NetGraph Guide — How to Read and Interpret Network GraphsNetwork graphs are essential tools for anyone responsible for maintaining performance, reliability, and security of networks. “NetGraph” is a generic name for visualizations that show network metrics over time or topology relationships between devices. This guide explains common NetGraph types, how to read them, what they reveal (and hide), and practical workflows for diagnosing issues and communicating findings.
Why network graphs matter
A good NetGraph turns raw telemetry into actionable insight. Rather than sifting through logs or CLI outputs, engineers use graphs to:
- spot trends (capacity growth, recurring spikes),
- detect anomalies (sudden latency or packet loss),
- correlate events across layers (application latency vs. link utilization),
- communicate status to stakeholders.
Types of NetGraphs and what they show
Time-series metric graphs
These plot one or more metrics against time (e.g., throughput, packets/sec, latency, error rate).
- Typical axes: x = time, y = metric value.
- Common visual forms: line charts, area charts, stacked area charts.
What to look for:
- Baseline and seasonality: normal traffic patterns repeating daily/weekly.
- Spikes and drops: short-lived events vs. sustained shifts.
- Correlation across metrics: CPU rise with throughput, latency rising with packet loss.
- Outliers: sudden aberrant values that may signal measurement error or real incidents.
Topology/graph maps
Show devices (nodes) and their links (edges). Often color-coded or sized by metric (e.g., link utilization).
- Useful for: spotting chokepoints, visualizing redundancy, understanding path dependencies.
What to look for:
- Single points of failure (high-degree nodes with heavy traffic).
- Asymmetrical traffic patterns (one direction saturated).
- Unexpected links or devices indicating misconfiguration or security issues.
Heatmaps
Display metric magnitude across two dimensions (time vs. hosts, port vs. application).
- Useful for quickly spotting hot spots and patterns across many entities.
What to look for:
- Persistent hot rows/columns (problematic host or service).
- Diurnal patterns visible as stripes.
- Sparse vs. dense activity areas.
Distribution plots (histograms, box plots, CDFs)
Show how values are distributed rather than how they change over time.
- Useful for: understanding typical vs. tail behavior (e.g., 95th-percentile latency).
What to look for:
- Skewed distributions (long tail = intermittent poor performance).
- Variance and outliers; median vs. mean differences.
Sankey/flow diagrams
Show volume flow between components (e.g., requests between services).
- Useful for capacity planning and understanding traffic composition.
What to look for:
- Largest flows and their origins/destinations.
- Unexpected routing or traffic leaks.
Reading NetGraphs: step-by-step approach
- Understand the question
- Are you troubleshooting a user complaint (latency), assessing capacity, or scanning for security anomalies?
- Pick the right graph type
- Use time-series for incidents, topology for structural issues, heatmaps for many hosts.
- Check axes and units
- Confirm time range, aggregation interval (1s vs. 1m vs. 1h), and units (bps vs. Bps).
- Establish the baseline
- Compare the observed period to a “normal” period (same day last week, typical business hours).
- Identify deviations
- Note magnitude, duration, and which metrics/devices are affected.
- Correlate across graphs
- Bring in CPU, interface errors, routing changes, and application logs to build a causal chain.
- Drill down and validate
- Query raw data or packet captures to confirm the graph’s implication and rule out visualization artifacts.
- Document and act
- Record the finding, root cause, and remediation steps; update runbooks if needed.
Common patterns and their interpretations
- Rising throughput with stable latency: generally healthy scaling; watch for future saturation.
- Rising latency with increasing packet loss: network congestion or faulty hardware.
- Sudden drop to zero throughput: link down, routing flap, or monitoring failure.
- CPU/memory spike on a router with correctable errors increasing: software bug or overload.
- Asymmetric traffic between peers: routing policy or link capacity differences.
- Persistent high 95th-percentile latency but low median: intermittent congestion affecting tail users.
Pitfalls and misleading signals
- Aggregation hides short spikes: long aggregation windows (e.g., 1h) smooth brief but important events.
- Missing context about sampling/collection: dropped metrics or polling gaps can appear as outages.
- Visualization defaults can mislead: stacked areas vs. lines change perception of contribution.
- Misinterpreting correlation as causation: two metrics rising together may be symptoms of a third cause.
- Unit mismatches: confusing bits and bytes leads to wrong capacity conclusions.
Practical diagnostics examples
Example 1 — Intermittent high latency
- Time-series: latency spikes every 10 minutes.
- Correlate: interface error counters show bursts, and CPU on a firewall spikes simultaneously.
- Likely cause: intermittent hardware fault or bufferbloat on the firewall; capture packets to check retransmissions.
Example 2 — Gradual throughput growth causing saturation
- Time-series: upward trend over months.
- Heatmap: new service shows increasing rows of activity.
- Action: plan capacity upgrade, or implement traffic shaping and prioritize critical flows.
Example 3 — Sudden outage for a service
- Topology map: server becomes isolated; ARP or routing entries missing.
- Distribution/Capture: no TCP handshakes arriving; BGP logs show route withdraw.
- Action: check routing policies, check device logs, failover if redundant paths exist.
Best practices for creating effective NetGraphs
- Choose meaningful defaults: reasonable time ranges and aggregation intervals for your environment.
- Label axes and units clearly.
- Use consistent color semantics (e.g., red for error conditions).
- Provide interactive drill-downs from summary to raw data.
- Annotate graphs with deployment/maintenance events to avoid confusion.
- Keep dashboards focused: one main question per chart.
- Store raw, high-resolution data for a limited time and downsample older data with preserved summaries (e.g., histograms).
Communicating findings
- Start with the observable facts: what changed, when, and the measured impact (e.g., 95th-percentile latency rose from 40 ms to 600 ms at 14:12 UTC).
- Provide correlation evidence (graphs + timestamps).
- State probable cause and confidence level.
- Recommend steps (rollback, failover, capacity change, ticket escalation).
- Attach or link to the exact graphs and queries used.
Quick reference: checklist before reporting an incident
- Time range appropriate and includes pre/post-event data
- Aggregation interval small enough to show relevant spikes
- Units and axes verified
- Correlated graphs examined (CPU, interface errors, routing, application logs)
- Raw evidence (pcap, traces) collected if needed
- Annotated timeline of events and actions
Network graphs condense big datasets into human-readable visuals. The skill is not only reading shapes and colors but asking the right follow-up questions, correlating multiple data sources, and validating hypotheses. Use the steps and patterns above to make NetGraph a reliable tool for troubleshooting, planning, and communicating network health.
Leave a Reply