Test Direct Connect Failover with Fault Injection

A lot of network outages don’t start with a dramatic “everything is down.” They start with a small, boring change: a Border Gateway Protocol (BGP) session flaps, a route preference shifts, or a redundant path that should take over… doesn’t. If your applications rely on AWS Direct Connect for predictable connectivity, those “small” moments can turn into real downtime fast.

AWS just made it easier to prove your Direct Connect resilience before production proves it for you: AWS Direct Connect now supports resilience testing through AWS Fault Injection Service (FIS). Practically, that means you can run controlled experiments that disrupt BGP sessions on your Direct Connect virtual interfaces and watch how traffic and applications respond.

This post is part of our AI in Cloud Computing & Data Centers series, where the theme is simple: modern infrastructure reliability is increasingly driven by “intelligent operations”—automation, experimentation, and feedback loops that look a lot like AI-driven optimization, even when there isn’t a model in the loop. Fault injection is one of the most effective ways to get there.

What AWS added: Direct Connect BGP disruption in AWS FIS

Answer first: AWS FIS can now intentionally disrupt BGP sessions over AWS Direct Connect virtual interfaces (VIFs) so you can validate failover behavior under controlled conditions.

AWS Fault Injection Service is AWS’s managed chaos engineering platform: you define an experiment, specify targets, set guardrails (stop conditions), run the test, and observe. With this update, the “target” can be a Direct Connect BGP session associated with a VIF.

Here’s what that unlocks in plain terms:

• You can simulate a Direct Connect routing failure without pulling cables, calling carriers, or waiting for a real incident.
• You can verify that traffic fails over to redundant VIFs (or alternative network paths) the way your architecture diagram says it should.
• You can collect evidence—logs, metrics, traces—that your resilience mechanisms actually work.

This matters because network resilience is often treated as a design-time concern (“we have two links, therefore we’re resilient”). The reality? Resilience is a runtime property. You only know you’re resilient after you’ve tested failure.

Why this is a big deal for AI-driven infrastructure optimization

Answer first: Controlled fault injection turns resilience into measurable data, which is the foundation for AI-assisted operations and resource optimization.

If you want “AI in cloud computing” to mean something practical, you need inputs that are:

1. Repeatable
2. Observable
3. Comparable over time

Fault injection gives you exactly that. Each experiment produces an operational dataset: routing convergence times, retransmits, error rates, queue depth, application latency, failed transactions, and more. That dataset can feed smarter automation—whether it’s a rules-based system, an AIOps platform, or an ML model that learns what “healthy failover” looks like.

Fault injection is intelligent workload management in disguise

When BGP fails over, workloads don’t just “keep running.” They often behave differently:

• Stateful services may retry aggressively.
• Timeouts can trigger thundering herds.
• Connection pools can collapse and rebuild.
• Batch jobs might slow down or requeue.

Testing these behaviors lets you tune:

• Retry and timeout policies
• Connection keepalive settings
• Circuit breakers and backpressure
• Traffic engineering preferences

That’s intelligent workload management: shaping how your systems consume network and compute during turbulence, instead of letting them panic.

Resilience testing supports energy and capacity efficiency

When failover is unpredictable, teams overcompensate:

• Extra headroom “just in case”
• Over-provisioned links
• Always-on standby capacity that’s never exercised

Proactive testing reduces that uncertainty. If you can prove your failover behavior and convergence time, you can size capacity more rationally. In data centers and cloud environments, that translates to better utilization and fewer “idle safety buffers” burning cost and energy.

What to test: the Direct Connect failure modes that hurt most

Answer first: Test the failures that trigger unexpected routing behavior, slow convergence, or application-level timeouts—starting with BGP session disruption and path preference changes.

The new FIS action focuses on disrupting BGP sessions. That’s perfect, because BGP is where many “we built redundancy” stories fall apart.

Scenario 1: Primary VIF drops, traffic should shift to a redundant VIF

This is the classic design:

• Primary Direct Connect path
• Secondary Direct Connect path (different device/location) or backup connectivity

What you want to confirm:

• Traffic actually moves to the secondary path
• DNS, service discovery, and endpoints behave normally
• Application latency doesn’t spike beyond your SLO

What commonly breaks:

• Route preference not configured as expected
• Asymmetric routing introduces weird latency or drops
• Security controls (ACLs, firewalls, on-prem policies) differ by path

Scenario 2: Failover works, but convergence is too slow for your timeouts

A lot of apps have timeouts tuned for “normal” conditions. During a route change, you might see:

• 15–60 seconds of connection churn
• Retries backing up queues
• L7 gateways returning 502/504s

The goal of testing is to align network convergence with application behavior. If your network takes 30 seconds to settle, but your API gateway times out in 5, your users still feel an outage.

Scenario 3: Failover causes a noisy neighbor effect

When traffic shifts, it can concentrate:

• One VIF suddenly carries most traffic
• One NAT, firewall, or proxy becomes the bottleneck
• One set of instances starts saturating ENIs or conntrack

In AI-operations terms, this is where you want to detect early signals and automatically apply mitigations—rate limits, scaling policies, or traffic shaping.

How to run a Direct Connect resilience experiment (a practical playbook)

Answer first: Start with a tight blast radius, define stop conditions, measure convergence and user impact, then repeat until results are boring.

I’ve found that most chaos programs fail for one of two reasons: they start too big, or they don’t measure outcomes clearly. Here’s a practical sequence for Direct Connect + FIS.

Step 1: Pick one “golden path” service

Choose a service that:

• Represents real business traffic
• Has good observability (metrics + logs + traces)
• Has a defined SLO (latency, error rate, throughput)

If you can’t measure it, you can’t improve it.

Step 2: Define what success looks like (numbers, not vibes)

Write the success criteria before the test:

• Failover convergence time: e.g., under 30 seconds
• Error budget impact: e.g., < 0.1% additional 5xx over 10 minutes
• Latency ceiling: e.g., p95 < 250 ms during event
• No manual action required

Even if you don’t hit these targets on the first run, you’ll know exactly what to fix.

Step 3: Set stop conditions like you mean it

FIS supports stop conditions, and you should treat them as non-negotiable guardrails. Examples:

• Alarm on 5xx error rate crossing a threshold
• Alarm on queue depth or dropped connections
• Alarm on synthetic transaction failure

This is where disciplined testing becomes safe testing.

Step 4: Disrupt BGP on the primary VIF and watch the full stack

Don’t only watch the network.

Watch:

• BGP session state changes
• Route tables and effective paths
• Application error rate and latency
• Connection pool health
• Retries/timeouts and queue backlogs

The “AI angle” here is feedback loops: each run gives you labeled examples of “healthy vs unhealthy failover,” which can train detection and automated remediation later.

Step 5: Fix one thing, rerun the same experiment

The win isn’t running a dramatic test once. The win is rerunning until outcomes are predictable.

Common fixes after the first run:

• Adjust BGP preferences and route propagation
• Align timeout/retry strategies across services
• Add capacity to the actual bottleneck (often not the link)
• Improve runbooks and automated failover procedures

Where teams get Direct Connect resilience wrong

Answer first: They treat redundancy as a checkbox, forget the application layer, and never rehearse failover under load.

A few opinions, based on what I see most often:

“We have two links, so we’re fine.”

Two links that fail over slowly, or into a misconfigured policy, are still an outage.

“The network team owns this.”

Failover is a cross-layer event. Network, platform, and application teams all contribute to the outcome. If your retries or connection handling are messy, the best network in the world won’t save your user experience.

“We’ll test during the maintenance window.”

Maintenance windows are when systems are least representative: traffic is low, alerting is muted, and everyone expects weirdness. Resilience testing should happen in controlled, realistic conditions (with guardrails), because that’s how you learn what happens when it matters.

People also ask: Direct Connect + FIS quick answers

Does this replace redundancy design?

No. It validates it. Redundant architecture without testing is an assumption.

Is this only useful for large enterprises?

No. Any team using Direct Connect for predictable latency or compliance reasons benefits. The smaller the team, the more you should prefer managed testing over manual “pull the plug” drills.

What’s the AI connection if FIS isn’t an AI tool?

The connection is the operating model: experiment → observe → learn → automate. That loop is how AI-assisted cloud operations get reliable, and it’s how data centers get more efficient over time.

Next steps: turn this into a repeatable reliability program

AWS Direct Connect resilience testing with AWS Fault Injection Service is a practical step toward more reliable—and more optimizable—cloud connectivity. If you’re serious about uptime, you should want your failover behavior to be boring and predictable.

If you run Direct Connect today, do two things this week:

1. Pick one service and define numeric success criteria for a BGP failover test.
2. Run a small FIS experiment with strict stop conditions and capture what happened end-to-end.

The bigger question to leave on: once you have repeatable failure data, what will you automate first—detection, remediation, or capacity optimization?