An Adaptive IO Prefetching Approach for Virtualized Data Centers

作者：Chiang Ron C^*; Uppal Ahsen J; Huang H Howie

来源：IEEE Transactions on Services Computing, 2017, 10(3): 328-340.

DOI：10.1109/TSC.2015.2469295

• Data storage systems
• platform virtualization
• operating systems

Cloud and data center applications often make heavy use of virtualized servers, where flash-based solid-state drives (SSDs) have become popular alternatives over hard drives for data-intensive applications. Traditional data prefetching focuses on applications running on baremetal systems using hard drives. In contrast, virtualized systems using SSDs present different challenges for data prefetching. Most existing prefetching techniques, if applied unchanged in such environments, are likely to either fail to fully utilize SSDs, interfere with virtual machine I/O requests, or cause too much overhead if run in every virtualized instance. In this work, we demonstrate that data prefetching, when run in a virtualization-friendly manner can provide significant performance benefits for a wide range of data-intensive applications. We have designed and developed VIO-prefetching, consisting of accurate prediction of application needs in runtime and adaptive feedback-directed prefetching that scales with application needs, while being considerate to underlying storage devices and host systems. We have implemented a real systemin Linux and evaluated it on different storage devices with the virtualization layer. Our comprehensive study provides insights of VIO-prefetching's behavior at various virtualization system configurations, e.g., the number of VMs, in-guest processes, application types, etc. The proposed method improves virtual I/O performance up to 43 percent with the average of 14 percent for 1 to 12 VMs while running various applications on a Xen virtualization system.

• 出版日期2017-6