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Plan enterprise storage from measured workloads and service requirements—not from a drive count, a peak IOPS figure, or a single capacity forecast. Estimate usable capacity and performance separately, check the limits of every component in the data path, compare architectures against application needs, then validate the design with representative workloads.
What should a storage plan account for?
A defensible plan connects each application and data set to its capacity, performance, protection, and service requirements. Start by identifying what the system must support and how success will be measured; platform selection comes later.
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- Capacity: current data, expected growth, retention, snapshots, replicas, metadata or filesystem overhead, and any platform-specific reserve or overprovisioning behavior.
- Performance: IOPS, throughput, latency, I/O size, read/write mix, sequential or random access, concurrency, and the difference between typical and peak periods.
- Service requirements: availability, recovery objectives, security, encryption, data residency, consistency, access method, and cost constraints.
- Scope: the planning horizon, workload assumptions, and whether measurements come from a host, volume, pool, or whole array.
Keep production, development, test, backup, and archive requirements distinct. Google Cloud’s storage-planning questionnaire covers data type, present and future capacity, access, read/write patterns, simultaneous clients, security, replication, consistency, I/O rate, and throughput; it also notes that different environments can warrant different performance choices.
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How do you establish workload requirements?
Inventory applications and data
For each workload, record the interface it needs—block, file, or object—and how data is read and written. Include current capacity and the business forecast, retention and protection policies, expected client concurrency, and required availability and recovery behavior. Separate firm service-level requirements from estimates that still need measurement.
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Measure the existing environment
Use operating-system and storage-platform telemetry to characterize real demand over representative periods. Dell’s 2020 storage training manual identifies Perfmon for Windows and iostat for Linux as measurement options, and calls out workload IOPS, average I/O size, throughput, read/write percentage, and capacity as useful estimation inputs.
Capture the measurement window, workload conditions, concurrency, and collection point. A single peak reading can conceal normal demand or a short-lived burst; an array-wide average can also hide a busy application or volume. Treat vendor headline maxima as limits to investigate, not as evidence that the deployed application will achieve them.
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Translate business needs into testable requirements
Write down what the workload must sustain in normal and peak conditions, including latency expectations and the number of concurrent clients. Specify the intended planning horizon and the business assumptions behind growth and retention. This makes later estimates reviewable when those assumptions change.
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Forecast the data the workload must hold over the planning horizon, then account for the space that the chosen protection and storage design consumes. Capacity and performance are separate constraints: Dell’s sizing method calculates the drive count needed for capacity independently from the count needed for performance, then considers future growth and peak needs.
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- Start with measured occupied capacity and its growth history, segmented by application or data set where possible.
- Forecast retained data over the organization’s chosen planning horizon using explicit business assumptions rather than an unsupported universal growth rate.
- Account for snapshots, replicas, protection overhead, filesystem or metadata needs, and the platform’s reserve or overprovisioning behavior.
- Compare the resulting usable-space requirement with the usable capacity of candidate designs, not just their raw drive capacity.
There is no universal reserve percentage established for all enterprise workloads. Set a reserve based on the organization’s operational policy, workload variability, procurement lead time, and platform behavior, and record that choice in the plan. A drive is only one component of the resulting architecture; drive type and count must be checked against workload measurements and platform compatibility.
How do you size storage for IOPS, throughput, and latency?
Track these metrics together. IOPS counts operations; throughput measures the data moved over time; latency measures how long an operation takes. I/O size and read/write mix affect the relationship between them, so an IOPS target by itself does not describe bandwidth demand or application responsiveness.
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| Measure | What to capture | Why it matters |
|---|---|---|
| IOPS | Typical and peak operations, read/write mix, and concurrency | Helps characterize operation rate, but does not indicate transfer size or latency by itself. |
| Throughput | Data transferred over time, including normal and peak periods | Shows bandwidth demand; interpret it alongside I/O size and operation rate. |
| Latency | Observed response time under representative and peak conditions | Connects storage response to application service requirements. |
| I/O size and pattern | Average and peak transfer sizes; random or sequential access | Changes the performance profile and helps explain the relationship between IOPS and throughput. |
Check the entire path, not just the storage media: host, controller or virtual machine, network, array, and shared pool can each impose limits. Microsoft’s Azure Premium Storage guidance illustrates this with Azure-specific VM and disk limits: the VM must support the combined limits of its attached disks, and I/O size affects IOPS and bandwidth. Those Azure limits are not specifications for on-premises arrays. Benchmarking the application setup is a better way to observe how its actual configuration behaves.
Which storage architecture fits the workload?
First match the application’s required interface and access pattern, then compare candidate platforms for usable capacity, performance, resilience, availability, security, scalability, and cost. Google Cloud distinguishes block, file, and object storage and describes block storage as suitable for high-IOPS workloads such as transaction processing.
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| Storage type | Planning question | Consideration |
|---|---|---|
| Block | Does the application require block access and a high-operation-rate profile? | Google Cloud identifies transaction processing as an example of a high-IOPS use case. |
| File | Does the workload need shared file-based access? | Check access patterns, client concurrency, and the platform’s file-service behavior. |
| Object | Does the data and application support object-based access? | Assess access, retention, consistency, security, and growth needs against the service’s behavior. |
DAS, SAN, and NAS are broader architecture choices with infrastructure and workload trade-offs. Microsoft’s SharePoint Server documentation has a specific NAS scope: it discusses NAS support for content databases configured for remote BLOB storage. Its statements should not be generalized to unrelated applications.
Evaluate protection as part of the design
No RAID level is right for every workload. Compare usable capacity, write overhead, failure tolerance, rebuild behavior, and performance during both normal operation and degraded conditions. Verify design assumptions against the platform’s documented behavior and, where appropriate, its sizing tool or a qualified architecture review.
Microsoft’s SharePoint Server guidance recommends RAID 10 or a vendor-specific solution with equivalent performance for that SharePoint context. It is not a universal RAID prescription for enterprise storage.
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- Build a candidate configuration that meets the separate usable-capacity and performance requirements.
- Test with representative application I/O patterns, read/write mix, I/O sizes, and concurrency; include both normal and peak conditions.
- Measure latency, IOPS, and throughput, and check whether the host, network, controller, or storage layer is limiting results.
- Compare the observed results with the stated service requirements. Revise the assumptions or design if the workload does not meet them.
- After deployment, monitor capacity growth and performance, then revisit the plan when workload mix, retention, protection policy, application version, or business growth changes.
Microsoft recommends validation and monitoring in its SharePoint storage guidance, while its Azure guidance recommends benchmarking the application setup. For SharePoint Server specifically, Microsoft says a supported system must consistently return the first byte of data within 20 milliseconds. That is a SharePoint support condition, not a general enterprise-storage latency target.
Google Cloud describes storage design as iterative. In practice, the forecast and architecture should be treated as a plan to verify against observed use—not a one-time calculation whose assumptions remain fixed.
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