Abstract
In this extended abstract, we propose a new technique for query scheduling
with the explicit goal of reducing disk reads and thus implicitly increasing
query performance. We introduce \system, a learned scheduler that leverages
overlapping data reads among incoming queries and learns a scheduling strategy
that improves cache hits. \system relies on deep reinforcement learning to
produce workload-specific scheduling strategies that focus on long-term
performance benefits while being adaptive to previously-unseen data access
patterns. We present results from a proof-of-concept prototype, demonstrating
that learned schedulers can offer significant performance improvements over
hand-crafted scheduling heuristics. Ultimately, we make the case that this is a
promising research direction in the intersection of machine learning and
databases.