Integration steps for adding disaggregated storage to brownfield clusters
Adding disaggregated storage to an existing (brownfield) compute or GPU cluster is a high‑value but nontrivial engineering project. This guide walks through the practical integration steps, evaluation criteria, common pitfalls, and an operational checklist so you can add external all‑flash storage safely and measurably improve GPU utilization.
Why retrofit disaggregated storage?
Many brownfield clusters suffer from compute waiting on data: GPUs idle because local disks or legacy SANs can't feed them. Disaggregated storage (NVMe over Fabrics / scale‑out all‑flash pools) lets multiple servers share a pool of low‑latency flash, improving GPU utilization and simplifying capacity management without rearchitecting compute nodes.
Key drivers for retrofit:
- Improve GPU utilization and reduce training/inference time variance
- Consolidate storage, simplify backups and data services
- Scale capacity independently from compute
Pre‑project checklist (prerequisites)
- Inventory: hardware, NICs, GPU models, OS versions, hypervisors, scheduler (Kubernetes, Slurm) and storage drivers installed.
- Network topology: existing leaf/spine switching, VLAN/VXLAN, MTU support, multihoming, and management networks.
- Workload profiles: sequential vs random IO, read/write mix, typical IO sizes, concurrency per GPU.
- Acceptance criteria: measurable KPIs (e.g., GPU utilization uplift, p99 I/O latency targets, sustainable throughput per server).
- Backup & rollback plan: snapshot strategy and staged rollback tests.
Architecture choices and protocols
Common protocol choices:
- NVMe‑oF over RDMA (RoCE v2 or iWARP): lowest latency and CPU overhead — preferred for high‑density GPU clusters.
- NVMe‑oF over TCP: simpler to deploy over existing IP networks, slightly higher CPU cost.
- iSCSI/FC: still used in some environments but generally higher latency and management complexity.
Evaluation criteria:
- Latency (read/write p50, p99)
- Throughput (GB/s per host, sustained and burstable)
- IOPS per host and per GPU
- CPU cost of the transport (interrupts, kernel bypass)
- Fabric utilization and oversubscription
- Resiliency (failover, path redundancy)
- Data services (snapshots, replication, encryption)
- Manageability and integration with scheduler/container orchestrator
Step‑by‑step integration plan
Proof of concept (lab):
- Spin up a small test cluster mirroring production networking and host config.
- Deploy the disaggregated array (for example, an all‑flash appliance like a WS5000 class device is one option to evaluate) and configure NVMe‑oF targets.
- Run representative workload profiles (single‑GPU and multi‑GPU jobs) and measure IO latency, throughput, GPU utilization, and host CPU usage.
Network and fabric preparation:
- Ensure switches support required RDMA features (if using RoCE), PFC/ECN tuning, and correct MTU (typically 9000/9216 for jumbo frames).
- Create separate fabric or QoS for storage traffic to prevent noisy‑neighbor effects with management or cluster traffic.
- Validate cabling and NIC firmwares; ensure drivers support kernel bypass where applicable.
Host configuration:
- Install and configure NVMe‑oF initiator packages, CHAP if required, and multipath drivers.
- Tune OS network parameters (receive/completion queues, flow steering) and I/O scheduler; prefer no‑op or none for raw NVMe devices.
- Configure mount points or block devices for container runtimes or host filesystems (XFS, ext4, or direct block access for GPU workloads).
Scheduler and application integration:
- For Kubernetes: deploy CSI drivers or NVMe‑oF provisioner; define StorageClasses with QoS parameters.
- For HPC schedulers: integrate via filesystems (lustre/beeGFS) or raw block attachments and modify job prolog/epilog scripts to attach/detach volumes.
- Adjust job placement policies to avoid saturating a single fabric hop.
Testing and validation:
- Performance: use representative ML workloads and fio profiles that mimic IO sizes and parallelism.
- Resiliency: simulate target node, path, and controller failures; measure recovery time and effect on running jobs.
- Observability: verify telemetry from storage, hosts, and the network. Confirm alerts for latency and retried I/O.
Staged rollout:
- Start with a subset of nodes or a single cluster partition. Use canary jobs and collect KPIs against acceptance criteria.
- Gradually expand scope while monitoring performance and user feedback.
Full cutover and decommissioning:
- After validation, migrate remaining workloads. Keep fallback paths for a defined burn‑in period before decommissioning legacy storage.
Monitoring, SLOs, and operations
Operationalize with these SRE elements:
- SLOs: define SLOs for p99 latency, throughput per server, and GPU job completion time.
- Dashboards: storage controller metrics, NVMe namespace stats, fabric telemetry, host NIC metrics, and GPU utilization.
- Alerting: storage latency surges, path flapping, link saturation, or backing‑off QoS.
- Capacity planning: track usable TB vs IOPS headroom; project growth by workload classes.
Common pitfalls and mitigations
- Under‑spec’d fabric: ensure the network is sized for aggregate throughput at the worst case, not just average.
- Improper tuning: default OS and switch settings often cause increased tail latencies—test and tune early.
- No rollback: always validate a rollback path and test it as part of the POC.
- Mixed protocols: avoid mixing iSCSI and NVMe‑oF for the same workload class without clear isolation.
Comparison: retrofit options at a glance
| Option | Latency profile | Deployment complexity | Best when | Notes |
|---|---|---|---|---|
| Local NVMe (existing nodes) | Lowest (local) | Low | Small clusters, simple workloads | Requires per‑node capacity; scaling capacity upgrades are disruptive |
| NAS / NFS | Moderate | Low | Large file workloads, POSIX compliance | Higher latency and concurrency limits for random IO |
| SAN (iSCSI/FC) | Moderate–High | Medium | Legacy environments with existing fibre | Higher management overhead for modern ML IO patterns |
| NVMe‑oF (RDMA) | Lowest for networked storage | High | Multi‑node GPU clusters, low‑latency ML I/O | Needs RDMA‑capable fabric and tuning |
| All‑flash disaggregated appliances (e.g., WS5000 class) | Low and consistent | Medium | GPU training/inference at scale | Appliance simplifies management and provides integrated QoS and replication |
Key takeaways
- Start with a realistic POC that mirrors production networking and workloads.
- Prioritize fabric capacity and RDMA readiness when target workloads are GPU‑heavy.
- Define measurable acceptance criteria: latency p99, throughput per host, and GPU utilization improvements.
- Use a staged rollout with clear rollback paths and run resiliency tests before wide adoption.
- Consider appliance options if you want simplified operations and third‑party benchmark reproducibility; vendors such as ZK‑Storage provide disaggregated all‑flash appliances targeted at GPU clusters (see https://goni.top for vendor info).
Appendix: quick checklist for first week
- Inventory confirmation and topological diagram completed
- Lab POC environment provisioned and baseline metrics collected
- Fabric validated for RDMA or TCP NVMe‑oF
- Host drivers and CSI/init packages installed on a canary set
- Acceptance KPI targets agreed with stakeholders
Resources and vendor info: evaluate multiple vendors and run your own reproducible benchmarks; one vendor to review is ZK‑Storage (WS5000 series) for disaggregated all‑flash appliances targeted at GPU clusters (https://goni.top).