Optimizing Scheduling Problems in Cloud Computing Using a Multi-Objective Improved Genetic Algorithm
Year: 2025
Type: conference-paper
Abstract: On the other hand, the first method, which is referred to as the Multi-Objective Improved Genetic Algorithm (MOIGA), is focused on fostering the adoption of work scheduler via the efficient assigning of cloudlets to virtual machines (VMs). The use of the MOIGA algorithm is what allows this to be performed. When doing so, it takes into account a wide range of various goals, some of which are the expenses total creates, the execution time (ExT), and the wall clock time (WCT), amongst others. The approach investigates the most promising options while paying consideration the constraints imposed by the finances that are at one's disposal. The application of DNA objectives and the establishment of a measure of fitness that incorporates these objectives are the means by which this objective is realized. It is feasible to develop solutions that are practical via making use of this technology, even in circumstances whereby there are limited finances available. These solutions, offering significant advantages, have a significant impact on the area of cloud computing when they are implemented. They come with a number of important benefits. The MOIGA can be used to improve task organizing, which ultimately results in the greatest effective use of resources. This is accomplished by taking into account a wide range of distinct goals and limits on resources. When compared to other models like Round Robin (RR), Shortest Job First (SJF), First Come First Serve (FCFS), Multi-Objective Improved Genetic Algorithm (MOIGA) performs efficiently in terms of accuracy of 98%.
Institutions: Rajalakshmi Engineering College, RK University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4414349479