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Detecting Platform-Wide Outages with Synthetic Monitoring and Real-User Metrics

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2026-02-12

10 min read

Practical setup guide (2026) to detect and attribute CDN, DNS, and origin failures using synthetic checks, RUM, and SLO-aligned alerts.

Hook: Stop guessing when a platform outage happens — detect and attribute fast

If your engineering team woke up to the Jan 2026 outage spike that hit major players (Cloudflare, AWS, and high-profile platforms), you already felt the pain: widespread error reports, noisy alerts, and hours of chasing symptoms. The hard truth for technology teams is that surface symptoms—spikes in 5xxs or mass user complaints—don't tell you whether the root cause is a multi-CDN architectures, a edge compute platforms, or an origin/backend problem. This guide shows a pragmatic, reproducible setup using synthetic monitoring, real-user metrics (RUM), and robust alerting thresholds so you can detect platform-wide outages and confidently attribute them in minutes, not hours.

Why this matters in 2026

Late 2025 and early 2026 brought more clustered outages and cascading failures as the web became more distributed: multi-CDN architectures, widespread HTTP/3/QUIC adoption, more DNS-over-HTTPS/TLS (DoH/DoT) resolver variability, and edge compute platforms running business logic. Those trends increase the surface area where failures can look identical at first glance. Observability must evolve accordingly.

Multi-CDN and edge compute increase dependency complexity.
DoH/DoT and resolver diversity create inconsistent DNS results across regions.
HTTP/3 and QUIC change transport-layer timing characteristics (use QUIC-aware checks).
BGP routing anomalies and DDoS mitigation can cause regional reachability issues quickly.

Strategy overview: Combine synthetic checks, RUM, and telemetry

Detecting and attributing outages reliably requires three pillars working together:

Synthetic monitoring — deterministic checks from many locations (HTTP, DNS, TCP, traceroute, QUIC) to detect failures under controlled conditions.
Real-user monitoring (RUM) — client-side telemetry showing what actual users experience, including DNS/TCP/TLS and resource-level timings.
Infrastructure & network telemetry — origin health, CDN edge metrics, DNS authoritative server metrics, BGP feeds, and logs to validate hypotheses.

When an incident starts, use a simple decision matrix: if DNS queries fail across resolvers, suspect DNS; if DNS works but edge responses are 5xx across many regions and cache-hit ratios drop, suspect CDN; if edge returns cache-miss and origin connect times spike or origin 5xx increase, suspect origin. We'll turn that into checks and alerts below.

Design principles

Multi-location: Run checks from at least 8+ geographies and multiple ASes to avoid single-resolver bias.
Layered checks: Fast heartbeat (30s–60s) + deeper diagnostics (DNS trace, traceroute, origin connect) at lower cadence.
SLO-aligned alerts: Use error budgets and SLO windows to reduce noise and prioritize impactful incidents.
Correlate first: Alert only after synthetic and RUM signals align (configurable for critical services).
Automated runbooks: Each alert should include the next steps (dig, curl, headers, BGP lookup) so responders act faster — and prefer IaC templates and scripts where appropriate.

Synthetic monitoring: configuration checklist and examples

Synthetic checks are your early-warning system. Configure a mix of lightweight and deep checks:

1) Basic HTTP heartbeat

Purpose: detect general HTTP failures quickly.

Type: HTTP GET to a small static asset (e.g., /heartbeat.txt), expecting 200 and content hash.
Frequency: 30s from 8–12 global nodes (North America, EU, APAC, South America, Africa).
Headers: record Server, CF-Cache-Status (or provider cache header), Via; capture response time and TLS handshake time.
Failure criteria: non-200 OR content mismatch OR TLS failure OR response time > 3s.

2) DNS checks

Purpose: detect authoritative or recursive DNS issues and propagation problems.

Type: Do three checks — authoritative query for SOA/NS, recursive A/AAAA via multiple public resolvers, and DoH/DoT resolver checks.
Frequency: 1m for recursive checks, 5m for authoritative and full dig +trace.
Metrics: resolution time, NXDOMAIN/REFUSED/FORMERR rates, TTLs, DNSSEC failure flags, mismatched records across resolvers.
Failure criteria: >1% resolution failure across globe OR consistent NXDOMAIN across multiple resolvers OR authoritative server not responding.

3) TCP connect, TLS handshake, and QUIC checks

Purpose: separate transport-level failures from application errors.

Type: TCP connect to port 443, TLS handshake validation, and HTTP/3 probe (QUIC) when supported.
Frequency: 1m for TCP/TLS; 5m for QUIC health probes.
Failure criteria: connection timeout, handshake failure, certificate validation failures, or large handshake latency (>500ms).

4) Traceroute and BGP checks (deep diagnostic)

Purpose: capture routing anomalies or path blackholes that cause regional outages.

Type: traceroute, TCP traceroute, and a BGP prefix origin check (using BGPstream or RIPE RIS APIs).
Frequency: 5–15m (lower frequency to reduce noise), triggered on anomalies.
Output: hops, RTT per hop, unexpected AS path changes, new invalid origin ASes.

Example synthetic definition (pseudo-config)

<check id="heartbeat-global" type="http" interval="30s" nodes="us-east,us-west,eu-nl,ap-syd,sa-sp,af-za"
  url="https://www.example.com/heartbeat.txt" expect_status=200 expect_hash="e3b0c442...">
  </check>

RUM: What to collect and how to send it

RUM shows the true user impact. Instrument browsers and client apps to capture the following:

Timing APIs: Navigation Timing, Resource Timing, Paint Timing, Long Tasks (W3C specs) to get DNS/TCP/TLS/requestStart timings and Core Web Vitals.
Network error events: capture failed resource loads, CORS errors, and full networkError stack traces.
Edge & cache headers: capture response headers that indicate cache status (CF-Cache-Status, x-cache, via) where privacy allows.
Geolocation via IP/ASN: approximate region and ASN from IP to identify resolver or transit differences (don’t capture precise GPS unless consented).
Session sampling: default 5–10% sampling for full sessions, 100% for error sessions to avoid missing rare failures.

Use the Beacon API or OTLP exporters (2026 trend: RUM adoption of OpenTelemetry OTLP HTTP) to reliably send small payloads. Implement client-side aggregation (batch by user/session) and enforce PII stripping on the client.

Correlating synthetic and RUM to attribute failures

Attribution is easier when you map signals against the three layers.

CDN failure signature

Synthetic: HTTP checks return edge-specific 5xx codes (e.g., 520, 525, 503) across many locations; cache-hit ratio drops in synthetic headers.
RUM: Users across regions get edge errors; resource timing shows DNS and TCP succeed but responseStart is immediate error; increased origin fetches noted in edge metrics.
Infra: CDN provider status page or edge health metrics show elevated error rates; origin logs may show normal behavior because origin isn’t failing.

DNS failure signature

Synthetic: DNS queries fail (REFUSED, SERVFAIL, NXDOMAIN) or become slow; authoritative servers may be unreachable.
RUM: Sessions show no timing data beyond navigation start and networkError; browser fails before DNS resolution stages complete.
Infra: BGP/ASN checks are normal; the issue often shows in DNS provider dashboards or with specific resolvers (DoH differences are common).

Origin failure signature

Synthetic: Edge calls to origin (via a dedicated origin check) show high TCP connect times, timeouts, or 5xx from origin-specific headers.
RUM: Resource timings show DNS and TCP mostly OK, but request waiting (TTFB) spikes and many 5xx responses originate from origin headers; cache-miss rates increase.
Infra: Origin health metrics (CPU, thread pools, DB errors) show elevated errors and latency.

Alerting: thresholds, dedupe, and runbooks

Alerting must be precise to avoid pager fatigue. Use SLO-driven and multi-signal triggers.

Sample severity rules

Critical (P1): Three or more synthetic locations detect HTTP 5xx >5% in 2 consecutive 1-minute windows AND RUM global failed sessions >3% in 5 minutes. Trigger P1 and page on-call.
High (P2): Two locations’ synthetic checks detect DNS resolution failures >1% for 3 minutes OR origin TCP connect >500ms spike across multiple locations.
Warning (P3): Single-region elevated latency (p95 > 2s) or cache-hit rate drop >10% in one region for 10 minutes.

Use N-of-M location rules

Require anomalies in N of M distinct nodes to avoid false positives caused by a single noisy node or regional ISP incident. Example: alert only if >=3 nodes out of 8 show failure for 2 consecutive checks.

PromQL examples (conceptual)

expr: sum by (region)(rate(http_requests_total{status=~"5.."}[5m]))
/ sum by (region)(rate(http_requests_total[5m])) > 0.05

This computes per-region 5xx rate over 5 minutes. Combine with RUM failed session metric:

expr: rate(rum_failed_sessions_total[5m]) > 0.03

Alert dedupe & suppression

Suppress lower-severity alerts when a higher-severity correlated alert is active.
Use exponential backoff for repeated flapping events and require manual acknowledge for P1s.
Include automated context: recent synthetic results, sample RUM session IDs, and quick links to the CDN/DNS provider status pages. Integrate with your on-call tooling and runbooks (PagerDuty/Opsgenie or a lean on-call playbook such as the tiny teams support playbook).

Investigation runbook (first 10 minutes)

Check correlated synthetic dashboards: which checks/regions failed and at what times.
Verify RUM: sample failing sessions for client geography and ASN info.
Run targeted diagnostics from a shell: dig +trace, dig @8.8.8.8, curl --resolve, curl -v to capture headers, and traceroute/tcping to origin and edge IPs.
Check CDN headers and cache behavior: CF-Cache-Status, x-cache, Via. A sudden rise in MISS + edge 5xx suggests CDN issues.
Check authoritative DNS servers and provider dashboards; query multiple resolvers (DoH/DoT) to find resolver-specific issues.
Check BGP feeds (RIPE RIS / BGPStream) for prefix hijacks or major AS path changes.
Open origin logs and metrics: look for elevated 5xx, thread pool exhaustion, or DB errors.

Automation suggestions

Automate diagnostic collection: when an alert fires, execute a pre-approved script that runs dig, curl, traceroute from multiple runners and attaches results to the incident — consider packaging these as IaC templates and runbooks.
Use synthetic check webhooks to post snapshots into the incident (screenshots, response headers, time series snippets).
Integrate RUM with OpenTelemetry and export traces to the same observability backend for cross-correlation; this pairs well with modern compliant tracing and telemetry pipelines that centralize logs, metrics, and traces.

Privacy and compliance

RUM can capture sensitive data inadvertently. Implement client-side filtering, avoid sending URLs with PII, and ensure compliance with regional regulations (GDPR, CCPA) when capturing IP-to-geo mappings.

Future-proofing and 2026 predictions

Expect more reliance on multi-CDN failover and automated traffic steering—monitor your failover decisions synthetically before relying on them in production.
Adopt QUIC/HTTP3-aware synthetic probes; traditional TCP/TLS checks miss QUIC-specific issues.
DNS over HTTPS/TLS proliferation will keep creating resolver-specific visibility gaps—run both DoH and classic UDP checks.
AI-powered anomaly detection will move from naive thresholds to context-aware models—use ML to reduce alert noise but keep deterministic checks for attribution.

Actionable checklist (quick reference)

Deploy global HTTP heartbeat checks every 30s (static asset + hash).
Deploy recursive DNS checks (1m) + authoritative dig +trace (5m).
Run TCP/TLS/QUIC probes (1m) and traceroute/BGP checks (5–15m).
Instrument RUM with Navigation Timing, Resource Timing, and capture error sessions at 100%.
Set SLO-aligned alerting rules and require N-of-M location confirmation for P1s.
Automate diagnostic collection into incident tickets and integrate with on-call workflows and PagerDuty/Opsgenie.

"When the platform blinks, synthetic sees the flicker, RUM feels the sting, and your runbooks close the loop."

Case study: Rapid attribution during the Jan 2026 outage spike (concise)

During the Jan 16, 2026 spike affecting major platforms, teams using combined synthetic + RUM setups identified the root cause faster. Synthetic HTTP checks flagged elevated 520/525 edge errors from multiple CDN edges while DNS checks stayed green. RUM showed errors across many ASNs but with successful DNS resolution in timing traces. Correlation pointed to CDN edge software/route issues—teams triggered provider failover and mitigations within 20–40 minutes, shortening incident windows compared with teams that relied solely on backend telemetry.

Wrapping up: measurable outcomes and what to expect

By implementing the layered strategy above, you will:

Reduce mean time to detect (MTTD) by correlating deterministic synthetics with RUM.
Reduce mean time to resolve (MTTR) by automating diagnostics and using clear attribution rules.
Reduce false positives and pager fatigue by aligning alerts with SLOs and requiring multi-node confirmation.

Call to action

If you want a tested synthetic + RUM blueprint tailored to your stack (multi-CDN, DoH/DoT, or heavy edge compute), we can produce a ready-to-deploy configuration and alerting policy for your team. Reach out to start a pilot and reduce your outage detection time from hours to minutes.

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