Energy-efficient task scheduling algorithms on heterogeneous computers with continuous and discrete speeds

A large number of computing servers and personal electronic devices waste a tremendous amount of energy and emit a considerable amount of carbon dioxide, which is the major contribution to the greenhouse effect. Thus, it is necessary to significantly reduce pollution and substantially lower energy usage. Green computing techniques are utilized in a myriad of applications in energy conservation and environment improvement. New green task scheduling algorithms for heterogeneous computers with changeable continuous speeds and changeable discrete speeds are developed to reduce energy consumption as much as possible and finish all tasks before a deadline. A newly proven theorem can determine the optimal speed for tasks assigned to a computer with continuous speeds. This project seeks to develop innovative green task scheduling algorithms that have two main steps: heuristically assigning tasks to computers, and setting optimal or near-optimal speeds for all tasks assigned to each computer. Sufficient simulation results indicate that the algorithm with the best task schedule varied. Thus, two hybrid algorithms for continuous and discrete speeds are created separately to obtain the best task schedule among candidate task schedules. Potential research applications include incorporating energy-efficient software into mobile devices, sensor networks, data centers, and cloud computing systems.