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.

云边缘技术的使用为降低成本、提高效率和资源管理创造了巨大的潜力。这些功能鼓励用户和组织在物联网 (IoT) 中使用智能联合云边缘范式。人力资源管理 (HRM) 是联合云边缘计算的重要挑战之一。由于边缘环境中的硬件和软件资源是为响应人类请求而分配的，因此根据服务质量 (QoS) 因素选择最佳资源是物联网环境中的关键和重要问题。HRM 可以被认为是一个 NP 问题，通过适当的选择、分配和监控资源，系统效率会提高，响应时间会减少。在这项研究中，提出了在云边缘计算中使用 Whale 优化算法 (WOA) 来解决 HRM 问题的优化模型。实验结果表明，与其他元启发式算法相比，所提出的模型能够在两种不同场景下提高最小响应时间、分配成本和增加分配的人力资源数量。