Classification and Scheduling of Information-Centric IoT Applications in Cloud- Fog Computing Architecture (CS_IcIoTA)

Materialization in Internet-of-Things (IoT) has exponentially raised the usage of smart devices by the individuals or business organizations. Fog computing was introduced for serving the raising needs of IoT applications locally with minimal delay and cost. Based on the Quality of Service (QoS) requirements like data requirements, rate of data updating, and accessing authority of IoT applications, their requests may be processed on the locally available fog nodes at low cost or forwarded to the globally available rentedcloud-nodes for processing at higher cost. Hence, there is a key need of optimizing the information-centric IoT architecture for classifying the tasks of IoT applications and scheduling them on to the most suitable fog or cloud nodes for processing. The proposed CS_IcIoTA identifies the application needs and classifies them into diverse categories. The scheduler assigns the tasks from these categories either to the local fog nodes or to the remotely available rented-cloud-nodes for execution based on the current resource requirements of tasks. If the demanded computing or storage resources by the tasks is huge and if that is not attainable at fogs then cloud nodes are preferred otherwise local fog nodes are used. Three cloud nodes, four fog nodes and three IoT application domains with a sum of 1500 tasks are considered for the experimental analysis and performance evaluation. Simulation results states that the proposed CS_IcIoTA minimizes the average makespan time and service-cost up to 11.45%, and 10.60% respectively. Proposed CS_IcIoTA also maximizes the average fog node utilization up to 77.83%.