Task Scheduling in IoT Cloud-Fog Environment Utilising a Hybrid Method and Firefly Algorithm

The Internet of Things refers to a vast and interconnected distributed systems in which components have a high degree of heterogeneity in terms of software, hardware, and connectivity, while they deliver different services. The quality of service for users accessing the Internet of Things supported by cloud computing is increasing exponentially. However, the deployment of Internet of Things applications in a cloud environment can be very dynamic, resulting in service demands and high resource requirements. The task of effectively managing and optimising resource allocation while taking into account time-sensitive requests is a significant and complex problem that has implications on service quality. The proposed solution aims to improve task scheduling in a hybrid cloud environment for Internet of Things applications. This improvement involves implementing load-balancing techniques to achieve cost reduction, energy consumption, and enhanced overall execution time. The designed solution encompasses two main phases. In the first phase, clustering is performed on computing hosts. In the subsequent phase, user requests are assigned to a suitable cluster, utilising the enhanced Firefly algorithm. The simulation results demonstrated the efficiency of the proposed solution in terms of energy consumption and execution time compared to a benchmark algorithm.