A cloud-fog computing system for classification and scheduling the information-centric IoT applications

Abstract

Cloud users submit their applications for processing on the provider's infrastructure expecting a diverse set of computational, storage, or communicational resources. Classifying the tasks of these applications based on their needs and scheduling them onto the most suitable resources is a big obstacle. To manage with such issues, a system that supports classification and scheduling them on to the most efficient resource for execution is essential. An information-centric (IC) internet of things (IoT) system for cloud-fog computing environment, which supports these two key functionalities through ensured QoS was proposed in this article. The experimental simulations and performance analysis of the proposed approach was prepared by considering a global cloud with three servers, four fogs, and four types of IoT applications with a sum of 3,000 tasks collectively. Experimental outcomes state that the anticipated cloud-fog computing architecture reduces the average makespan time and service cost up to a maximum of 11.8%, and 12.63% respectively when compared with other existing approaches. Also, the cost-makespan trade-off (CMT) gains with the proposed CSIc_IoTA is up to 28.5% and can guarantee better QoS requirements of real-time IoT applications.