The Internet of things increases information volume in computer networks and the concept of fog will help us to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem. This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain the task requirements like the processing ones. After classification, virtual machines will be created in accordance with the predicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling (FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on a practical application of brain signal tracking system. According to the results, the FBCS method, compared with other methods, has reduced the energy consumption in the cloud and the executing task cost in cloud; and also the average of energy consuming in mobiles has been decreased by smart decision making.
Fog computing, tasks scheduling, machine learning, Bayesian classification
HEYDARI, GHOLAMREZA; RAHBARI, DADMEHR; and NICKRAY, MOHSEN
"Energy saving scheduling in a fog-based IoT application by Bayesian task classification approach,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
6, Article 10.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss6/10