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.
