Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT area. In this paper, using the Markov model learning method and calculating the need probability of each object to resources shortly to reduce latency and maximize network utilization, allocating resources in the fog layer has been possible and processed. By using simulations in the CloudSim platform, it is examined the processor productivity for the number of tasks, the workflow overhead for the number of tasks, physical machine’s energy consumption for the number of tasks, the data locality for the number of tasks, resource utilization for the number of tasks, and completion of task for the number of tasks and compared with the SMDP (SemiMarkov decision processes) and MDP methods, results show that the proposed research is effective and promising.