Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Abstract

Need for cloud computing has increased in the age of contemporary networked systems, driving the pursuit of optimal resource allocation and data processing. This is especially important in essential fields where security depends on computing performance, such as transportation systems. Even after much research has been done on the management of resources in cloud computing, finding algorithms that maximize job completion, minimize costs, and maximize resource consumption has remained a top priority. However, existing techniques have shown limitations, which calls for new ways. This is our work, which shown the novel hybrid approach that has the potential to completely change the game. The Neural Network Task Classification (N2TC) is the result of the merging of neural networks with genetic algorithms. This ground-breaking method skillfully applies the Genetic Algorithm Task Assignment (GATA) for resource allocation while utilizing neural networks for task categorization. Notably, our algorithm carefully considers execution time, response time, costs, and system efficiency in order to promote fairness, a defense against resource scarcity. Our method achieves a remarkable 13.3% cost reduction, a stunning 12.1% increase in response time, and a 3.2% increase in execution time. These strong indicators act as a wake-up call, announcing the power and revolutionary potential of our hybrid algorithm in transforming the paradigms around cloud-based task scheduling. This work represents a turning point in cloud computing, demonstrating an innovative combination of algorithms that not only overcomes current constraints but also ushers in a new era of efficacy and efficiency that has farreaching implications outside the domain of transportation systems.