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Spatial Locality Aware Disk Scheduling in Virtualized Environment

Xiao Ling 1 Shadi Ibrahim 2 Song Wu 1 Hai Jin 1
2 KerData - Scalable Storage for Clouds and Beyond
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : Exploiting spatial locality, a key technique for improving disk I/O utilization and performance, faces additional challenges in the virtualized cloud because of the transparency feature of virtualization. This paper contributes a novel disk I/O scheduling framework, named Pregather, to improve disk I/O efficiency through exposure and exploitation of the special spatial locality in the virtualized environment, thereby improving the performance of disk-intensive applications without harming the transparency feature of virtualization. The key idea behind Pregather is to implement an intelligent model to predict the access regularity of spatial locality for each VM. Moreover, Pregather embraces an adaptive time slice allocation scheme to further reduce the resource contention and ensure fairness among VMs. We implement the Pregather disk scheduling framework and perform extensive experiments that involve multiple simultaneous applications of both synthetic benchmarks and MapReduce applications on Xen-based platforms. Our experiments demonstrate the accuracy of our prediction model and indicate that Pregather results in the high disk spatial locality and a significant improvement in disk throughput and application performance.
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Xiao Ling, Shadi Ibrahim, Song Wu, Hai Jin. Spatial Locality Aware Disk Scheduling in Virtualized Environment. IEEE Transactions on Parallel and Distributed Systems, Institute of Electrical and Electronics Engineers, 2015, pp.14. ⟨10.1109/TPDS.2014.2355210⟩. ⟨hal-01087602⟩

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