Performance evaluation of an adopted model based on big-bang big-crunch and artificial neural network for cloud applications
DOI:
https://doi.org/10.48129/kjs.v48i4.9664Keywords:
Big-Bang Big-Crunch, Task, Computing, Fitness, Genetic, MIPS, PSO, SJF, SLA,Abstract
High-performance computing changes the way of computing. With emerging technologies like grid computing, cloud computing and smart health care application has changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside cloud there lies many issues related to efficient and cost-effective model to improve cloud performance and complete the client task with least cost and high performance. E-Health care services are one of most computational intensive services in cloud, they require real time computing which can only be achieved if the computational resources can compute it in least time. Cloud can accomplish this using efficient scheduling algorithm. This article focuses on task scheduling policy which aims to improve the performance in real-time with the least execution time, network cost and execution cost-effective at the same time. The proposed model is inspired from Big-Bang Big-Crunch algorithm in astrology. The work has been compared with a genetic algorithm, Particle swarm optimization and TOPSIS algorithm. The result shows that the proposed algorithm performs better than existing approaches taking execution time, network delay and execution cost into considerations. The results are validated using real-time data set and (standard workload file) from workload traces (San Diego Supercomputer Center (SDSC) Blue Horizon logs), and fabricated data sets. The proposed algorithm aims to improve the performance by reducing the scheduling delay, network delay with the least resource cost to complete the task in least cost to the user with high quality of service. The proposed model is to improve the performance of the system in real time and reduce the computing cost for users. The proposed system also aims to provide better quality of services to the user with optimal computing environment and high reliability for large scale application in smart health care.
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