Developing a Hybrid Heuristic and Metaheuristic Scheduling Algorithm (HHMH) for Cloud-Fog Computing Environment

Abstract

Constant Internet of Things (IoT) developments has been producing vast volumes of data in recent years, placing pressure on the infrastructure of cloud computing (CC). To meet the criteria set by the IoT devices, the Fog computing (FC) architecture is considered the next generation of CC. It consequently removes the big issue of higher delays in the cloud-IoT paradigm, but CC cannot be replaced by FC. The processing power of the Fog Node is limited and the cloud must be added to form a Cloud-Fog (CF) computing model. Inefficient scheduling of user tasks in CF computing can result in higher response time and execution time and also minimize the throughput. Therefore, it is only possible to obtain the real benefits of CF computing by applying efficient task scheduling (TS) strategies. Because of this, the thesis proposes an efficient hybrid scheduling algorithm (HHMH) for CF computing, which is a hybrid of Modified Particle Swarm Optimization (MPSO) and First Come First Serve (FCFS). These algorithms have certain drawbacks. FCFS algorithm is the simplest task scheduling technique but it has the problem of inefficient resource utilization and long waiting time. MPSO has a problem with response time, execution time, and throughput when the task and environment are heterogeneous. The key aim of this thesis is to minimize the average response time and average execution time, to improve the usage of VMs, and to increase throughput by scheduling the tasks effectively. The proposed algorithm was tested and simulated on Cloudsim by using 11 datasets of 40 up to 500 number tasks with different sizes. Experimental results show that compared to MPSO, FCFS, and SJF, the developed HHMH algorithm lowers the average response time by 54.91%, 11.5%, and 2% for 500 tasks respectively. For 500 tasks, we measured the throughput and found that the developed HHMH algorithm performs 2.19 tasks per second, while MPSO performs 2.01 tasks per second, FCFS performs about 1.88 tasks per second, and SJF performs 2.03 tasks per second. Calculated execution time and VM usage of the developed HHMH algorithm is also better.