An optimized human resource management model for cloud-edge computing in the internet of things

The use of cloud-edge technology creates significant potential for cost reduction, efficiency and resource management. These features have encouraged users and organizations to use intelligence federated cloud-edge paradigm in Internet of Things (IoT). Human Resource Management (HRM) is one of the important challenges in federated cloud-edge computing. Since hardware and software resources in the edge environment are allocated for responding human requests, selecting optimal resources based on Quality of Service (QoS) factors is a critical and important issue in the IoT environments. The HRM can be considered as an NP-problem in a way that with proper selection, allocation and monitoring resource, system efficiency increases and response time decreases. In this study, an optimization model is presented for the HRM problem using Whale Optimization Algorithm (WOA) in cloud-edge computing. Experimental results show that the proposed model was able to improve minimum response time, cost of allocation and increasing number of allocated human resources in two different scenarios compared to the other meta-heuristic algorithms.