NVMe固态硬盘的工作负载感知预算补偿调度

Byung-Geun Jun, Dongkun Shin
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引用次数: 26

摘要

近年来,为了降低功耗和提高I/O性能,固态硬盘(ssd)正在取代数据中心存储系统中的硬盘驱动器(hdd)。此外,为了缓解主机和SSD之间I/O接口的性能瓶颈,利用pcie的NVMe SSD正在出现在数据中心SSD中。NVMe接口支持称为单根I/O虚拟化(SR-IOV)的I/O虚拟化机制,这是一种设备自虚拟化技术,支持从虚拟机到I/O设备的直接路径。多个虚拟机可以共享一个支持sr - iov的物理设备,而无需虚拟机监视器的干预。支持sr - iov的SSD应该提供一个设备级调度器,它可以在考虑性能隔离和公平性的情况下调度来自多个vm的请求。在本文中,我们提出了一种设备级请求调度的工作负载感知预算补偿调度算法。为了保证性能隔离,设备级调度器估计SSD设备中每个虚拟机对垃圾收集(GC)成本的贡献。根据估计的GC贡献,将补偿每个VM的预算以实现性能隔离。我们用SSD模拟器实验了所提出的技术的效果。实验表明,该调度器可以保证多个虚拟机共享不同工作负载的NVMe SSD时的性能隔离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Workload-aware budget compensation scheduling for NVMe solid state drives
Recently, solid state drives (SSDs) are replacing hard disk drives (HDDs) in datacenter storage systems in order to reduce power consumption and improve I/O performance. Additionally, in order to mitigate the performance bottleneck at I/O interface between host and SSD, the PCIe-leveraging NVMe SSD is emerging for data center SSDs. The NVMe interface supports the I/O virtualization mechanism called single root I/O virtualization (SR-IOV), which is a device self-virtualization technique for supporting direct paths from virtual machines (VMs) to I/O devices. Multiple virtual machines can share an SR-IOV-supporting physical device without intervention of virtual machine monitor. SR-IOV-supporting SSD should provide a device-level scheduler which can schedule the requests from multiple VMs considering performance isolation and fairness. In this paper, we propose a workload-aware budget compensation scheduling algorithm for the device-level request scheduler. To guarantee the performance isolation, the device-level scheduler estimates the contribution on the garbage collection (GC) cost of each virtual machine in the SSD device. Based on the estimated GC contributions, the budget of each VM is compensated for performance isolation. We experimented the effects of the proposed technique with an SSD simulator. The experiments showed that the scheduler can guarantee the performance isolation when multiple VMs share an NVMe SSD with different workloads.
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