Improving scalability of database systems by reshaping user parallel I/O

Modern database systems suffer from compromised throughput, persistent unfair I/O processing and unpredictable, high latency variability of user requests as a result of mismatches between highly scaled user parallel I/O and the I/O capacity afforded by the database and its underlying storage I/O stack. To address this problem, we introduce an efficient user-centric QoS-aware scheduling shim, called AppleS, for user-level fine-grained I/O regulation that delivers the right amount and pattern of user parallel I/O requests to the database system and supports user SLOs with high-level performance isolation and reduced I/O resource contention. It is designed to enable database systems to proactively regulate user request behaviors based on runtime conditions to reshape user access pattern to hide excessive user parallelism from the I/O stack that has a limited concurrent processing capability. This helps achieve scalable throughput for multi-user workloads in a fair and stable manner. AppleS is implemented as a user-space shim for transparent user-differentiated I/O scheduling, making it highly flexible and portable. Our extensive evaluation, run on real databases (MySQL and MongoDB), demonstrates that, by incorporating AppleS in the existing database systems, our solution can not only improve the throughput (up to 39.2%) in a fairer (3.2× to 40.6× fairness improvement) and more stable (up to 2× lower latency variability) manner, but also support user SLOs with less I/O provisioning.