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Edge Decision Patterns
Edge systems are not just about processing data, but deciding what to do locally vs what to send to cloud.
Patterns
Filter at Edge
- Send only important data
- Example: Send temperature only if > 50°C
Aggregate at Edge
- Combine data before sending
- Example: Send hourly average instead of raw stream
Act at Edge
- Immediate action without cloud
- Example: Turn off machine if overheating
Forward to Cloud
- Send raw or enriched data for analytics
- Example: ML training data
Why it matters
- Reduces bandwidth
- Improves latency
- Avoids cloud dependency
Offline-First Edge Systems
Edge systems must assume network failure is normal.
Key Concepts
Local Buffering
- Store data locally when network is down
Retry Mechanisms
- Send data when connection is restored
Eventual Sync
- Edge and cloud will sync later
Example
A delivery truck loses connectivity:
- Continues tracking locally
- Syncs data when back online
Risk
- Data duplication
- Out-of-order events
Data Reduction at Edge
Sending all raw data to cloud is expensive and unnecessary.
Techniques
Sampling
- Send every Nth record
Thresholding
- Send only when values cross limits
Compression
- Reduce payload size
Feature Extraction
- Send insights instead of raw data
- Example: send “anomaly detected” instead of full signal
Benefit
- Lower bandwidth cost
- Faster processing
Edge AI (Inference at Edge)
Edge devices can run ML models locally.
What runs at Edge
- Image classification
- Anomaly detection
- Voice recognition
What stays in Cloud
- Model training
- Heavy computation
- Model updates
Example
Security camera:
- Detects person locally
- Sends alert instead of full video
Challenge
- Limited compute power
- Model updates across devices
Edge Failure Scenarios
Edge systems fail differently than cloud systems.
Common Failures
Device Failure
- Hardware crash
Network Loss
- No connectivity to cloud
Data Loss
- Buffer overflow or corruption
Clock Drift
- Incorrect timestamps
Design Considerations
- Retry logic
- Local storage
- Idempotent processing
- Time synchronization
Edge vs Fog vs Cloud
Edge
- Closest to device
- Real-time decisions
- Limited compute
Fog
- Intermediate layer
- Aggregation and coordination
Cloud
- Centralized
- Storage, analytics, ML training
Example
Smart factory:
- Edge: machine sensor detects anomaly
- Fog: aggregates factory data
- Cloud: long-term analytics
Event-Driven Edge Systems
Edge systems are typically event-driven.
What is an Event?
A change or trigger:
- Temperature exceeds threshold
- Motion detected
- Device status change
Flow
Device → Event → Edge Processing → Action / Cloud
Example
Motion sensor:
- Detects movement
- Triggers camera recording
- Sends alert
Benefit
- Efficient processing
- Real-time response