Simulations and the ability to predict the future can show what effect adding new hardware, or changing the load, can have. Armed with the right know-how, anybody can use this tool.
Performance management comprises three sequential processes: monitoring, analysis, and modeling (Figure 1). Monitoring is the theme of this article, analysis is the ability to identify patterns in monitored data, and modeling uses monitored data to predict future events, such as resource bottlenecks. PDQ (Pretty Damn Quick), a queuing analysis tool that comes as a Perl module, helps make predictions possible.
Figure 1: A schematic timeline showing the relationship between performance monitoring, analysis, and modeling.
Introduction
Choosing a monitoring solution and completing the installation and configuration isn't the end; it's just a new starting point. The core requirement is to collect monitored performance data. Without that, the performance characteristics of systems and applications cannot begin to be quantified. That is the monitoring phase.
But, monitoring alone is akin to watching needles jitter on the dashboard of a car or the cockpit of an aircraft. To assess the future picture, it is important to look out the window and see what is down the road or what other aircraft might be flying near you. The problem with just relying on monitoring alone is that it only
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One tool you can use to monitor the performance of storage devices is iostat
. In this article, we talk a bit about iostat, introduce a Python script that takes iostat data and creates an HTML report with charts, and look at a simple example of using iostat to examine storage device behavior while running IOzone.
The Linux kernel has several I/O schedulers that can greatly influence performance. We take a quick look at I/O scheduler concepts and the options that exist within Linux.
The Linux kernel has several I/O schedulers that can greatly influence performance. We take a quick look at I/O scheduler concepts and the options that exist within Linux.