The real-time scheduling advisor (RTSA) is an entirely userlevel system that an application running on a typical shared, unreserved distributed computing environment can turn to for advice on how to schedule its compute-bound soft real-time tasks. Given a list of hosts, a description of the CPU demands of the task, the deadline, and a confidence level, the RTSA will recommend one of the hosts and predict, as a confidence interval, the running time of the task on that host. The RTSA is based on a scalable and extensible shared resource prediction system based on statistical time series analysis. In this paper, we first describe how the RTSA builds on this underlying system to provide its service, and then we evaluate its performance using a randomized methodology based on real background workloads, determining the effect of different factors. We also compare it with a random approach and a measurement-based approach.