What are the performance specifications for a 17kVA rack with 2x200GB ethernet?

Understanding rack-level performance specifications is crucial for datacenter planning, power budgeting, and performance forecasting for large-scale MinIO deployments.

This addresses critical planning needs:

• Datacenter capacity planning
• Power and cooling requirements
• Network architecture design
• Performance expectations at scale

Answer

A standard 12-node MinIO “DataPod” configuration delivers exceptional performance within a 17kVA power envelope.

DataPod Configuration

Hardware Specifications:

• 12 × 2U servers per rack
• 2 × 200 GbE network per server
• 36 NVMe SSDs per server (QLC or TLC)
• 432 total drives per rack

Performance Specifications

Key Principle: The system will saturate the network before running out of I/O bandwidth.

Throughput at 50% Network Utilization for Client Traffic:

Why 50% Network Utilization?

• Accounts for protocol overhead
• Leaves headroom for replication
• Ensures stable operation under load
• Accommodates burst traffic

Capacity Specifications

Usable Storage:

• 5.6 PB usable with EC 12+4
• Based on typical drive capacities
• 75% storage efficiency
• After all overhead calculations

Raw Capacity Calculation:

432 drives × 15.36 TB average = 6.6 PB raw
EC 12+4 efficiency (75%) = 5.0 PB usable
With larger drives (30TB QLC) = 9.7 PB usable

Power Specifications

Power Consumption:

• < 18 kW typical load
• Within 17 kVA envelope with 20% headroom
• Includes all components (servers, drives, networking)
• Allows for power supply inefficiencies

Power Breakdown:

Per Server:
- Base system: 800W
- 36 SSDs: 360W (10W each)
- 2×200 GbE NICs: 100W
- Cooling fans: 240W
Total: ~1,500W per server

Rack Total:
12 servers × 1,500W = 18,000W (18 kW)

Network Architecture

Aggregate Network Capacity:

12 nodes × 2 × 200 GbE = 4,800 Gbps
= 600 GB/s theoretical maximum
= 300 GB/s practical throughput (50% utilization)

Network Distribution:

• Each node: 400 Gbps capacity
• Per node throughput: 25 GB/s practical
• Full bisection bandwidth recommended
• Dual-path for redundancy

Performance per Watt

Efficiency Metrics:

Real-World Performance Expectations

Small Objects (< 1MB):

• 3-5 million ops/sec
• Network packet-rate limited
• CPU becomes factor at scale

Large Objects (> 10MB):

• 300 GiB/s reads
• 200 GiB/s writes
• Network bandwidth limited
• Linear scaling with nodes

Mixed Workload:

• 250 GiB/s aggregate
• 1-2 million ops/sec
• Balanced resource utilization

Scaling Considerations

Multi-Rack Scaling:

1 Rack (12 nodes): 300 GiB/s read, 5.6 PB
2 Racks (24 nodes): 600 GiB/s read, 11.2 PB
4 Racks (48 nodes): 1.2 TiB/s read, 22.4 PB

Linear Scaling Factors:

• Performance scales linearly with racks
• Power scales linearly (18 kW per rack)
• Management remains single-cluster simple

Configuration Best Practices

1. Network Design:

   • Dual 200 GbE for redundancy
   • Separate front/back networks
   • Non-blocking spine-leaf topology

2. Power Planning:

   • Plan for 20 kW per rack actual
   • Include cooling overhead
   • N+1 power redundancy

3. Drive Selection:

   • QLC for capacity-optimized
   • TLC for performance-optimized
   • Match to workload requirements

Comparison with Traditional Storage

Key Advantages

The DataPod architecture demonstrates:

• Network-optimized design - I/O never the bottleneck
• Power efficiency - More performance per watt
• Density - Maximum capacity per rack
• Simplicity - Single-cluster management
• Scalability - Linear performance growth

This configuration represents the optimal balance of performance, capacity, and power efficiency for modern datacenter deployments, particularly suited for AI/ML workloads, content delivery, and large-scale object storage requirements.