Edge Computing and Its Integration with the Cloud
Edge computing is a distributed computing paradigm that involves processing data closer to the data source or "edge" of the network, rather than sending all data to a centralized cloud server.
Edge computing complements cloud computing in IoT in the following ways:
Low Latency
- IoT applications often require real-time or near-real-time responses, such as autonomous vehicles, industrial control systems, and healthcare monitoring.
- By processing data at the edge, latency is reduced because data doesn't need to travel to a distant cloud server for analysis.
Reduced Bandwidth Usage
- Sending all IoT data to the cloud can strain network bandwidth and increase data transfer costs.
- Edge devices can filter, aggregate, and preprocess data locally, sending only relevant information to the cloud.
Privacy and Compliance
- Some data generated by IoT devices may be sensitive or subject to regulatory compliance.
- Edge computing allows organizations to keep sensitive data local and only transmit aggregated, non-sensitive insights to the cloud.
High Availability
- Edge computing can provide fault tolerance and resilience by continuing to process data even when connectivity to the cloud is interrupted.
- This is crucial for mission-critical IoT applications.
Hybrid Architectures
- Many IoT solutions use a hybrid approach, combining cloud and edge computing.
- Devices at the edge perform initial data processing and filtering, while the cloud handles more intensive analytics, long-term storage, and centralized management.
Data Preprocessing at the Edge
- IoT devices can preprocess and filter data at the edge before sending relevant information to the cloud.
- This reduces the volume of data sent to the cloud and allows for faster, localized decision-making.
- Devices like sensors and cameras can perform initial data reduction and analysis.
Edge Analytics
- Edge servers or gateways can host analytics models that provide immediate insights and trigger real-time actions based on IoT data.
- For instance, a manufacturing machine with IoT sensors can use edge analytics to detect anomalies and stop production when issues are identified.
Data Storage in the Cloud
- Cloud resources are still valuable for long-term storage, advanced analytics, and cross-device data aggregation.
- Data from IoT devices can be archived in the cloud for historical analysis, predictive maintenance, and trend analysis.
Conclusion
You can read other articles written by me through these links.
System Design Series
Introduction To Parallel Computing
Deep Dive Into Virtualization
Insights Into Distributed Computing
Cloud Computing Series
1. Cloud Service Models
2. Cloud Deployment Models
3. Cloud Security
4. Cloud Architecture
5. Cloud Storage
6. Networking In The Cloud
7. Cloud Cost Management
8. DevOps In Cloud & CI/CD
9. Serverless Computing
10. Container Orchestration
11. Cloud Migration
12. Cloud Monitoring & Management
13. Edge Computing In Cloud
14. Machine Learning In Cloud
Computer Networking Series
1. Computer Networking Fundamentals
2. OSI Model
3. TCP/IP Model : Application Layer
4. TCP/IP Model : Transport Layer
5. TCP/IP Model : Network Layer
6. TCP/IP Model : Data Link Layer
Version Control Series
1. Complete Guide to Git Commands
2. Create & Merge Pull Requests
3. Making Open Source Contributions
Linux
Complete Guide to Linux Commands
Thanks For Reading! 💙
Garvit Singh