quality of service in server systems.