CBW240AC-E: How Does It Enhance High-Capacity
Overview of the Cisco CBW240AC-E The Cisco CBW240...
The DS-C9124V-8PIVK9 is a port activation license for Cisco MDS 9124V multilayer fabric switches, designed to scale Fibre Channel SAN infrastructures from 8 to 24 ports through field-upgradable licensing. This license supports mixed-speed configurations (4/8/16/32G) and integrates with Cisco NX-OS SAN-OS software to reduce cabling complexity by 50% in high-density storage environments.
1. Dynamic Port Activation
2. Power & Thermal Efficiency
3. Security & Compliance
Metric | MDS 9148 (Fixed 48-Port) | DS-C9124V-8PIVK9 Activated |
---|---|---|
Initial CAPEX | $57,000+ | 40% reduction with base 8 ports |
Mixed-Speed Support | 16G/32G only | 4G to 32G auto-negotiation |
Encryption Throughput | 2.4M IOPS | 4.8M IOPS with MACsec-256 |
Energy Efficiency | 1.8W/Gbps | 0.9W/Gbps optimized ASICs |
Q: How to integrate with Cisco UCS C4800 ML servers?
A: The license enables Inter-VSAN Routing (IVR) through NX-OS 9.3+, synchronizing QoS policies between MDS 9124V switches and UCS domains via Cisco DCNM. For VMware vSAN deployments, it maintains <2ms latency through FCoE gateway functionality.
Q: What maintenance optimizes performance?
The DS-C9124V-8PIVK9 redefines pay-as-you-grow SAN economics, enabling 23μs consistent latency for AI/ML training clusters accessing distributed NVMe arrays. Its adaptive zoning automatically quarantines misconfigured storage nodes, critical for HIPAA-compliant medical imaging archives. Enterprises transitioning to 32G FC infrastructure should prioritize this model for eliminating hardware refresh cycles through non-disruptive speed upgrades. Procurement validation should be coordinated through the Cisco hardware marketplace to ensure compatibility with NetApp FAS/E-Series or Dell PowerStore arrays.
Cisco’s software-defined SAN evolution achieves full expression here—port capacity becomes a consumable resource rather than fixed hardware. The license’s 55°C ambient tolerance and predictive failure analysis via DCNM machine learning models make it indispensable for edge compute nodes in automated manufacturing.