Fog Computing
Updated 2026-09-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Fog computing was proposed by Cisco to extend cloud computing toward the network edge, placing compute, storage, and networking capability in gateways, routers, and base stations close to the data source. The core claim is that processing near the source cuts latency and bandwidth consumption, which matters when devices generate data faster than the network can carry it. The qualification is that fog nodes complement rather than replace the cloud: global analysis, long-term storage, and model training still belong centrally, so the design question is what to push down and what to keep.
SCAFFOLDING EFFECT
Reduce cognitive load
- Latency triage: find the decisions that must be made faster than a round trip to the center allows. - Placement choice: decide which processing belongs at the edge and which belongs centrally. - Delegation metaphor: push authority to front-line nodes when the center cannot respond in time.
Anchor fast decisions
Sending every reading to a central site consumes bandwidth and adds round-trip delay, so decisions that depend on immediate context arrive too late to be useful. Processing at the edge removes the round trip for those decisions while filtering data so that only the summaries travel upward. The centre then handles the aggregate view, which needs breadth rather than speed, so the split matches each layer to the question it can answer.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- wiki.mbalib.comhttps://wiki.mbalib.com/wiki/%E9%9B%BE%E8%AE%A1%E7%AE%97verified
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