telcotestingresilience

Simulating Carrier-Scale Failures: Playbook for Testing Mobile Network Resilience

nnet work

2026-01-29

10 min read

Emulate carrier-scale outages safely to test reconnection logic, scale limits, and customer experience following lessons from the Jan 2026 Verizon disruption.

Simulating Carrier-Scale Failures: A Playbook for Testing Mobile Network Resilience

Hook: If your team still treats carrier outages as once-in-a-blue-moon events, a January 2026 nationwide Verizon disruption showed how rapidly a single software failure can cascade into millions of customer-impacting connections. Network teams must be able to emulate such failures safely to validate reconnection logic, scale limits, and customer experience — without causing real outages.

Why this matters in 2026

Telco infrastructure has become overwhelmingly software-driven: cloud-native 5G cores, containerized network functions, and API-based orchestration dominate deployments. That shift reduces hardware single points of failure but increases the attack surface for software bugs and configuration errors. Late 2025 and early 2026 incidents — including the high-profile Verizon outage in January 2026 — forced operators and cloud providers to accelerate resilience testing. In practice that means: automated, repeatable emulation of carrier-scale failures, observability tuned for mass reconnection events, and production-safe chaos engineering integrated into CI/CD pipelines.

What you will get from this playbook

Concrete, safe methods to emulate carrier-scale outages in lab and staged environments
Automation patterns and code snippets for fault injection, traffic shaping, and scale testing
KPIs to measure reconnection behaviors and customer experience
Runbook phases, safety controls, and rollback strategies for production pilots

Top-level approach: Safe, repeatable, auditable

Apply the same principles used in DevOps chaos engineering, but with stricter safety controls and telco context:

Isolate — Test in isolated labs or air-gapped slices before any production attempt.
Automate — Use IaC and CI pipelines for repeatability and rollback.
Observe — Collect control-plane, user-plane, and application metrics in high resolution.
Throttle — Ramp failure scope in waves (10%, 25%, 50%) before going wider.
Respond — Have automated kill switches, pre-authorized rollback runbooks and a communications plan.

Phase 1 — Design & hypothesis: what failure behaviors do you need to validate?

Start with clear, testable hypotheses. Examples:

After a nationwide control-plane software restart, 95% of devices will re-attach within 3 minutes without manual intervention.
MME/AMF can handle X attach requests/second before queuing leads to dropped attach attempts.
Push-notification delivery fails when IMS registers exceed Y concurrent refreshes.

Map hypotheses to system boundaries and targets: RAN, S1/N2 control-plane, GTP-U user-plane, IMS, DNS, backend APIs.

Phase 2 — Build a safe emulation environment

Two parallel labs are essential: a functional lab that runs realistic CNF/VNF stacks (Open5GS, srsRAN, free diameter/HSS replacements, open-source IMS) and a load lab that can generate millions of simulated UEs and application sessions.

Core components to assemble

CNF/Cloud 5G core: Open5GS, free5GC, or your vendor CNFs running on Kubernetes.
RAN emulator: srsRAN or commercial RAN simulators that support thousands of simulated UEs.
UE simulators: Commercial UE farms or open tools (srsUE, UERANSIM) for attach/TAU cycles.
Traffic generators: TRex, sipp (SIP/IMS), iperf, and HTTP/S synthetic clients for app-level behavior.
Traffic control & fault injection: tc/netem, iptables, eBPF scripts, or chaos tools (Gremlin, Chaos Mesh, Litmus).
Observability: High-cardinality metrics (Prometheus), traces (Jaeger), and logs (ELK/Opensearch). Use specialized telco parsers for diameter/S1AP/NGAP logs.

Example: lab network partition using tc and iptables

To emulate a loss of GTP-U (user-plane) traffic between UPF and eNodeB/DU:

sudo iptables -A FORWARD -p udp --dport 2152 -j DROP
# undo
sudo iptables -D FORWARD -p udp --dport 2152 -j DROP

# introduce latency/loss on interface eth1
sudo tc qdisc add dev eth1 root netem delay 200ms loss 5%

GTP-C (control-plane) ports you may target during controlled experiments: GTP-C UDP 2123, S1AP/NGAP over SCTP (SCTP ports 36412/38412), Diameter (3868). Do NOT block management/SSH unless intentionally testing orchestration failure.

Phase 3 — Reconnection and scale test patterns

Design tests that exercise both device reconnection logic and core scaling limits:

Test 1 — Mass attach after control-plane restart

Bring down AMF/MME process or simulate a software configuration rollback in lab.
Simultaneously, ensure a large set of UE simulators are in an idle state ready to perform attach.
Bring the control-plane back up and measure time-to-attach distribution, attach success rate, message retransmissions, and error codes.

Key metrics: attach/sec, attach latency p50/p95/p99, ATTACH_REJECT reasons, retransmit counts.

Test 2 — Staggered reconnection waves to validate backoff behavior

Many devices implement exponential backoff. Validate that clients’ reconnection logic respects network guidance and does not stampede control plane:

Create waves: 5% of UEs, then 20%, then 75%.
Observe queue depths at AMF/MME and failure surge behavior (timeouts, blocked attach paths).
If stampede appears, test mitigations: connection rate limiting at the edge, per-IMSI token-bucket, or paging backoff instructions.

Test 3 — Application-level reconnection (push, auth, API)

Network reconnection is only part of the customer experience. Test apps and backend services that depend on network signaling:

Simulate reauth flows (OAuth refresh), push notification re-registration, and IMS/SIP re-REGISTER bursts.
Use sipp for SIP/IMS re-register testing and instrument SIP transactions per second.

Automation examples: integrate into CI/CD

Automated experiment definitions are essential for reproducibility and audit trails. Below is a simple Chaos Mesh-style experiment to block GTP-U for a namespace of UPFs (example YAML):

apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
  name: block-gtpu
  namespace: telecom-testing
spec:
  action: drop
  mode: fixed
  selector:
    namespaces:
      - upf-namespace
  ipList:
    - 10.42.1.0/24
  ports:
    - 2152
  duration: '300s'

Wrap experiments in a pipeline step (GitOps-friendly):

Run integration tests against control-plane mocks.
Trigger chaos experiments in the lab via CI (with automatic rollback on SLO breach).
Promote to canary stage with throttled scope and real UE simulators.

Observability: what to collect and why

High-fidelity telemetry is the difference between diagnosing a reconnection bug and guessing. At minimum capture:

Control-plane metrics: attach/sec, detach/sec, TAU/sec, authentication failures, PDU session establishment times.
User-plane metrics: GTP-U throughput, per-UPF session counts, packet loss and latency.
Protocol logs: S1AP/NGAP traces, GTP-C traces, Diameter messages, SIP traces for IMS.
Application and API metrics: push notification registration rates, API error rates from backend.
Network infrastructure: BGP route changes, DNS query spikes, load balancer connection rates.

Instrument dashboards with percentiles and cumulative distribution functions (CDFs) for attach latency. Keep raw traces for at least 7–14 days during active experiments.

Scaling limits: how to find your system's breaking points

Progressive ramp tests uncover different bottlenecks:

CPU and memory saturation on AMF/MME/SMF and UPF pods or VMs
Database limits in HSS/UDM or subscriber data stores — watch transaction latency
Network I/O constraints between edge and core (GTP tunnels, N6 interfaces)
Orchestration and autoscaling delays — pods may overshoot or be rate-limited by cloud provider quotas

Run load until you see graceful degradation, not catastrophic failure. Define clear abort criteria (e.g., attach success rate < 90% for 5 consecutive minutes) to stop tests before customer-impacting behavior extends beyond your lab.

Case study: Hypothetical lab replay of the January 2026 nationwide outage

In a recent internal exercise modeled on the January 2026 Verizon incident, an operator recreated a software upgrade rollback that caused a timing regression in the AMF attach path. Test summary:

Setup: 500k simulated UEs in lab across multiple RAN cells using srsRAN + UERANSIM clusters.
Fault: Introduced a control-plane delay (200ms) plus a small GTP-C packet loss (1–2%).
Result: 18% attach failures at the AMF due to retransmit storm; devices that power-cycled eventually re-attached, but many mobile apps failed due to incomplete IMS re-registration.
Fixes validated: connection rate limiting at the edge and backoff guidance reduced retransmit storms; a patch to the AMF fixed the timing regression.

Lessons: test both the network and dependent services (IMS, push, DNS), and prepare a customer-communication template in advance.

Safe production testing: pilot, ramp, rollback

When you move to production pilots, follow a strict control set:

Authorizations: Secure change advisory board (CAB) sign-off and legal checks.
Scope: Use subscriber cohorts and geographic slices — do not test on all customers.
Schedule: Prefer low-traffic windows with pre-notified maintenance windows where applicable.
Kill switches: One-command rollback that reverts network ACLs, removes chaos rules, and re-deploys golden config.
Analytics gating: If any SLO breaches occur, automation aborts the experiment and executes a rollback runbook.

Safety, compliance and stakeholder communication

Regulatory and customer trust considerations are critical. Best practices:

Notify regulators when tests could affect emergency services or lawful interception paths.
Avoid experiments that interfere with 911/E112 paths or critical IoT SIMs used by health or safety devices.
Coordinate with customer-care and legal teams to pre-approve credit or remediation scripts.
Document each experiment in an audit log (who ran it, what was changed, start/end times, and rollbacks).

Advanced strategies for 2026 and beyond

As carrier stacks evolve, adapt your testing strategy:

Network slicing tests: Validate slice isolation under failure — a failure in a best-effort slice must not degrade URLLC slices.
Edge and MEC validation: Test the behavior of session continuity when UPF fails at the edge and traffic is tunneled to central cloud.
CNF lifecycle chaos: Experiment with control-plane upgrades (canary, blue/green) at scale and observe cross-component regressions.
Supply-chain resilience: Verify multi-vendor and multi-cloud failover for core functions and data-plane paths.
AI-based anomaly generation: Use ML to synthesize realistic reconnection patterns based on prior incidents (late 2025 incident telemetry can be used to train models).

Checklist: Minimum viable test to emulate a Verizon-style outage

Isolated lab with cloud-native core and RAN emulator.
UE simulators capable of mass attach and power-cycle workflows.
Traffic control tooling (iptables/tc or chaos platform) with scripted rollback.
Observability for control/user/APP planes (Prometheus + tracing + log aggregation).
Automated abort criteria tied to SLOs and a one-command rollback.
Stakeholder notifications and legal/regulatory gate.

Sample runbook: step-by-step for a 50% reconnection wave

Pre-check: Health of orchestration, DB replication status, and emergency paths (SSH, NMS).
Start experiment: Apply network chaos to drop GTP-C for 50% of UPFs for 2 minutes.
Monitor: Attach/sec, attach success rate, API error rate, and customer-facing app errors.
Ramp down: Restore connectivity; allow 10 minutes to observe re-attach stabilization.
Analyze: Collect traces for failed attaches, error codes, retransmit counts, and backend traces.
Remediate: If SLOs breached, execute rollback and assess fixes before repeating at smaller scope.

“Resilience is not an afterthought — it’s part of the delivery pipeline.”

KPIs and SLOs you should track

Attach success rate (target 99%+ in lab, production targets defined by operator policy)
Time to re-attach p50/p95/p99
API-level success rates for authentication and push re-registration
Network-induced application error increase (%) compared to baseline
Mean time to rollback and mean time to restore normal attach rates

Final recommendations and 2026 trends to watch

Through 2026 expect these ongoing shifts to shape outage emulation practices:

Cloud-native telco toolchains: Standardized orchestration (Kubernetes + CNF best practices) will make automated rollbacks and canaries more powerful.
Regulatory attention on resilience: Regulators will demand more demonstrable testing and post-incident transparency following widespread outages.
Edge elasticity: More functionality at the edge will require localized chaos tests for session continuity.
AI-driven detection and remediation: Automated anomaly detection and self-healing will reduce blast radius if you integrate experiments into your observability fabric.

Actionable takeaways

Build an isolated, instrumented lab that mirrors production as closely as possible.
Automate chaos experiments and integrate them into CI with strict abort criteria.
Test both network reconnection and dependent application workflows.
Use staged pilots with authorized kill switches; document and audit every experiment.
Track reconnection KPIs and iterate on rate limiting, backoff guidance and AMF/MME tuning.

Closing: start small, test often, document everything

The Verizon outage in January 2026 is a reminder that modern carrier networks can be disrupted by software issues and configuration mistakes at scale. The right approach is not to avoid failure but to practice it under controlled, auditable conditions. Use the playbook above to build repeatable tests, validate reconnection behaviors, and harden your stack against the next large-scale incident.

Call to action: Ready to build a lab-grade emulation for your network? Contact our engineering team for a tailored assessment and a starter automation repo that includes UE simulation scripts, chaos definitions, and dashboard templates tuned for telco KPIs.

net work

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.