A framework for IoT-based Resource Scheduling in the Cloud

Abstract

Scheduling the resources is a fundamental part of the cloud environment. The facilitation of an efficient scheduling approach is a challenging issue. An ideal resource allocation framework that utilizes square-fuzzy methodology and radial basis function network (RBFN) is discussed here. The main motive of this model is to reduce communication and computation costs. The sensor devices in the IoT sensor layer are clustered initially. Further, the sensor data gathered by cluster heads are transferred to the fog layer. The network traffic is optimized through the fuzzy technique. The energy consumption of the network is reduced by the fog layer and then the data is transferred to the cloud where essential attributes of the cloud server and input are used for scheduling of resources by utilizing modified RBFN. The proposed methodology is analyzed and compared with existing models.