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Technical considerations
Some of the key technical considerations that are critical to a storage-focused OpenStack design architecture include:
- Input-Output requirements
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Input-Output performance requirements require researching and modeling before deciding on a final storage framework. Running benchmarks for Input-Output performance provides a baseline for expected performance levels. If these tests include details, then the resulting data can help model behavior and results during different workloads. Running scripted smaller benchmarks during the lifecycle of the architecture helps record the system health at different points in time. The data from these scripted benchmarks assist in future scoping and gaining a deeper understanding of an organization's needs.
- Scale
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Scaling storage solutions in a storage-focused OpenStack architecture design is driven by initial requirements, including
IOPS, capacity, bandwidth, and future needs. Planning capacity based on projected needs over the course of a budget cycle is important for a design. The architecture should balance cost and capacity, while also allowing flexibility to implement new technologies and methods as they become available. - Security
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Designing security around data has multiple points of focus that vary depending on SLAs, legal requirements, industry regulations, and certifications needed for systems or people. Consider compliance with HIPPA, ISO9000, and SOX based on the type of data. For certain organizations, multiple levels of access control are important.
- OpenStack compatibility
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Interoperability and integration with OpenStack can be paramount in deciding on a storage hardware and storage management platform. Interoperability and integration includes factors such as OpenStack Block Storage interoperability, OpenStack Object Storage compatibility, and hypervisor compatibility (which affects the ability to use storage for ephemeral instance storage).
- Storage management
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You must address a range of storage management-related considerations in the design of a storage-focused OpenStack cloud. These considerations include, but are not limited to, backup strategy (and restore strategy, since a backup that cannot be restored is useless), data valuation-hierarchical storage management, retention strategy, data placement, and workflow automation.
- Data grids
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Data grids are helpful when answering questions around data valuation. Data grids improve decision making through correlation of access patterns, ownership, and business-unit revenue with other metadata values to deliver actionable information about data.
When building a storage-focused OpenStack architecture, strive to build a flexible design based on an industry standard core. One way of accomplishing this might be through the use of different back ends serving different use cases.