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Eliminating the I/O blender: The promise of flexible data placement

约翰Mazzie, Sayali Shirode | July 2023

Flexible data placement (FDP) is a possible forthcoming feature of the NVMeTM specification that has been proposed by Google and Meta.1 The purpose of this feature is to reduce the 写放大 (WA) when multiple applications are writing, modifying and reading data on the same device.2 Benefits of reduced WA for these companies come in the form of more usable capacity and potentially a longer useful life for each device.

We proposed an experiment to determine how helpful FDP might be. In this test, we are using a 7.68TB 微米 7450 PRO 固态硬盘 split into four equal (1.92TB) namespaces and executing parallel flexible input/output tester (fio) workloads on each namespace.3 These workloads are all sequential writes but vary in block size (4K, 16K, 64K and 256K). We also execute these workloads individually to four 1.92TB 微米 7450 PRO 固态硬盘s, which we imagine as the most optimal implementation of FDP where all application data receives dedicated NAND space and does not get interleaved on the device as shown in 图1.

data layout of FDP implementation

图1

data layout of FDP implementation

图2

写放大 chart

Though this is a simple experiment, it shows the potential benefits for FDP implementation on future devices. We can also see how some applications, which are designed to write sequentially as much as possible, would benefit from FDP when contending for the same drive resources.

1. For additional information on FDP, see http://nvmexpress.org/wp-content/uploads/Hyperscale-Innovation-Flexible-Data-Placement-Mode-FDP.pdf 
2. For additional information on 写放大, see http://www.snia.org/education/online-dictionary/W 
3. Fio documentation is available here: http://fio.readthedocs.io/en/latest/fio_doc.html 

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Principal 存储 Solutions Engineer

约翰Mazzie

John is a Member of the Technical Staff in the Data Center Workload Engineering group in Austin, TX. He graduated in 2008 from West Virginia University with his MSEE with an emphasis in wireless communications. John has worked for Dell on their 存储 MD3 Series of 存储 arrays on both the development and sustaining side. John joined 微米 in 2016 where he has worked on Cassandra, MongoDB, 和Ceph, and other advanced 存储 workloads.

存储 Solutions Engineer

Sayali Shirode

Sayali收到了一个M.S. in electrical and computer engineering from Colorado State University in 2015. She's currently a 存储 Performance Engineer at 微米's Austin location and has previously worked as Firmware Test Engineer at 微米's Colorado location. She focuses on analyzing the performance of data center applications.