Data Centers

Workload Dependent IO Scheduling for Fairness and Efficiency in Shared Storage Systems

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Executive Summary

Supporting QoS control mechanisms in shared storage arrays is constrained by the well-justified fear of impacting the system efficiency. This motivates the authors' study of the tradeoff between fairness and efficiency in shared storage systems. They propose two adaptations that can be applied to existing IO scheduling mechanisms: the concurrency bound and the batch size. Although these knobs are well known, their impact on system performance and automatic adaptation based on current workload characteristics have not been studied before. Using synthetic benchmarks and trace workloads, they show that the adaptive proportional share algorithm achieves over 90% IO efficiency while maintaining the specified QoS requirements.

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