The exponential growth in computing capability and use has produced a
high demand for large, high-performance storage systems.
Unfortunately, advances in storage system research have been limited
by (1) a lack of evaluation workloads, and (2) a limited understanding
of the interactions between workloads and storage systems. We have
developed a tool, the Distiller that helps address both
limitations.
Our thesis is as follows: Given a storage system and a workload for
that system, one can automatically identify a set of workload
characteristics that describes a set of synthetic workloads with the
same performance as the workload they model. These representative
synthetic workloads increase the number of available workloads with
which storage systems can be evaluated. More importantly, the
characteristics also identify those workload properties that affect
disk array performance, thereby highlighting the interactions between
workloads and storage systems.
This dissertation presents the design and evaluation of the Distiller.
Specifically, our contributions are as follows. (1) We demonstrate
that the Distiller finds synthetic workloads with at most 10% error
for six out of the eight workloads we tested. (2) We also find that
all of the potential error metrics we use to compare workload
performance have limitations. Additionally, although the internal
threshold that determines which attributes the Distiller chooses has a
small effect on the accuracy of the final synthetic workloads, it has
a large effect on the Distiller's running time. Similarly, (3) we find
that we can reduce the precision with which we measure attributes and
only moderately reduce the resulting synthetic workload's
accuracy. Finally, (4) we show how to use the information contained in
the chosen attributes to predict the performance effects of modifying
the storage system's prefetch length and stripe unit size.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/4985 |
| Date | 14 May 2004 |
| Creators | Kurmas, Zachary Alan |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 850106 bytes, application/pdf |
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