QoS Based Optimal Resource Allocation and Workload Balancing for Fog Enabled IoT

Abstract This paper is aimed at efficiently distributing workload between the Fog Layer and Cloud Network then optimizing resource allocation in cloud networks to ensure better utilization quick response time of resources available end user. We have employed a Dead-line aware scheme migrate data based on profiling used K-Means clustering Service-request prediction model allocate all requests. To substantiate our model, we iFogSim, which an extension CloudSim simulator. The results clearly show that when optimized network Quality Service parameters exhibit efficiency output.