Pump up array performance


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I/O workloads
Subsystem performance tuning starts with knowing your hosts' I/O workloads. I/O characteristics such as typical I/O transfer length, read-to-write ratio, random-to-sequential I/O ratio, I/O operations/sec, and megabytes read/sec and written/sec need to be monitored and recorded. All data recovery and availability requirements must also be considered. Workload characteristics should measure activity at the I/O subsystem and not just what's driven by the application; a host's buffer can often capture some I/O activity and never present it to the subsystem. Workload characteristics can also change once a subsystem is tuned to support it. Workload monitoring is an ongoing endeavor that should be done multiple times before and during the life of a subsystem.

Workload monitoring tools are available from a number of vendors. For open systems, EMC Corp., Hitachi Data Systems (HDS), IBM Corp., Sun Microsystems Inc. and other vendors offer performance monitors as standalone products or bundled with their storage products. For mainframes, resource measurement facility-based products provide performance data. Some databases, such as Oracle and Microsoft SQL Server, offer special-purpose performance monitors. Operating systems such as AIX, HP-UX, Mac OS X, Red Hat Linux, Windows NT/Server 2003 and others have special-purpose performance/activity monitors that can display this sort of information on a host basis.

Some storage resource management (SRM) applications

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such as Computer Associates (CA) International Inc.'s BrightStor, Hewlett-Packard (HP) Co.'s AppIQ and others have features or components that provide workload performance statistics. Lacking these tools, open-systems administrators can use iostat and/or sar commands to gather device statistics on a device-by-device basis for a single host. Gathering and merging this information from all hosts generating a workload may produce sufficient results for workload measurement.

From a subsystems perspective, most workloads exhibit a time-dependent mixture of activities; however, primary workloads generally fall into the following three dimensions:


  • Highly sequential workloads require a high degree of read and/or write activities.

  • Highly random workloads demand a high number of read and/or write operations/sec with no perceived pattern in the block numbers accessed.


  • High-throughput workloads access large blocks of data and push multiple megabytes/sec or gigabytes/sec to or from a subsystem.

  • High-transaction or low-throughput workloads demand a relatively high number of small blocks of data from a subsystem.


  • High read-to-write workloads demand a relatively low amount of write activity from a subsystem.
Most workloads can be characterized within these three dimensions. A video production house, for example, might have a high-sequential, highthroughput workload that's characterized by a low read-to-write ratio (e.g., lots of video being written). On the other hand, a video server environment may exhibit similar needs for high sequential/throughput, but a relatively high read-to-write ratio (lots of video being read). Database workloads are more complex. Indices may be considered low throughput and high random, and the read-to-write ratio can be high or low depending on update frequency. Database table access varies between write and read based on the size of rows, the frequency of updates and the amount of ad hoc queries (sequential vs. random).

This was first published in January 2006

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