How To Test CN2 Japan Link Quality And Generate Visual Reports

This article systematically explains how to assess the quality of CN2 Japanese links from China to Japan, covering objectives, indicators, tools, testing steps, data processing, and visualization, producing graphical reports that are easy for management and engineering teams to understand, balancing one-time diagnostics with long-term trend monitoring needs.

What key metrics are used to assess link quality?

When evaluating link quality, attention should be paid to metrics such as latency (RTT), jitter, packet loss rate, bandwidth throughput, routing variation, and reachability. For backbone lines like CN2, RTT and packet loss directly affect user experience, jitter impacts real-time services (voice/video), bandwidth determines throughput, and route hopping and BGP path stability reflect link health and redundancy. Metrics need to be weighted by service type; for example, games are sensitive to low latency, while file transfers focus more on bandwidth.

Which testing tool is suitable for end-to-end quality inspection?

Common tools include ping, traceroute (or tracert), mtr (combining ping and traceroute), iperf3 (bandwidth), speedtest, smokeping (latency history), and PCAP packet capture (deep analysis). When choosing, consider whether scheduled tasks are supported, whether CSV/JSON can be exported, and whether agents can be deployed on both the test end and the end. For CN2 Japan links, it is recommended to deploy one test host each on the China and Japan sides, using iperf3 for throughput and mtr for continuous latency and packet loss detection.

How to design the test environment and sampling strategies to ensure data reliability?

The test environment must have a fixed network configuration, minimize local network noise (such as Wi-Fi), and ensure that the test device aligns with the business path under test. Sampling strategies include: multi-time (peak/off-peak), multi-day (at least 7 days), and multi-sample (multiple samples per test to take the mean/median). Adds packet frequency detection to real-time services to capture instantaneous jitter; For throughput testing, different numbers of concurrent streams are used to estimate the actual bandwidth boundary. Additionally, record test time, ISP nodes, BGP prefixes, and ports to facilitate problem localization.

How can data collection, cleaning, and storage be achieved for subsequent visualization?

Data collection should use a unified output format (JSON or CSV recommended), with key fields including timestamps, source/destination IP, RTT, packet loss, bandwidth, TTL, hop list, etc. Cleaning steps: Remove missed or overtime samples, eliminate outliers (such as exceeding 3 times the standard deviation), and fill in the missing periods (missing interpolation or annotation). For storage, time-series databases (such as InfluxDB, Prometheus) can save sampling points, and relational databases or object storage can store raw logs and pcap. This allows for both short-term analysis and long-term trend comparison.

Where and how can you generate visual reports to present them to different audiences?

Visualization tool selection depends on audience: engineers prefer Grafana, Kibana, or Jupyter Notebook combined with Plotly to closely examine raw curves and jumps; Management is better suited to exporting PDF/HTML summary charts, such as daily/weekly/monthly delay distribution histograms, packet loss heatmaps, bandwidth utilization line charts, and routing change timelines. Report generation process: Extract data from the time-series database→aggregate by dimension (period/source purpose), generate charts→ and accompany statistical tables (mean, median, 95th percentile) → export templated reports. Automation can be triggered by scripts at scheduled times and distributed via email or BI platforms.

Why combine route tracing and BGP information to analyze the root cause of the problem?

Routing changes or cross-border link detours can significantly affect RTT and packet loss; even if the physical link is normal, quality fluctuations may occur due to BGP policies or ISP relay strategies. By combining traceroute/mtr's per-hop delay and packet loss, as well as BGP routing history (such as RouteViews or local BGP monitoring), you can pinpoint which hop or operator caused the issue. Only by merging performance metrics with path information can one distinguish whether it is a physical link failure, switching congestion, or an upstream ISP policy issue, thereby guiding effective communication and remedial measures.

How often and how many samples are needed for statistically significant assessment?

Frequency and sample size depend on business needs: real-time sensitive business suggests a detection frequency of 1 minute or less; For regular business, you can set it for 5–15 minutes. The sample duration is recommended to cover weekly fluctuations for at least 7 days, ideally 30 days to observe cyclical changes. Each measurement takes at least 3–5 samples per minute, taking an average or median. Each bandwidth test lasts 30–120 seconds and repeats 3 times to reduce jitter effects. Statistically, increasing the sample size lowers the confidence interval, making it easier to judge whether quality has significantly declined.

How can you turn findings into actionable operations and optimization recommendations?

conclusions are extracted from the visualization report and prioritized: if persistent high latency or packet loss occurs, first confirm whether it is a local link, backbone relay, or end-to-end issue, then adopt short-term mitigation (adjusting routing strategies, switching backup routes, limiting burst traffic) and long-term optimization (applying for better BGP strategies, adjusting CDN nodes, procuring higher-grade CN2 or interconnect bandwidth). At the same time, an alarm threshold is established (such as RTT95 exceeding the target or packet loss rate >1%), combined with automated fault ticketing, to ensure timely follow-up and closed-loop issues.