A New Fuzzy-Based Method For Load Balancing In The Cloud-Based Internet Of Things Using A Grey Wolf Optimization Algorithm

Cloud computing provides high accessibility, scalability, and flexibility in the era of computing for different practical applications. Internet of things (IoT) is a new technology that connects the devices and things to provide user required services. Due to data and information upsurge on IoT, cloud computing is usually used for managing these data, which is known as cloud-based IoT. Due to the high volume of requirements, service diversity is one of the critical challenges in cloud-based IoT. Since the load balancing issue is one of the NP-hard problems in heterogeneous environments, this article provides a new method for response time reduction using a well-known grey wolf optimization algorithm. In this paper, we supposed that the response time is the same as the execution time of all the tasks that this parameter must be minimized. The way is determining the status of virtual machines based on the current load. Then the tasks will be removed from the machine with the additional load depending on the condition of the virtual machine and will be transferred to the appropriate virtual machine, which is the criterion for assigning the task to the virtual machine based on the least distance. The results of the CloudSim simulation environment showed that the response time is developed in compared to the HBB-LB and EBCA-LB algorithm. Also, the load imbalancing degree is improved in comparison to TSLBACO and HJSA.