Suli Yang

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The progress of a big data job is often a function of storage, networking and processing. Hence, for efficient job execution, it is important to collectively optimize all three components. Prior proposals [1], in contrast, have focused on mainly on one or two of the three components. This narrow focus constraints the extent to which these proposals can(More)
We introduce <i>split-level I/O scheduling</i>, a new framework that splits I/O scheduling logic across handlers at three layers of the storage stack: block, system call, and page cache. We demonstrate that traditional block-level I/O schedulers are unable to meet throughput, latency, and isolation goals. By utilizing the split-level framework, we build a(More)
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