Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.2ArchitectureMedium
A manufacturing company is integrating new IoT sensors on its factory floor. These sensors generate a high volume of time-sensitive data that needs to be processed close to the source to enable real-time anomaly detection and control robotic arms without significant latency. The processed data then needs to be aggregated and sent to a central cloud for long-term storage and analytics. Which architectural concept is best suited for this scenario?
- AData center virtualization
- BServerless computing
- CEdge computing
- DCentralized cloud computing
Show answer & explanationAnswer & explanation
Correct answer: C. Edge computing
Edge computing involves processing data closer to the source (the IoT sensors on the factory floor in this case), which is crucial for reducing latency and enabling real-time actions like controlling robotic arms. The processed data can then be sent to a central cloud for further analysis and storage, aligning perfectly with the scenario.
Why the other options are wrong
- A. Data center virtualization focuses on optimizing resource utilization within a data center, not on processing data at the network edge.
- B. Serverless computing is a cloud execution model that does not inherently address the need for processing data physically close to IoT devices for low latency.
- D. Centralized cloud computing would introduce unacceptable latency for real-time control of robotic arms.
Edge Computing
A distributed computing paradigm that brings computation and data storage closer to the sources of data, reducing latency and bandwidth usage.
- Processes data near the source (e.g., IoT devices).
- Reduces latency for real-time applications.
- Optimizes bandwidth by sending only aggregated data to the cloud.
- Enhances security by processing sensitive data locally.
Memory trick: Distributed Computing: Cloud, Edge, Data, Serverless - Where's the brain?