Cascade: Optimal Transaction Scheduling for High-Contention Workloads

The performance of multi-core transactional systems is a well-studied area, with the ultimate goal of optimizing the balance between high concurrency and the appearance of serial execution. In recent years, partitioning-based systems have shown that we can gain benefits from ahead-of-execution analysis on batches of transactions. However, this benefit is largely lost as contention increases. In this work, we investigate what it means for a batch to be executed optimally in the face of high contention. Intuitively, we will consider a schedule to be optimal if it minimizes the time spent waiting for exclusive access to tuples. We also design an algorithm that can optimally schedule batches, that satisfy particular properties, in quasi-linear time. We then implement it in a system called Cascade, and provide a preliminary evaluation against an existing system.