The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customer bases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, the authors focus in this work on portfolio scheduling – here, the dynamic selection and use of a scheduling policy, depending on the current system and workload conditions, from a portfolio of multiple policies.