Neural network inspired differential evolution based task scheduling for cloud infrastructure

In recent years, cloud computing has become an essential technology for businesses and individuals alike. Task scheduling is a critical aspect of cloud computing that affects the perfor-mance and efficiency of cloud infrastructure. During this pandemic where most of the healthcare services like COVID-19 sampling, vaccination process, patient management and other services are dependent on cloud infrastructure. These services come with huge clients and server load in a small instance of time. These task loads can only be managed at cloud infrastructure where an effi-cient resource management algorithm plays an important role. The optimal utilization of cloud infrastructure and optimization algorithms plays a vital role. The cloud resources rely on the allo-cation policy of the tasks on cloud resources. Simple static, dynamic, and meta-heuristic techniques provide a solution but not the optimal solution. In such a scenario machine learning and evolution-ary algorithms are only the solution. In this work, a hybrid model based on meta-heuristic tech-nique and neural network is proposed. The presented neural network inspired differential evolution hybrid technique provides an optimal assignment of the tasks on cloud infrastructure. The performance of the DE-ANN hybrid approach is performed using performance metrics, aver-age start time(ms), average finish time(ms), average execution time(ms), total completion time(ms), simulation time(ms), and average resource utilization respectively. The proposed DE-ANN approach is validated against BB-BC, and Genetic approaches. It outperforms the existing meta -heuristic techniques i.e. Genetic approach, and Big-Bang Big-Crunch. The performance is evaluated using two configuration scenarios using 5 virtual machines and 10 virtual machines with varying tasks from 1000 to 4500. Experimental results show that the DE-ANN technique significantly improves task scheduling performance compared to other traditional techniques. The technique achieves an average improvement of 19.15% in total completion time(ms), 32.23% in average finish time(ms), 51.95% in average execution time(ms), and 33.24% in average resource utilization respec-tively. The DE-ANN technique is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures. (c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).