Image Optimization Analytics

Track and analyze the performance of Medianova’s Image Optimization system through real-time GPU utilization and processing metrics, helping you balance workloads and enhance delivery efficiency.

The Analytics dashboard provides visibility into how Medianova’s image optimization engine performs across your CDN resources. It highlights key performance metrics—such as GPU utilization, processing throughput, and optimization efficiency—to help you ensure consistent, fast, and cost-effective image delivery.

Medianova Image Optimization Analytics

Available Metrics

The GPU Usage chart visualizes how system resources are utilized during image processing and WebP conversion.

Metric	Description
Peak Usage	The highest GPU utilization percentage observed during the selected time period.
Average Usage	The mean GPU workload over the reporting window.
Time-Based Trends	Shows fluctuations in GPU activity correlated with request load or time of day.

Use shorter time ranges (e.g., last 1 hour) to analyze real-time optimization performance, or longer ranges (e.g., 24h/7d) for trend analysis.

How to Interpret Data

1. High GPU Usage

• Indicates heavy image transformation or large-scale WebP conversion.

• Expected during peak traffic or mass optimization operations.

• Sustained high usage may suggest scaling additional GPU resources.

2. Low GPU Usage

• Suggests reduced processing demand or effective caching.

• Typically means the system is efficiently serving pre-optimized images.

• If traffic is high but GPU usage remains low, caching and delivery optimizations are working as intended.

3. Sudden Spikes

• Can occur due to unoptimized workflows or irregular API request patterns.

• Investigate spikes to ensure consistent response times and workload distribution.

Optimization Opportunities

Regular monitoring of GPU Analytics helps identify areas where optimization can further improve performance:

• Balance Workloads – Distribute image processing evenly across nodes to prevent bottlenecks.

• Improve Caching Efficiency – Minimize repetitive optimization tasks for identical images.

• Scale Resources – Allocate or deallocate GPU nodes dynamically based on load.

• Refine Parameters – Adjust image resizing, compression, or WebP settings for cost-performance balance.

Performance data can also inform future infrastructure planning, ensuring you scale only when necessary.

Why Analytics Matters

Monitoring GPU Usage ensures that Medianova’s Image Optimization system operates at peak performance, delivering measurable business benefits:

• Faster Image Delivery – Reduce latency through balanced edge processing.

• Cost Efficiency – Avoid over-provisioning GPU resources.

• Reliability and Stability – Prevent system slowdowns during peak image workloads.

• Data-Driven Decisions – Optimize configuration based on real metrics, not assumptions.

Efficient resource utilization directly contributes to smoother operations, improved user experience, and lower operational costs.