Improving Scalability of Database Systems by Reshaping User Parallel I/O
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Date
2022-04-05Author
Li, Ning
Jiang, Hong
Che, Hao
Wang, Zhijun
Nguyen, Minh Q.
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Show full item recordAbstract
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 userdifferentiated 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.