IPORM: A Resource Management Scheme for Fog-Enabled Cloud Environments: An Improved Political Optimizer-Based Approach

Abstract

In recent years, with the rapid growth of the internet of things (IoT), the number of devices connected to the network has increased significantly. The rising trend of mass data that objects send to the network has become a big challenge for cloud networks. The next generation IoT applications require low delay and real-time responses. Hence, IoT applications need a new processing paradigm to be able to access distributed cloud services at the network edge. In recent years, fog computing has emerged as a distributed computing model compatible with heterogeneous environments that can provide cloud services with low delay at the network edge. Meanwhile, services provided by the IoT suffer from varying workload changes over time, and approaches are needed to automatically provision resources and satisfy quality of service (QoS) requirements. With this motivation, this study presents an improved political optimizer-based resource management scheme for fog-enabled cloud environments, which is named IPORM. IPORM seeks to efficiently allocate resources to IoT requests with the purpose of maximizing resource utilization. In addition, other objectives such as energy, bandwidth, and delay are also included in IPORM. Simulation results show that our scheme significantly improves network performance in terms of various metrics compared to its counterparts.