Approximate Query Processing has become increasingly popular as larger data sizes have increased query latency in distributed query processing systems. To provide such approximate results, systems return intermediate results and iteratively update these approximations as they process more data. In shared clusters, however, these systems waste resources by directing resources to queries that are no longer improving the results given to users. We describe ReLAQS, a cluster scheduling system for online aggregation queries that aims to reduce latency by assigning resources to queries with the most potential for improvement. ReLAQS utilizes the approximate results each query returns to periodically estimate how much progress each concurrent query is currently making. It then uses this information to predict how much progress each query is expected to make in the near future and redistributes resources in real-time to maximize the overall quality of the answers returned across the cluster. Experiments show that ReLAQS achieves a reduction in latency of up to 47% compared to traditional fair schedulers.