Workload dependent IO scheduling for fairness and efficiency in shared storage systems

Supporting QoS control mechanisms in shared storage arrays is constrained by the well-justified fear of impacting the system efficiency. This motivates our study of the trade off between fairness and efficiency in shared storage systems. We 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, we show that the adaptive proportional share algorithm achieves over 90% IO efficiency while maintaining the specified QoS requirements.