GSNOC
Detection Engine

PDD Anomaly Detection

Post-dial delay varies naturally by time of day, destination, and carrier load. GSNOC builds hour-of-day and destination-aware baselines so PDD anomalies represent real problems — not expected variation.

The PDD Monitoring Problem

High post-dial delay degrades user experience — callers hear silence before ring-back and often hang up. But PDD varies significantly by time of day, destination, and network load, making simple thresholds generate constant noise.

  • Long PDD causing callers to abandon calls before connection, degrading effective ASR and customer experience
  • Time-of-day PDD variation generating false alerts during legitimate peak periods
  • Destination-specific PDD differences making universal thresholds inappropriate for international routing
  • Carrier-specific PDD degradation going undetected when mixed with routes that have naturally higher PDD

How PDD Anomaly Detection Works

Eight analysis methods combine hour-of-day bucketing, destination awareness, and trend analysis to separate signal from noise.

Hour-of-Day Baselines

Separate PDD baselines are maintained for each hour of the day per carrier and route, so peak-hour PDD increases don't trigger off-peak alert thresholds.

Destination-Aware

PDD baselines are maintained per destination prefix so that international routes with inherently higher PDD are not compared against domestic baselines.

Spike Detection

Sudden PDD spikes that exceed the hour-adjusted baseline by a statistical threshold flag potential carrier congestion or routing issues.

Sustained Elevation

PDD that remains elevated over multiple evaluation periods — even below spike thresholds — is flagged as a sustained degradation event.

Carrier Comparison

Compares PDD across multiple carriers serving the same destination to identify which carrier is the source of observed delays.

Routing Change Detection

Identifies step changes in PDD that correlate with routing configuration changes — distinguishing intentional changes from accidental degradation.

Congestion Pattern Analysis

Identifies PDD patterns consistent with carrier congestion — elevated delay during specific time windows that repeat across days.

Trend Analysis

Tracks PDD trends over days and weeks to identify carriers whose PDD is slowly worsening before it becomes a visible problem.

gsnoc.local
Screenshot: PDD Anomaly Detection screenshot

Why GSNOC PDD Anomaly Detection Is Different

  • Hour-of-day bucketed baselines eliminate the constant false positive problem of simple PDD thresholds
  • Destination-aware baselines correctly handle international routes that have inherently higher PDD than domestic
  • Carrier comparison identifies which carrier is responsible when multiple carriers serve the same destination

Key Metrics

Current PDD

Average post-dial delay measured in the current evaluation window in milliseconds

Baseline PDD

Expected PDD for this hour, destination, and carrier based on rolling historical data

Deviation (ms)

How many milliseconds the current PDD exceeds the hour-adjusted baseline

Percentile

Where current PDD sits in the historical distribution — 95th percentile and above indicates a significant anomaly

Related Detection Engines

See it in action

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