An efficient IoT task scheduling algorithm in cloud environment using modified Firefly algorithm

Internet and communication technologies now rely heavily on cloud computing, making it an indispensable component and allowing users to access infrastructure, platforms, and applications on a flexible, scalable, on-demand via a pay-per-use model. As an essential component of cloud computing, the scheduling algorithm controls many virtualized resources and plays a crucial role in providing efficient cloud services. Assigning Internet-of-Things (IoT) tasks to virtual machines (VMs) is one of the most significant issues and falls in the category of NP-Hard problems. This paper proposes a scheduler based on the Firefly Algorithm (FFA) to solve the IoT task scheduling problem in cloud computing efficiently. The modified Firefly Algorithm-based scheduler employs the transfer function (TF) and the Quantization technique to schedule IoT tasks on VMs in a cloud environment to minimize the makespan of IoT tasks. The proposed algorithm has been compared with the well-known Harris Hawks Optimization (HHO) and Differential Evolution (DE) algorithms. The simulation study demonstrates that the proposed algorithm outperforms HHO and DE algorithms regarding convergence speed and result quality.