storage

Eliminating the I/O blender for flexible data placement

John Mazzie, Sayali Shirode

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 write amplification (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 Micron 7450 PRO SSD 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 Micron 7450 PRO SSDs, 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 Figure 1.

data layout of FDP implementation

Figure 1

data layout of FDP implementation

Figure 2

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

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MTS, Systems Performance Engineer

John Mazzie

John Mazzie is a storage engineer who has been with Micron since 2016 and has been on the SNIA Technical Council as an elected member since October 2025. John is interested in the storage use case for AI and looks forward in helping the industry figure out this next phase for storage. Prior to joining Micron, John was at Dell working on the development of the MD Series of storage arrays.
John Mazzie

Storage Solutions Engineer

Sayali Shirode

Sayali is a Staff Engineer, Systems Performance at Micron. Her current work focuses on analyzing the performance of AI workloads and data-center applications for storage systems. She holds a Master's degree in Electrical and Computer engineering from Colorado State University.
Sayali Shirode

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