Synthesizing Representative I/O Workloads Using Iterative Distillation
Kurmas, Zachary Alan
Mackenzie, Kenneth M.
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Storage systems designers are still searching for better methods of obtaining representative I/O workloads to drive studies of I/O systems. Traces of production workloads are very accurate, but inflexible and difficult to obtain. (Privacy and performance concerns discourage most system administrators from collecting such traces and making them available to the public.) The use of synthetic workloads addresses these limitations; however, synthetic workloads are accurate only if they share certain key properties with the production workload on which they are based (e.g., mean request size, read percentage). Unfortunately, we do not know which properties are "key" for a given workload and storage system. We have developed a tool, the Distiller, that automatically identifies the key properties (more formally called attribute-values) of the workload. These attribute-values can then be used to generate a synthetic workload representative of the production workload. This paper presents the design and evaluation of the Distiller. We demonstrate how the Distiller finds representative synthetic workloads for simple artificial workloads and three production workload traces.
- CERCS Technical Reports 
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