(also edge computing)
Fog computing definition
Fog computing is a decentralized computing infrastructure in which data, compute, storage, and applications are located somewhere between the data source and the cloud. Rather than sending all data to a centralized cloud for processing, fog computing brings the computing closer (or to the “edge”) to the devices generating the data. This reduces the amount of data that needs to be transported, the consequent traffic, and the distance it has to travel, thereby improving efficiency and reducing the amount of latency.
Fog computing examples
- Internet of Things (IoT): Fog computing is widely used in IoT, where many devices generate large amounts of data that need real-time processing. For example, a smart thermostat controlling temperature in real-time.
- Autonomous vehicles: Self-driving cars require immediate processing of vast amounts of data for safe operation, which is made possible by fog computing.
Advantages and disadvantages of fog computing
- Reduced latency: By processing data closer to its source, fog computing reduces the time it takes for the data to travel, resulting in real-time or near-real-time insights.
- Improved privacy and security: The closer processing of data means less data transferred, reducing potential interception opportunities.
- Complexity: Fog computing involves managing more decentralized devices, making it more complex to manage and secure.
- Cost: While fog computing can be more efficient, the costs associated with setting up and maintaining these systems can be high.
Using fog computing
- Consider the need for real-time processing: Not every application needs the speed and real-time processing capabilities that fog computing provides.
- Ensure strong security measures: With increased complexity and decentralization, strong security measures are critical to protect data.