Recovering disk storage metrics from low‐level trace events

Abstract

SummaryBlock devices such as magnetic disks are nonvolatile data storage devices that transfer data in fixed‐size chunks. They are the main nonvolatile memory that holds the file system, and they are also used in virtual memory mechanisms such swapping and page fault handling. Investigating storage performance issues requires a full insight into the operating system internals. Kernel tracing offers an efficient mechanism to gather information about the storage subsystem at runtime. Still, the tracing output is often huge and difficult to analyze manually.In this paper, we introduce a framework to compute meaningful storage performance metrics from low‐level trace events generated by LTTng. A stateful approach is used to model the state of the storage subsystem. Efficient data structures and algorithms are proposed to offer a reasonable response time, allowing the user to navigate throughout the trace and to retrieve metrics from any time range. The framework includes a visualization system that provides different graphical views that represent the collected information in a convenient way. These views are synchronized together, forming a comprehensive perspective that makes storage performance investigation a much more comfortable task. Different use cases are presented to show the usefulness of the framework in real‐world applications.