Storage Protocols
Storage Protocol Comparison
| Protocol | Interface | Max Throughput | Latency | Best For |
|---|---|---|---|---|
| NVMe (PCIe 5.0) | Direct PCIe | 14 GB/s per drive | ~70 microseconds | Local high-performance storage |
| NVMe-oF (RoCEv2) | Ethernet (100/200 GbE) | 100+ GB/s aggregate | ~100 microseconds | Shared NVMe over network |
| NVMe-oF (FC-NVMe) | Fibre Channel 32/64G | 64+ GB/s aggregate | ~100 microseconds | FC environments migrating to NVMe |
| iSCSI | Ethernet (10/25/100 GbE) | Network-limited | ~500 microseconds | Cost-effective block storage |
| Fibre Channel (SCSI) | FC 16/32G | 32+ GB/s aggregate | ~500 microseconds | Legacy SAN environments |
| NFS v4.1/v4.2 | Ethernet | Network-limited | ~1 millisecond | Shared file storage, AI datasets |
| SMB 3.x | Ethernet | Network-limited | ~1 millisecond | Windows file sharing |
NVMe over Fabrics
NVMe over Fabrics (NVMe-oF) extends the NVMe protocol over a network fabric, enabling shared NVMe storage with latency that approaches local NVMe performance. NVMe-oF over RoCEv2 (RDMA over Converged Ethernet) delivers ~100 microsecond latency over 100 GbE networks, compared to ~500 microseconds for iSCSI over the same network.
NVMe-oF is the right protocol for AI training workloads that require shared high-performance storage: enabling multiple GPU servers to access the same NVMe storage pool simultaneously, with the throughput and latency that AI workloads require.
NVMe-oF requires lossless networking
Object Storage Architecture
Object storage is the right architecture for AI training datasets, backup targets, and large-scale unstructured data. It provides horizontal scalability (capacity grows by adding nodes), high sequential throughput (multiple nodes serve data in parallel), and an S3-compatible API that most AI frameworks support natively.
Scalability
Object storage scales horizontally, adding nodes adds both capacity and throughput. There is no practical upper limit on capacity.
Throughput
Multiple nodes serve data in parallel, aggregate throughput scales with node count. A 10-node cluster delivers 10x the throughput of a single node.
Durability
Erasure coding provides data durability without the overhead of full replication. 4+2 erasure coding provides 99.999999999% (11 nines) durability.
API compatibility
S3-compatible API is supported by most AI frameworks (PyTorch, TensorFlow), data pipeline tools, and backup software.
Data Protection Schemes
RAID and Erasure Coding Comparison
| Scheme | Drive Failures Tolerated | Overhead | Performance Impact | Best For |
|---|---|---|---|---|
| RAID 1 (Mirror) | 1 | 100% | Read: 2x, Write: 1x | Boot drives, small critical datasets |
| RAID 5 | 1 | 1/N | Write penalty | Legacy, inadequate for large drives |
| RAID 6 | 2 | 2/N | Write penalty | Standard for enterprise arrays |
| RAID 10 | 1 per mirror pair | 100% | Excellent | High-performance databases |
| Erasure Coding (4+2) | 2 | 50% | Moderate | Object storage, large-scale |
| Erasure Coding (8+3) | 3 | 37.5% | Moderate | Large-scale object storage |
RAID 5 is inadequate for modern large-capacity drives, the probability of a second drive failure during RAID 5 rebuild is significant. RAID 6 (or equivalent erasure coding) is the minimum standard for enterprise storage.
Replication Architecture
Synchronous Replication
RPO: Zero (no data loss)
RTO: Minutes (automatic failover)
Cost: High (requires low-latency link)
Use: Mission-critical workloads requiring zero data loss
Asynchronous Replication
RPO: Minutes to hours (depends on replication interval)
RTO: Minutes to hours
Cost: Lower (tolerates higher latency)
Use: Business-critical workloads tolerating some data loss
Snapshot-Based Replication
RPO: Hours (depends on snapshot frequency)
RTO: Hours
Cost: Low
Use: General workloads, development environments
Continuous Data Protection
RPO: Seconds
RTO: Minutes
Cost: Medium
Use: Workloads requiring near-zero RPO without synchronous replication cost
Storage Management
Modern storage management platforms provide unified visibility across all storage tiers: block, file, and object. Key capabilities include capacity trending and forecasting, performance monitoring and alerting, automated tiering, and API-based provisioning for infrastructure-as-code workflows.
Storage management that is not integrated with the broader infrastructure management platform creates visibility gaps: capacity exhaustion events that are not detected until applications fail, performance degradation that is not correlated with storage metrics, and provisioning workflows that require manual intervention.