How to evaluate the cache hit rate performance of Taiwan’s CDN CN2 in different regions

This article is aimed at technical and operations personnel responsible for website/application acceleration, GEO optimization, and CDN selection, introducing a systematic approach to evaluating the cache hit rate of Taiwan’s CDN CN2 in different regions. It covers indicator definitions, data collection, and key points for test design and analysis, helping to quantify the acceleration effects of CDN in various locations.

Definition and Prioritization of Key Metrics

Key metrics must be defined before evaluation: Cache hit rate, origin fetch rate, edge latency, time to first byte (TTFB), and bandwidth utilization. Cache hit rate is crucial, but it should be considered in conjunction with origin fetch rate and latency to avoid letting a single metric mislead optimization efforts.

Data Collection Sources and Methods

It is recommended to collect edge logs, origin-pull logs, and synthesized probing data simultaneously. Edge logs reflect actual hits, while origin-pull logs can verify the reasons for misses ； Synthetic probing is used to cover geographical and temporal blind spots. When combined, the three can form a complete dataset and provide a basis for temporal analysis.

Key Points of Synthetic Test Design

Synthetic testing needs to cover major user regions, different operators, and time periods, and design both static and dynamic resource requests. Use multi-point probes to simulate real access paths, and control request frequency and cache headers (such as Cache-Control, Expires) to evaluate the impact of policies.

Real Traffic Log Analysis Process

When analyzing edge access logs, the request time, URI, response status, Cache status fields, and client ASN/geographic information should be extracted. Group and count the hit rate by region, operator, and URI type, and cross-check it with the origin-pull logs to rule out false positives.

The impact of differences between nodes and routes

Taiwan’s CDN exhibits varying caching performance across different regions or service providers due to differences in node coverage and routing strategies. During analysis, requests need to be mapped to specific edge nodes or POPs to determine whether low hit rates are caused by node capacity, synchronization latency, or routing backhaul.

Suggestions for hit rate calculation and visualization

It is recommended to calculate the hit rate by layering it by region/operator/resource type: Hits ÷ Total requests. Use heatmaps, time series, and box plots to show differences, and set baseline and SLA thresholds to facilitate long-term trend tracking and anomaly detection.

Common influencing factors and troubleshooting approaches

Common factors affecting cache hit rates include improper cache control headers, content segmentation and query parameters, content validity across different regions, and origin-pull strategies. During troubleshooting, prioritize checking HTTP headers, URI parameter normalization, and resource versioning strategies.

Continuous Monitoring and Alerting Policies

Establish real-time monitoring and alerts: An alert is triggered when the hit rate of a certain region or resource type drops below a threshold, or when there is an abnormal increase in traffic originating from those sources. Combined with automated reporting and drill-down tools, quickly locate nodes, time windows, and request samples for intervention.

Optimization Methods and Practical Recommendations

Optimization can start with adjusting cache policies, compressing and merging resources, increasing edge cache capacity or nodes, improving origin-pull strategies (such as hierarchical origin-pulling), and intelligently routing content that is not cached. Verify the optimization effects in stages based on the analysis results.

Summary and Implementation Recommendations

Evaluating the cache hit rate of Taiwan’s CDN CN2 requires multi-source data, combined with synthetic and real traffic testing, node mapping, and temporal analysis. It is recommended to establish continuous monitoring, hierarchical metrics, and automatic alert mechanisms, and to verify and optimize the results through small-scale iterations to ensure that each region steadily achieves its acceleration goals.