SNIA SSSI SSSI
results across multiple test runs and multiple DUTs. This enables direct, consistent, DUT-to-DUT performance characterization.
£ The profile of the stimulus can be managed to reflect aspects of the user’s workload, enabling comparison of a single DUT to multiple real-world use cases. This increases the efficiency of the test process because one can establish the DUT’s performance across the potential use cases and do so non-disruptively and in a single test run.
£ Defining workload stimulus at the block IO level eliminates the affects of the software/hardware stack on the test stimulus - i.e. different test environments may treat application level IOs differently thus presenting a different test stimulus at the device level. Use of device level IO characterization ensures that the test sponsor is directly testing the DUT with a known test stimulus, not one that is changed as it traverses the IO software/hardware stack. Furthermore, knowing your workload characteristics at the device level and comparing those to the synthetic test results enables:
£ Knowledge of what to tests best determine a given SSD’s performance strengths and weaknesses
£ Accurate comparison of different SSDs £ Correct selection of the best SSD for several different workloads
SSDs have introduced a heretofore unheard of level of storage device performance. Whereas IOPs used to be measured in the hundreds (at best), SSDs have increased the single drive performance by at least two orders of magnitude. However, this dramatic performance improvement comes at a substantial cost. These, combined with the “drop in” appearance of SSDs, require that a greater importance be placed on understanding the variables that affect performance measurement. Although the natural tendency is to measure SSD performance the same way that one traditional measures conventional
storage device performance - i.e. through the complete software IO stack - a different approach is required since SSD performance can be greatly affected by host side variables. Synthetic workload testing applied directly to the DUT (treated as a block level device) ensures that host side variables are minimized. This results in a more precise measurement with far fewer run-to-run, and host-to-host variances. Synthetic tests can still be mapped to expected application traffic and, in fact, offer a clearer understanding of overall performance expectation. This is due to their consistent results and their ability to measure several workloads in a single test run. The use of a synthetic workload test is preferred because end user workloads are extremely diverse. This enables one to accurately and consistently measure SSD performance under the widest variety of workloads and demand intensities. The end user, knowing the attributes of their particular IO profile, can select those test results, which best represents their environment.
The SNIA SSS Technical Working Group (TWG) at
www.snia.org has published a reference Performance Test Specification (PTS). Included in that specification are application independent test methods that will offer the reader insight into a wide variety of SSD performance metrics as well as a specification for a reference test platform. The SNIA Performance Test Specification is free to download - the reader need not join SNIA. The test specification is available at:
www.snia.org/sssi/pts
In addition, the SSSI has posted an “Understanding SSD Performance” webpage at
www.snia.org/forums/sssi/pts where the reader can see summary PTS SSD performance results, links to various SSD related resources and other SSD performance items. For more information about SNIA and the Solid State Storage Initiative (SSSI) visit
www.snia.org/forums/sssi.
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