Firefly Optimization Algorithm for Multi-Objective Job Scheduling in Cloud Computing

Due to the increasing use of the Internet of Things, efficient task scheduling in cloud computing has become increasingly important with the aim of maximizing the use of available resources, reducing energy consumption, and enhancing the quality of service (QoS). In this paper, we use the Firefly Optimization (FFO) algorithm to improve scheduling efficiency and minimize the overall completion time in cloud environments. For this purpose, twelve distinct scenarios were designed in the Cooja Contiki simulator environment with the perspective of computationally intensive, input/output intensive, and mixed workloads, and the overall completion time results obtained with the Min-Min and GA-PSO-Min methods were compared and the better performance of the method was confirmed. Due to the increasing use of the Internet of Things, efficient task scheduling in cloud computing has become increasingly important with the aim of maximizing the use of available resources, reducing energy consumption, and enhancing the quality of service (QoS). In this paper, we use the Firefly Optimization (FFO) algorithm to improve scheduling efficiency and minimize the overall completion time in cloud environments. For this purpose, twelve distinct scenarios were designed in the Cooja Contiki simulator environment with the perspective of computationally intensive, input/output intensive, and mixed workloads, and the overall completion time results obtained with the Min-Min and GA-PSO-Min methods were compared and the better performance of the method was confirmed.

Keywords:

Firefly Optimization Algorithm , Internet of Things , Cloud Computing , Job Scheduling , Total Time Spent , Efficiency in Energy Use , Scalability and Multi-Objective Optimization