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Observability Patterns to Detect Provider-Scale Network Failures Quickly

UUnknown

2026-02-18

10 min read

Design probes and synthetic transactions that detect Cloudflare/AWS provider-wide outages in minutes — with aggregation, SLO-driven alerts, and runbooks.

Catch provider-scale network failures before customers complain

When Cloudflare or AWS has a partial outage, user reports are too slow. You need probes, synthetic transactions, and alerting designed to detect provider-wide incidents in minutes — not after a Twitter thread explodes. This guide shows practical patterns, code examples, and runbook steps you can implement in 2026 to detect provider-scale problems faster and reduce mean time to detect (MTTD).

Why this matters in 2026

Late 2025 and early 2026 saw a string of high-impact incidents where major CDNs and cloud providers influenced large parts of the Internet simultaneously. Increased edge complexity, aggressive global software rollouts, and more dynamic routing (including frequent RPKI/ROA updates and multi-homed anycast changes) have raised the bar: single-point public status pages and passive user reports are no longer reliable early signals.

Quick takeaway: You must move from ad-hoc health checks to an observability strategy that blends globally distributed probes, realistic synthetic transactions, multi-dimensional aggregation, and SLO-driven alerts.

High-level observability pattern

Design your detection system around five components:

Distributed probes — many small vantage points across ISPs and regions.
Realistic synthetic transactions — end-to-end flows that exercise CDN, DNS, TLS, and origin paths.
Telemetry aggregation — roll up failures by provider, ASN, region, and POP.
Anomaly detection & alerting — SLO-aware, provider-correlated alerts with suppression rules.
Runbooks & automation — clear steps plus automated mitigations for fast MTTR.

1) Design distributed probes for provider-scale visibility

Single-region or single-ISP probes miss provider-wide patterns. Aim for diversity along three dimensions:

Geographic diversity: global coverage across regions and major metros.
Network diversity: different ISPs, mobile vs fixed, enterprise networks, cloud provider ASNs.
Edge diversity: probes from ISP resolvers, public recursive resolvers (1.1.1.1, 8.8.8.8, DoH), and vantage points behind major CDNs.

Where to run probes

Managed synthetic platforms (ThousandEyes, Catchpoint, Uptrends) for broad coverage.
Lightweight agents on multi-cloud FaaS (Cloudflare Workers, AWS Lambda@Edge, GCP Cloud Functions) to create inexpensive edge probes.
Open measurement networks (RIPE Atlas, M-Lab) for independent checks and BGP telemetry.
On-prem and branch probes to detect regional ISP-specific issues.

Probe cadence and cost trade-offs

High-frequency probes detect fast-moving provider incidents faster but cost more. Use tiered cadences:

Critical path checks: 30s–1m cadence (login APIs, payment flows) from 20+ diverse vantage points.
Network & DNS probes: 1–5m cadence from 50–200 vantage points worldwide.
Broad coverage checks: 5–15m cadence for target discovery and passive telemetry.

2) Build synthetic transactions that exercise real failure modes

Don't rely on simple pings. Provider outages manifest across layers — DNS, TCP, TLS, HTTP, and app logic. Your transactions should validate the full path.

Core synthetic transaction patterns

DNS resolution + authoritative check: resolve your domain using multiple resolvers (ISP resolver, 1.1.1.1, 8.8.8.8, DoH) and validate that the returned A/AAAA/CNAME records match expected provider edge addresses.
TCP/TLS handshake: ensure SYN/ACK and TLS handshake complete within threshold (e.g., TLS < 500ms from each region).
HTTP end-to-end: fetch a small known resource and validate response codes, headers (server, via, x-cache), and body checksum.
Stateful flows: scripted login, session cookie validation, and an API call that depends on origin reachability (use Playwright or Puppeteer for browser paths).
Edge-specific checks: validate CDN cache behavior (miss vs hit), edge-routing headers, and origin fallbacks.

Example: simple multi-resolver DNS + HTTP probe (bash)

#!/bin/bash
# Probe: DNS via multiple resolvers then HTTP fetch
DOMAIN=example.com
RESOLVERS=(1.1.1.1 8.8.8.8 9.9.9.9)
for R in "${RESOLVERS[@]}"; do
  dig +short @$R $DOMAIN A | sed -n '1p'
done
# HTTP check
curl -sS -D - -o /tmp/body.txt https://$DOMAIN/healthz --max-time 10
if grep -q "ok" /tmp/body.txt; then
  echo "HTTP ok"
else
  echo "HTTP failed"
fi

Example: Playwright synthetic transaction (node)

const { chromium } = require('playwright');
(async () => {
  const browser = await chromium.launch();
  const page = await browser.newPage();
  await page.goto('https://app.example.com/login', { waitUntil: 'networkidle' });
  await page.fill('#user', 'probeuser');
  await page.fill('#pass', process.env.PROBE_PASS);
  await page.click('#submit');
  await page.waitForSelector('#dashboard', { timeout: 10000 });
  console.log('Login synthetic success');
  await browser.close();
})();

3) Aggregate telemetry to detect provider-scale patterns

Individual probe failures are noisy. Detecting provider-wide incidents requires grouping and correlation.

Aggregate by these dimensions

Provider/ASN: map IPs to ASN/provider and count failures per ASN.
POP/Edge: use response headers (e.g., Cloudflare: cf-ray, AWS: x-amz-request-id or x-cache) to group by edge POP.
Region/Metro: group by probe location to find geographic concentration.
Resolver: group DNS errors by recursive resolver to detect DoH/DoT problems or resolver-specific outages.

Detection heuristics that work

Proportion threshold: if >30% of global probes or >50% of probes in two different regions fail within 3 minutes, escalate to provider-scale alert.
Cross-layer correlation: simultaneous DNS failures across multiple resolvers plus HTTP TLS handshake failures strongly indicate provider-level CDN/DNS problems.
Sudden spike detection: use short-window anomaly detection (e.g., 3-min vs 1h baseline) to catch fast incidents from rollouts.
AS-path changes: combine BGP stream signals (route withdrawals, hijacks) with probe failures for confidence; cross-reference feeds like postmortem and incident comms when assembling evidence.

4) Alerting: SLO-driven, provider-aware, and playbook-friendly

Alert fatigue is fatal. Your alerts should be meaningful for provider-scale incidents and actionable for on-call teams.

Alert types and routing

Severity P1 (provider-scale): triggered when aggregated failures cross your provider-scale heuristics. Notify on-call, paging, and exec stakeholders.
Severity P2 (regional outage): targeted paging for regional infra owners; lower noise allowed.
Informational: trend alerts for early signs, routed to Slack channels and dashboards.

Alert content: what to include

Summary: provider suspected, affected regions, timestamp, severity.
Evidence: probe failure rate, sample probe outputs, headers showing POP IDs, DNS answers, and BGP changes.
Suggested action: follow runbook steps (e.g., validate provider status page, switch DNS policy, enable direct origin routing).
Automations: include links/buttons to run mitigations (toggle failover, scale origin, adjust TTL).

Alerting strategy examples

Use Prometheus + Alertmanager or a managed observability platform. Example PromQL for provider-scale alert (conceptual):

# percent failing probes over 5m grouped by provider
(sum by(provider) (probe_failures{job="synthetic"}[5m])
/ sum by(provider) (probe_runs{job="synthetic"}[5m])) > 0.3

5) Runbooks and automated mitigations

Have playbooks that map provider-scale alerts to concrete steps. Keep them short and role-specific.

Minimal provider-scale runbook (P1)

Confirm: open the aggregated dashboard and verify failures across ≥2 regions and ≥2 ASNs.
Check provider status: query provider status APIs (Cloudflare, AWS Health API). Document the response.
Cross-validate: run independent checks (RIPE Atlas, public monitors, third-party dashboards like DownDetector/StatusGator).
Mitigate: enable origin direct routing (bypass CDN) or switch DNS to alternate provider if you have multi-CDN setup.
Communicate: update incident channel and status page with initial impact and mitigation steps.
Postmortem: collect synthetic logs, BGP histories, and provider update timelines for RCA.

Automations to reduce toil

Automatic failover: low-risk traffic reroute to secondary CDN/origin when P1 triggers.
Pre-warmed origins: keep an origin path available for instant cutover.
Auto-posting status: use scripts to post verified incident summaries to your status page and Slack channels.

Implementation: practical examples

Using AWS Synthetics (CloudWatch Canary) + Lambda

AWS Synthetics can run browser-based canaries from multiple regions. Combine canary results with Lambda-based aggregation that maps failed IPs to ASNs and posts to Alertmanager or PagerDuty.

# high level: every canary run posts JSON to an S3 bucket; Lambda consumes, enriches with ASN lookup, and writes to TSDB

Edge probes with Cloudflare Workers

Cloudflare Workers Cron Triggers provide cheap, globally distributed probes. Deploy a lightweight fetch that records DNS resolution via DoH, TLS timings, and POP response headers back to your collector. Use Workers KV or Logs for short-term storage.

Integrating RIPE Atlas and BGP telemetry

Subscribe to RIPE Atlas and BGPstream alerts for route withdrawals/hijacks. When your probes show increasing failures for IPs in a given ASN, cross-reference public BGP data before escalating to a full P1 — this improves signal-to-noise.

Advanced strategies and 2026 trends

Expect the following trends through 2026 — incorporate them into your observability strategy:

Edge-native probes: more teams will run probes in provider edge compute (Workers, Lambda@Edge) for sub-second detection. See hybrid edge orchestration patterns in the Hybrid Edge Orchestration Playbook.
eBPF-powered path introspection: use eBPF at the origin to correlate TCP/TLS anomalies with network stack signals in real time.
AI-assisted correlation: ML models that ingest probe telemetry, BGP events, and provider status feeds to surface likely root cause quicker — combine this with guidance on Gemini-guided workflows for model-ops and alerting.
Proactive SLO-driven remediations: systems that automatically cut TTLs, change DNS weights, or spin up alternative providers when SLO degradation is detected.
Multi-CDN and multi-DNS as standard: vendor lock-in risk has pushed many teams to active multi-provider setups to reduce blast radius; see hybrid orchestration playbooks for patterns.

Measurement and validation

Track the effectiveness of your observability investment.

MTTD improvement: compare mean time to detect before/after synthetic expansion.
False positive rate: tune thresholds to keep false positives under control (see testing guidance like cache & threshold testing).
SLO compliance: measure synthetic success rate against SLOs and use that to drive capacity or provider selection.

Common pitfalls and how to avoid them

Too few vantage points: leads to blind spots. Start small but diversify quickly.
Overfitting thresholds: tight thresholds cause pager fatigue. Use multi-dimensional correlation before paging.
Relying solely on provider status pages: they lag. Treat them as one signal, not the source of truth.
No runbook automation: manual steps slow response. Automate safe mitigations and provide one-click actions in alerts.

"Detecting provider-scale outages is not about more probes — it's about smarter probes, better aggregation, and runbooks that let you act before users notice."

Sample provider-scale alert template

Use this in your alerting system; include fields and links to automate evidence collection:

Title: P1 - Provider-scale outage suspected for Cloudflare (or AWS)
Severity: P1
Evidence:
  - probe_fail_rate: 42% (5m)
  - affected_regions: EU-WEST, US-EAST
  - affected_ASNs: AS13335, AS16509
  - sample_responses: [curl output, cf-ray headers]
Suggested Actions:
  1) Confirm provider status API (link)
  2) Trigger failover to secondary CDN (link)
  3) Post initial incident update (link)

Post-incident: RCA checklist

Collect time-synced synthetic logs and provider status announcements.
Map affected IPs to ASNs and POPs and compare with BGPstream data.
Evaluate what mitigations worked and what caused delays.
Update SLOs, probe coverage, and runbooks accordingly.

Putting it together: a 30-day starter plan

Week 1: Deploy 10–20 probes across major regions. Implement DNS + HTTP core checks.
Week 2: Add stateful synthetic transactions for critical user flows and integrate ASN enrichment.
Week 3: Configure aggregated dashboards and set provider-scale alert heuristics; test runbooks with war games.
Week 4: Automate one low-risk mitigation (e.g., switch traffic to a secondary origin) and measure MTTD improvement.

Final recommendations

Start with realistic probes, diversify vantage points, and build aggregated, SLO-aware alerts. Provider-scale incidents are rare but high-impact. The combination of synthetic transactions, ASN-aware aggregation, automated mitigations, and concise runbooks will catch issues faster than user reports and reduce operational load.

Call to action

Ready to implement provider-scale detection? Start with a lightweight pilot: deploy 10 global probes, configure DNS + HTTP synthetic transactions, and create one P1 runbook. If you want a reusable starter kit — including Playwright scripts, Prometheus rules, and runbook templates tuned for Cloudflare and AWS patterns — reach out to truly.cloud or download our 30-day playbook to reduce your MTTD in weeks, not months.

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