Difference Between Edge Computing and Distributed Computing
| Parameters | Edge Computing | Distributed Computing |
|---|---|---|
| Definition | Edge computing moves computation and data storage closer to the data source or end-users, typically at the network’s edge. | Distributed computing involves processing and data storage across multiple nodes or machines, usually in a network or cluster. |
| Cost Effectiveness | Costs of operations and maintenance are lower. | Costs of operations and maintenance are higher. |
| Location | Computing resources near data source/end-users, reducing latency & bandwidth needs. | Computing resources spread across nodes/machines, geographically dispersed. |
| Data Transfer | Edge computing minimizes data transfer to central servers/cloud, emphasizes localized data processing & analysis. | Distributed computing involves data transfer between nodes, coordinating tasks & exchanging information. |
| Scalability | Edge computing scales horizontally by adding more devices, improving system performance through load distribution. | Distributed computing scales horizontally by adding nodes, increasing capacity to handle larger workloads. |
| Security | Highly secure with data and Edge devices in proximity. | Multiple servers increase security vulnerability. |
| Computing Capability | Low | High |
| Data Processing Location | In the device itself | At Severs |
| Response Time | Low | High |
Difference Between Edge Computing and Distributed Computing
Edge computing and distributed computing are two computing approaches that aim to enhance performance, efficiency, and scalability. Edge computing focuses on placing computational resources, such as processing power and storage, closer to the data source or end-users. This proximity enables real-time data processing, reduces latency, and minimizes the need for data transfer to remote servers or the cloud. Edge computing is particularly beneficial for applications that require low latency, high responsiveness, and efficient bandwidth usage.