Top Benefits of Using Virtual Volumes in Modern Storage

Virtual Volumes vs. Traditional Storage: A Practical ComparisonStorage architectures shape how organizations manage data, extract value, and control costs. Two prevailing approaches—Virtual Volumes (VVols) and traditional block-based storage—offer different trade-offs in flexibility, management, performance, and operational model. This article compares them practically: what each is, how they work, where they excel, and how to choose between them.

What are Virtual Volumes?

Virtual Volumes (VVols) is a storage integration framework defined by VMware (but conceptually applicable beyond just VMware) that shifts management of storage objects from LUNs/volumes into more granular, VM-centric constructs. Instead of presenting large monolithic logical units (LUNs) to a hypervisor, the storage system exposes per-VM and per-VM-disk objects. The hypervisor and storage array communicate via a protocol (vSphere APIs for Storage Awareness — VASA) that enables policy-driven data services (snapshots, replication, QoS) to be applied at the VM-disk level.

Key characteristics:

• Granular visibility and control at the VM/VMDK level.
• Policy-driven automation of data services.
• Offloads array-level intelligence to manage per-object operations.
• Reduces need for manual LUN management and complex host-side storage constructs.

What is Traditional Storage?

Traditional storage (often called LUN-based or VMFS/NFS-based storage in virtualized environments) exposes block or file volumes to hosts. Administrators create logical units (LUNs) or file shares, format them with a filesystem (VMFS, NTFS, ext4, etc.), and place many virtual machines on these containers. Data services (snapshots, replication, thin provisioning) are usually applied at the LUN/volume level.

Key characteristics:

• Coarse-grained management at the LUN or datastore level.
• Widely supported across many platforms and storage arrays.
• Data services applied to the entire volume; per-VM granularity requires manual constructs (separate LUNs).
• Mature tooling and administrative familiarity.

How They Work — Practical Differences

• Management Model:

  • VVols: Storage objects map to VMs and VMDKs. Admins manage policies that describe required capabilities (performance, protection, replication). vCenter and array coordinate to place and manage objects automatically.
  • Traditional: Admins create datastores (LUNs) and decide VM placement. Policies are limited or manual; operations often require moving entire datastores to change protections.

• Data Services:

  • VVols: Snapshots, clones, replication, and QoS can operate per VMDK; storage array handles operations without impacting other VMs on the same physical media.
  • Traditional: Snapshots and replication operate at datastore/LUN level—snapshotting many VMs together, potentially wasting space and creating restore complexity.

• Storage Efficiency:

  • VVols: Better space utilization via per-object thin provisioning and efficient per-VMDK snapshots; avoids over-provisioning LUNs for future growth.
  • Traditional: May lead to stranded space inside LUNs; capacity planning must consider worst-case growth of shared datastores.

• Performance and QoS:

  • VVols: Policies enable more precise QoS per VM/disk; arrays can schedule resources accordingly.
  • Traditional: QoS often at array level or requires separate LUNs for isolated performance—adds operational overhead.

• Operational Complexity:

  • VVols: Initially more complex to understand; once set up, reduces day-to-day manual tasks through automation.
  • Traditional: Simpler conceptually—admins often comfortable with LUN/datastore model—but more repetitive work (LUN creation, mapping).

Benefits: Side-by-side

When VVols Win (Use Cases)

• Environments with heavy VM churn (frequent create/delete), because VVols avoid constant LUN churn.
• Large virtualization deployments seeking policy-driven automation and per-VM data services.
• Teams that need per-VM replication, snapshot, or QoS without creating many datastores.
• Service providers offering tenant-level isolation, billing, and custom SLAs.
• Workloads requiring fine-grained performance guarantees (databases, latency-sensitive apps).

When Traditional Storage Still Makes Sense

• Small environments or labs where simplicity matters and scale is limited.
• Arrays or ecosystems that lack mature VASA/ VVols support—migrating to VVols may not be feasible.
• Organizations with established processes and tooling tightly coupled to LUN/datastore constructs.
• Scenarios where vendor-specific features or optimizations are only available through traditional integration.

Migration and Compatibility Considerations

• VVols require array support for VASA and may need certain firmware/software versions.
• Migration typically involves converting VMs/datastores to VVols — tools vary by vendor. Plan for data movement, downtime windows, and backup validations.
• Hybrid approaches are common: run VVols for critical or high-churn workloads while keeping stable datastores for legacy systems.
• Test backups/restore and disaster recovery workflows after switching to VVols to ensure policies behave as expected.

Operational Best Practices

• Start with a pilot: migrate a small set of non-critical VMs to validate workflows and monitor performance.
• Define clear storage policies (performance, retention, replication) and map them to SLAs.
• Ensure array firmware and vCenter versions support the necessary VASA capabilities.
• Monitor capacity at the VVols object level and on the array to avoid surprises from thin provisioning.
• Keep documentation and runbooks for failover/restore, since new per-VM constructs change operational procedures.

Cost & Licensing

Costs vary by vendor. VVols themselves are an architecture; storage arrays or management suites may charge for advanced VASA/VVols features, per-VM licensing, or replication packs. Factor in:

• Array software/feature licenses.
• Possible training/administrative transition costs.
• Savings from reduced LUN proliferation and more efficient capacity utilization.

Quick Decision Checklist

• Need per-VM snapshots/replication/QoS? Choose VVols.
• Small lab or unsupported array? Stick with traditional storage.
• Want to reduce manual datastore management and embrace policy-based operations? VVols preferred.
• Must maintain legacy tooling that depends on LUNs? Consider hybrid approach.

Conclusion

Virtual Volumes represent a shift from coarse-grained, volume-centric storage management to a VM-centric, policy-driven model that improves granularity, efficiency, and automation. Traditional LUN/datastore storage remains broadly supported, simpler to adopt, and appropriate where scale or vendor support limits adoption of VVols. The practical choice often becomes a hybrid: adopt VVols where their benefits are clear (high churn, strict SLAs, per-VM services) and retain traditional datastores for legacy or small-scale needs.