Improving scalability of database systems by reshaping user parallel I/O
Improving scalability of database systems by reshaping user parallel I/O
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DOI：
10.1145/3492321.3519570
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发表时间：
2022-03
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通讯作者：
Ning Li;Hong Jiang;Hao Che;Zhijun Wang;Minh Q. Nguyen
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作者：
Ning Li;Hong Jiang;Hao Che;Zhijun Wang;Minh Q. Nguyen
由于高度扩展的用户并行I/O与数据库及其底层存储I/O堆栈提供的I/O容量之间的不匹配，现代数据库系统遭受折衷的吞吐量、持久的不公平I/O处理以及用户请求的不可预测的高延迟可变性。为了解决这个问题，我们引入了一个有效的以用户为中心的QoS感知调度垫片，称为AppleS，用户级细粒度的I/O监管，提供正确的用户并行I/O请求的数量和模式的数据库系统，并支持用户SLO与高级别的性能隔离和减少I/O资源争用。它的目的是使数据库系统能够主动调节用户的请求行为的基础上运行时的条件，重塑用户的访问模式，以隐藏过多的用户并行的I/O堆栈，具有有限的并发处理能力。这有助于以公平和稳定的方式为多用户工作负载实现可扩展的吞吐量。AppleS作为用户空间垫片实现，用于透明的用户差异化I/O调度，使其具有高度灵活性和可移植性。我们在真实的数据库（MySQL和MongoDB）上进行的广泛评估表明，通过将AppleS集成到现有的数据库系统中，我们的解决方案不仅可以以更公平（3.2倍到40.6倍的公平性改善）和更稳定（高达2倍的延迟变化）的方式提高吞吐量（高达39.2%），而且还可以以更少的I/O配置支持用户SLO。
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.