Semantic Prefetching of Correlated Query Sequences

We present a system that optimizes sequences of related client requests by combining small requests into larger ones, thus reducing per-request overhead. The system predicts upcoming requests and their parameter values based on past observations, and prefetches results that are expected to be needed. We describe how the system makes its predictions and how it uses them to optimize the request stream. We also characterize the benefits with several experiments.