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Links 1 through 10 of 118 by Ken Robson tagged analysis

The Internet’s Transmission Control Protocol, or TCP, has proved remarkably adaptable, working well across a wide range of hardware and operating systems, link capacities, and round trip delays.

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When I/O latency is presented as a visual heat map, some intriguing and beautiful patterns can emerge. These patterns provide insight into how a system is actually performing and what kinds of latency end-user applications experience. Many characteristics seen in these patterns are still not understood, but so far their analysis is revealing systemic behaviors that were previously unknown.

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In my previous post, I showed NFS random read latency at different points in the operating system stack. I made a few references to hits from DRAM - which were visible as a dark solid line at the bottom of the latency heat maps. This is worth exploring in a little more detail, as this is both interesting and another demonstration of Analytics.
Here is both delivered NFS latency and disk latency, for disk + DRAM alone:

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This is the home of the knowledge system that demonstrates the power and structure of DYA|Infrastructure, the Sogeti infrastructure architecture methodology. This is achieved by presenting the infrastructure architecture knowledge base of a fictitious company named SmartMart. Much of the content of this knowledge base is generic and applicable universally, like descriptions of infrastructure function and pattern types.

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Finding out why your Linux computer performs the way it does has been a hard task. Sure, there is Oprofile, and even ‘perf’ in recent kernels. There is LatencyTOP to find out where latencies happen.

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ost of my readers will understand that cache is a fast but small type of memory that stores recently accessed memory locations.  This description is reasonably accurate, but the “boring” details of how processor caches work can help a lot when trying to understand program performance.

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Paper on the use of time series analysis with regularly sampled systems stats.

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trace is a tracing utility built directly into the Linux kernel. Many distributions already have various configurations of Ftrace enabled in their most recent releases. One of the benefits that Ftrace brings to Linux is the ability to see what is happening inside the kernel. As such, this makes finding problem areas or simply tracking down that strange bug more manageable.

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The Ftrace tracing utility has many different features that will assist in tracking down Linux kernel problems. The previous article discussed setting up Ftrace, using the function and function graph tracers, using trace_printk(), and a simple way to stop the recording of a trace from user space. This installment will touch on how user space can interact with Ftrace, faster ways of stopping the trace, debugging a crash, and finding what kernel functions are the biggest stack hogs.

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Ftrace is an internal tracer designed to help out developers and designers of systems to find what is going on inside the kernel. It can be used for debugging or analyzing latencies and performance issues that take place outside of user-space.

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