This document discusses improving the performance of MapReduce jobs in heterogeneous computing environments. The original MapReduce scheduler assumes tasks will progress at a constant rate, but in clouds with diverse hardware, tasks can finish at different speeds. The proposed LATE scheduler estimates each task's finish time and prioritizes speculative execution of the task predicted to finish last. This approach cut response times by half compared to the original scheduler in tests on Amazon EC2.