Massive block IO systems are the work horses powering many of today's largest applications. Databases, healthcare systems, and virtual machine images, are examples for block-storage applications. The massive scale of these workloads, and the complexity of the underlying storage systems, makes it difficult to pinpoint problems when they occur. This work attempts to shed light on workload patterns through visualization, aiding our intuition.
We describe our experience in the last three years of analyzing and visualizing customer traces from XIV, an IBM enterprise block storage system. We also present results from applying the same visualization technology to Linux filesystems.
We show how visualization aids our understanding of workloads, and how it assists in resolving customer performance problems.
By: Ohad Rodeh, Haim Helman, David Chambliss
Published in: RJ10514 in 2013
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