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Device estate
Source of truth: physical world
Edge-Fog-Cloud Architecture Comparison Tool
Compare IoT workload placement across edge, fog, and cloud tiers using latency, bandwidth, privacy, and resilience evidence.
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Learner-ready edge-fog-cloud architecture comparison workbench with scenario presets, task placement controls, animated tier flow, data reduction math, live metrics, and decision guidance.
Architecture
Placement
Latency and traffic
Place each IoT task where the evidence says it belongs.
Edge, fog, and cloud are not competing products. They are cooperating tiers. Use the presets, move tasks between tiers, and watch how latency, upstream traffic, cloud exposure, and resilience change together.
Hybrid
Tier mix
-- ms
Critical loop
-- MB/s
Cloud traffic
Checking
Deadline fit
Animated tier comparison
Step through the same event as it is filtered, aggregated, analyzed, stored, and converted into a control action.
EDGE
Edge
Load --
FOG
Fog
Load --
CLOUD
Cloud
Load --
Raw event captured
The device estate produces readings, frames, or control signals before any tier has reduced the data.
Task placement
Move each task using the tier buttons. The metrics update immediately.
Evidence board
These are teaching estimates, but the units and relationships match the architecture trade-off.
-- ms
Critical decision loop
-- ms
Risk-adjusted p95 estimate
-- MB/s
Traffic reaching fog
-- GB/mo
Cloud data volume
--%
Raw data exposed upstream
--/100
Local resilience score
Learning support
Edge
Use edge when a control loop cannot wait for a network round trip, when raw data should stay local, or when the device can keep operating while disconnected.
- Typical loop shown here: 1-10 ms.
- Trade-off: limited compute, storage, and fleet visibility.
Fog
Use fog for gateway, plant, building, campus, or regional decisions that need more context than one device but less delay than cloud.
- Typical loop shown here: 10-100 ms.
- Trade-off: shared infrastructure must be managed and secured.
Cloud
Use cloud for historical storage, fleet analytics, model training, dashboards, and jobs where elasticity matters more than immediate control.
- Typical loop shown here: 100-500 ms.
- Trade-off: WAN dependency, higher upstream exposure, and possible bandwidth cost.
How the estimates are calculated
raw MB/s = events per second x payload KB / 1024.
traffic to fog applies only reductions completed at the edge before data leaves the local device estate. traffic to cloud applies reductions completed at edge or fog before cloud upload.
critical loop latency = round-trip transport to the alert or inference tier + processing time. Edge transport is modeled as 4 ms, fog as 28 ms, and cloud as 175 ms before processing.
risk-adjusted p95 is a teaching estimate that increases the loop latency when WAN risk is high. It is not a measured service-level objective.
Practice prompts
- Start with Video analytics. Move inference from edge to cloud. Explain what changed first: latency, cloud traffic, or resilience.
- Start with Building sensors. Raise WAN risk above 30%. Decide which tasks should stay local.
- Start with Historical analytics. Lower the latency deadline to 50 ms. Identify why the cloud-centered pattern no longer fits.
Technical accuracy notes
The numbers are intentionally bounded teaching estimates, not vendor benchmarks. Real deployments must measure radio airtime, gateway queueing, TLS setup, broker behavior, cloud region distance, model runtime, batching, and retries.
Hybrid is normal. Production IoT often filters at edge, aggregates at fog, and sends compact facts to cloud for storage, dashboards, and model improvement.