Utility Data Center (UDC) provides a flexible, cost-effective infrastructure to support the hosting of applications for Internet services. In order to enable the design of a "utility-aware" streaming media service which automatically requests the necessary resources from UDC infrastructure, we introduce a set of benchmarks for measuring the basic capacities of streaming media systems. The benchmarks allow one to derive the scaling rules of server capacity for delivering media files which are: i) encoded at different bit rates, ii) streamed from memory vs disk. Using an experimental testbed, we show that these scaling rules are non-trivial. In this paper, we develop a workload-aware, media server performance model which is based on a cost function derived from the set of basic benchmark measurements. We validate this performance model by comparing the predicted and measured media server capacities for a set of synthetic workloads.

Abstract In order to design a "utility-aware" streaming media service which automatically requests the necessary resources from Utility Data Center infrastructure, several classic performance questions should be answered: how to measure the basic capacity of a streaming media server? what is the set of basic benchmarks exposing the performance limits and main bottlenecks of a media server? In this paper, we propose a set of benchmarks for measuring the basic capacities of streaming media systems for different expected workloads, and demonstrate the results using an experimental testbed.