Neural Network-Based Resource Allocation Model in Multilayered Cloud

High-performance computing is changing computing. With emerging technologies like grid computing, cloud computing applications have changed the way we compute and communicate. Cloud computing has made computing huge amounts of data on the fly possible and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with a pay per use model so that users pay for only those resources that they have used. Inside cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. Cloud is meant to deal with the most computationally intensive services; they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This chapter focuses on task scheduling policy which aims to improve the performance in real-time with the least execution time, network cost and cost-effective performance parameters. The proposed model is inspired by the Big Bang–Big Crunch algorithm in astronomy. The proposed algorithm aims to improve the performance by reducing the scheduling delays and network delays with the least resource cost to complete the task at the least cost to the user with high quality of service.