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The fundamentals of storage efficiency

Performance optimization of a storage system requires maximizing storage processing resource utilization while minimizing storage system congestion. As processing utilization increases, the remaining processing resources and the ability to serve additional requests decreases; if that buffer is too small, the likelihood of hitting a performance limit increases. For instance, an array that operates at 50% average performance utilization is less likely to hit performance limits than one that has an 80% average utilization.

Reporting, monitoring and storage analytics that come with storage systems, or are available as add-ons from array vendors or third-parties, are critical components to optimizing performance utilization and adding processing resources as needed, rather than oversizing an array or reacting after the fact. “Whether it’s performance or capacity, measuring metrics and knowing what you have through tools from storage vendors or third parties is the most important thing,” Schulz said.

These tools not only help identify performance issues, they’re important in determining the appropriate cure.

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Performance of a storage system isn’t determined by a single metric but by a combination of factors that affect how fast data can be served to apps. IOPS, throughput and latency are the key factors, but they vary depending on the workload (random vs. sequential), block size (large vs. small) and transaction type (read vs. write), and their relevance and impact on performance is application dependent. For instance, when streaming video applications, fast sequential reads of bigger files and larger blocks prevail, but randomized reads are usually the predominant transactions found in virtualized environments.

We’ll now look at techniques for optimizing performance and storage resource utilization. Since there isn’t a magic storage bullet, each approach has its merits and disadvantages.

This was first published in August 2012

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