I've been conducting a performance and cost analysis for our multi-cloud firewall deployment and have encountered a consistent pattern of unexpectedly high memory utilization on the Barracuda CloudGen Firewall virtual appliances (specifically, the VF series on Azure). This isn't a transient spike but a sustained baseline consumption that far exceeds the documented expectations for our given throughput and connection specifications.
My initial deployment used a **VF50L** for a development/test environment with minimal traffic. Despite an average throughput of less than 10 Mbps and under 1,000 concurrent connections, the appliance consistently reports 75-85% memory usage as shown via the CudaCare portal and the internal `boxinfo` command. A typical `boxinfo` snippet shows:
```
Memory Usage:
Total: 7982 MB
Used: 6654 MB (83.36%)
Buffers: 124 MB
Cached: 584 MB
Free: 620 MB
```
This baseline consumption seems disproportionately high. Scaling up to a **VF100** in production, which handles approximately 450 Mbps and 45,000 concurrent connections, we observe memory usage persistently between 82-90%. This forces us into a larger instance size than our pure throughput/connection table requirements would dictate, directly impacting our monthly committed spend.
**Key observations and initial analysis:**
* The high memory footprint appears immediately upon deployment, even before traffic configuration, suggesting a significant static overhead.
* The "Cached" memory metric is relatively low, indicating this is not simply Linux disk caching at play.
* This pattern is consistent across both Azure (Dv3-series) and AWS (m5-series) deployments, ruling out a hypervisor-specific issue.
* The memory consumption reduces OpEx flexibility, as we cannot rightsize based on network metrics alone and must over-provision to maintain a safe operating margin.
I am attempting to model the true total cost of ownership (TCO), and this memory behavior is a significant variable. Has anyone else performed a similar resource utilization audit and observed comparable figures? More specifically:
* Are these sustained 80%+ memory baselines considered normal operation by Barracuda?
* Has anyone successfully correlated specific features (e.g., Advanced Threat Protection, URL Filtering, SSL Inspection) to discrete increments in static memory allocation?
* What is the community's observed "headroom" threshold before considering an upgrade? Is operating consistently above 85% considered stable?
Any detailed metrics or operational experiences would be invaluable for refining our cost models and capacity planning assumptions.