Edge Computing Reliability & Incident Response Benchmark

Welcome! This survey explores edge reliability, failure handling, and release practices across teams and organizations. Please answer based on your current workload(s). There are no right or wrong answers — we are interested in your honest experience. Participation is voluntary, and you may stop at any time. Your responses are confidential, anonymized, and reported only in aggregate. Results will be used for benchmarking research. Estimated time: 8–10 minutes.

Do you currently work with, manage, or make technical decisions about edge computing workloads?

• Yes
• No

Which edge use cases are you currently working on? Select all that apply.

• IoT/IIoT telemetry or control
• Video analytics or computer vision
• AR/VR or real-time interaction
• Retail POS or in-store systems
• Gaming or real-time multiplayer
• AI/ML inference at the edge
• Content delivery or CDN workers
• Offline-first mobile/web
• Autonomous/robotics
• Industrial gateways
• Other (please specify)

What overall availability target do you aim for on your most critical edge paths?

• No formal target
• < 99% (less than two 9s)
• 99% (two 9s)
• 99.5%
• 99.9% (three 9s)
• 99.95%
• 99.99% (four 9s)
• 99.999%+ (five 9s or higher)

In the past 90 days, which failure modes affected your edge workload? Select all that occurred.

• Network partition or high packet loss
• DNS or CDN routing issues
• Cold starts or warmup delays
• Certificate expiry or clock drift
• Configuration drift/mismatch
• Cache inconsistency or stale data
• Device resource exhaustion (CPU/RAM/storage)
• Upstream dependency outage
• Datastore write conflicts
• Inconsistent model versions at edge
• Timeout/retry storms
• OTA/update failure
• None of the above

Which signals do you actively monitor for edge reliability? Select all that apply.

• Latency percentiles (p50/p95/p99)
• Success/error rate
• Cold start rate
• Cache hit ratio
• Sync backlog size or queue depth
• Device heartbeat/uptime
• Resource usage (CPU/memory/disk)
• TLS/cert errors
• Offline duration per device/site
• Version drift across sites
• Custom business KPIs
• Other (please specify)

How often do you deploy changes to edge components?

• On every commit (continuous deployment)
• Daily
• Weekly
• Biweekly
• Monthly
• Less often

Rank the following areas by where investment would most reduce edge incidents for your team next quarter (most impactful at top).

1. Observability/monitoring
2. Pre-release testing at edge
3. Release safeguards (flags/canary/rollback)
4. Resilience patterns for offline/intermittent
5. Capacity and performance tuning
6. Runbooks/automation and on-call training

We'd like to explore your edge reliability practices in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your experience.

What is your primary role?

• Backend/Platform engineer
• Mobile/Web app engineer
• SRE/DevOps
• Data/ML engineer
• Edge/Embedded engineer
• Engineering manager/Tech lead
• Other (please specify)

Thank you for participating! Your input helps advance understanding of edge reliability practices across the industry. Results will be shared in aggregate form.

What is the primary runtime or environment for your edge workload?

• Serverless at edge (e.g., CDN workers)
• Embedded Linux on device
• RTOS / microcontroller
• On-prem edge gateway/appliance
• Containers on edge (e.g., K8s at edge)
• Mobile app (native/hybrid) with edge logic
• Browser service worker
• Other (please specify)

Do you maintain SLIs/SLOs specifically for edge components?

• Yes, for most edge components
• Yes, for critical paths only
• Partially defined
• No
• Not sure

Which patterns do you use to handle intermittent connectivity? Select all that apply.

• Write-behind with background sync
• CRDTs or conflict-free merges
• Local-first storage with reconciliation
• Event sourcing with replay
• Queued writes with exponential backoff
• Graceful degradation / limited offline mode
• Block writes until online
• None of the above
• Other (please specify)

How effective are your current alerts at promptly detecting edge incidents?

Which of the following pre-release practices do you perform for edge deployments? Select all that apply.

• Integration tests against edge environment
• Load/performance testing at edge
• Chaos/fault injection testing
• Connectivity/offline simulation testing
• Security/compliance scans
• Manual QA or smoke tests
• None of the above
• Other (please specify)

Based on your responses in this survey, please share any additional thoughts about your edge reliability challenges, priorities, or anything we may have missed.

How many years have you worked with edge workloads?

• Less than 1 year
• 1–2 years
• 3–5 years
• 6–10 years
• More than 10 years

What is your typical end-to-end latency target (p95) for critical edge requests?

• < 10 ms
• 10–50 ms
• 50–100 ms
• 100–250 ms
• 250–500 ms
• 500 ms–1 s
• > 1 s
• No defined target

When a major edge degradation occurs, rank your team's typical response actions in the order you would perform them (first action at top).

1. Rollback or disable via feature flag
2. Shift traffic to cloud fallback
3. Degrade UX gracefully (reduced functionality)
4. Increase cache TTL / serve stale on error
5. Apply backpressure / tighter rate limits
6. Trip circuit breakers to isolate faults

Which safeguards are part of your edge release process? Select all that apply.

• Feature flags
• Staged rollouts
• Canary by PoP/region/site
• Auto-rollback on SLO breach
• Policy checks in CI/CD
• Two-person review/approval
• Signed releases/attestations
• SBOM/vulnerability scan gates
• None of the above
• Other (please specify)

How many employees are in your organization?

• 1–10
• 11–50
• 51–200
• 201–1,000
• 1,001–5,000
• 5,001–10,000
• 10,001+

What is your typical acceptable error rate target for edge services?

• < 0.01%
• 0.01–0.1%
• 0.1–0.5%
• 0.5–1%
• 1–5%
• > 5%
• No defined target

What is your organization's primary industry?

• Technology
• Retail/E-commerce
• Manufacturing
• Media/Gaming
• Telecom
• Transportation/Logistics
• Healthcare
• Finance
• Public sector
• Other (please specify)

At approximately what end-user error rate would you typically trigger a rollback for an edge change?

• < 0.1%
• 0.1–0.5%
• 0.5–1%
• 1–2%
• 2–5%
• > 5%
• No defined rollback threshold
• It depends on the service/path

In which regions do you primarily operate edge workloads? Select all that apply.

• North America
• Europe
• APAC
• LATAM
• Middle East
• Africa
• Global/multi-region

Approximately how many active edge sites or devices do you manage?

• 1–10
• 11–50
• 51–200
• 201–1,000
• 1,001–10,000
• 10,001–100,000
• 100,001+