Mehdi Beyzaee Nejad - Master's degree in software engineering from Basir University

Internet of Things has increased the amount of generated data in the computer networks. Fog computingaims to curb such amounts of data. Timing in distributed environments is a NP-hard problem. In thispaper, timing in fog has been done through Bayesian classification. Bayesian classification has beenapplied to obtain processing requirements of tasks. After classification, virtual machines will be createdaccording to each class. iFogsim simulator has been used to check the performance of the proposedmethod in the fog environment. Algorithms have been evaluated upon the program of brain signaltracking system. According to the results, smart decision making in a cloud environment which iscombined with fog has been caused to reduction in energy consumption in the cloud, the cost of executingthe task in the cloud and the average energy consumption of mobile devices.

fog computing, tasks timing, machine learning, Bayesian classification