CAB-IND=: What Is Its Function in Cisco Indus
Decoding the CAB-IND= The CAB-IND=...
The A9KV-V2-DC-E= is a high-density, multi-rate line card engineered for Cisco’s ASR 9000 Series routers, specifically targeting hyperscale data center interconnect (DCI) and cloud exchange deployments. Designed as an evolution of the V1 model, this module supports mixed-speed port configurations (10G/25G/40G/100G) and integrates advanced telemetry features to meet the demands of dynamic, high-throughput environments like AI fabric backbones or multi-cloud gateways.
Feature | A9KV-V2-DC-E= | A9KV-V1-DC-E= |
---|---|---|
Port Flexibility | 10G/25G/40G/100G | 10G/40G/100G |
Max Buffer per Port | 64 MB | 32 MB |
Latency (Cut-Through) | <750 ns | <1.2 µs |
Telemetry Granularity | Per-flow INT | Per-port SNMP |
Encryption Overhead | 0% | 5% at 100G |
This iteration targets AI/ML workload scaling and hyperscaler DCI where microburst absorption and encryption transparency are non-negotiable.
Yes, but it requires IOS XR 7.10.1+ and a minimum 3000W power shelf for full performance. Legacy chassis (pre-2019) may need airflow retrofit kits.
Yes, via standardized EVPN-VXLAN or SR-MPLS protocols, but features like INT require [“A9KV-V2-DC-E=” link to (https://itmall.sale/product-category/cisco/) optics for end-to-end visibility.
The Chiplet ASIC employs Dynamic Load Balancing (DLB), redistributing traffic across underutilized paths to prevent congestion.
Operates optimally at 10°C to 35°C with side-to-side airflow kits mandatory in enclosed racks.
The A9KV-V2-DC-E= isn’t just a hardware refresh—it’s a capex multiplier for data centers struggling with east-west traffic explosions. While its upfront cost is 30% higher than the V1, the opex savings from encryption offload and adaptive power can amortize costs within 18 months in 100G-heavy setups. For teams eyeing liquid-cooled data centers, its thermal resilience (up to 45°C with auxiliary cooling) future-proofs investments. Pair it with Cisco Nexus Dashboard to automate fabric provisioning and avoid buffer bloat in AI training clusters.