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Before adding solid-state drives, right-size your infrastructure using workload profiling

Len Rosenthal, Load DynamiX | Oct. 19, 2015
If you’re looking to add Solid-State Drives to your storage environment you want to avoid under-provisioning to ensure performance and scalability, but to meet cost goals and avoid unnecessary spending you need to avoid over-provisioning. Workload profiling can help you achieve the critical balance.

Applying workload profiles

After creating our workload profiles, we can typically use them to validate storage solutions with the following types of testing:

  • Limits finding: determining the workload conditions that drive performance below minimal thresholds, and the documenting of storage behavior at failure point
  • Functional testing: the investigation under simulated load of various functions of the storage system (e.g., backup, etc.)
  • Error injection: the investigation under simulated load of specific failure scenarios (e.g., fail-over when a drive or controller fails)
  • Soak testing: the observation of the storage system under load sustained over extended periods of time (e.g., 3 days, 1 week, or more)

Performance and load testing with workload profiles can be used to tune and validate flash and hybrid storage infrastructure in critical areas, including:

  • Properly pre-conditioning flash arrays to create a realistic application state prior to applying load.
  • Stressing and profiling of specific SSD behaviors, such as data compression and deduplication techniques and other common enterprise features, such as clones and snapshots.
  • Testing with realistic emulations of application workloads typically deployed with flash storage.

Why is workload profiling important?

Workload profiling can offer vital insights into the existing or planned SSD infrastructure of an organization, empowering storage professionals to optimize cost while assuring performance and reliability goals are met.

With a robust storage performance validation process in place, engineers and architects can optimally select and configure networked storage systems for their workloads by aligning performance requirements to purchase and deployment decisions.  With such insight, application performance can be predictably assured and storage costs can be significantly reduced for production storage systems.  It simply enables storage architects and engineers to make better, more informed decisions by eliminating the guesswork related to storage performance.

 

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