Fog Computing: Issues, Challenges and Future Directions

In Cloud Computing, all the processing of the data collected by the node is done in the central server. This involves a lot of time as data has to be transferred from the node to central server before the processing of data can be done in the server. Also it is not practical to stream terabytes of data from the node to the cloud and back. To overcome these disadvantages, an extension of cloud computing, known as fog computing, is introduced. In this, the processing of data is done completely in the node if the data does not require higher computing power and is done partially if the data requires high computing power, after which the data is transferred to the central server for the remaining computations. This greatly reduces the time involved in the process and is more efficient as the central server is not overloaded. Fog is quite useful in geographically dispersed areas where connectivity can be irregular. The ideal use case requires intelligence near the edge where ultra-low latency is critical, and is promised by fog computing. The concepts of cloud computing and fog computing will be explored and their features will be contrasted to understand which is more efficient and better suited for real-time application