Welcome to the InfoSci Platform
Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems

Jiguang Duan, Yan Li, Liying Duan, Amit Sharma
Copyright: © 2022 |Volume: 17 |Issue: 1 |Pages: 15
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799894001|DOI: 10.4018/IJITWE.306915
Cite Article Cite Article

MLA

Duan, Jiguang, et al. "Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems." IJITWE vol.17, no.1 2022: pp.1-15. http://doi.org/10.4018/IJITWE.306915

APA

Duan, J., Li, Y., Duan, L., & Sharma, A. (2022). Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems. International Journal of Information Technology and Web Engineering (IJITWE), 17(1), 1-15. http://doi.org/10.4018/IJITWE.306915

Chicago

Duan, Jiguang, et al. "Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems," International Journal of Information Technology and Web Engineering (IJITWE) 17, no.1: 1-15. http://doi.org/10.4018/IJITWE.306915

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The cloud computing platforms are being deployed nowadays for resource scheduling of real time data intensive applications. Cloud computing still deals with the challenge of time oriented effective scheduling for resource allocation, while striving to provide the efficient quality of service. This article proposes a time prioritization-based ensemble resource management and Ant Colony based optimization (ERM-ACO) algorithm in order to aid effective resource allocation and scheduling mechanism which specifically deals with the task group feasibility, assessing and selecting the computing and the storage resources required to perform specific tasks. The research outcomes are obtained in terms of time-effective demand fulfillment rate, average response time as well as resource utilization time considering various grouping mechanisms based on data arrival intensity consideration. The proposed framework when compared to the present state-of-the-art methods, optimal fitness percentage of 98% is observed signifying the feasible outcomes for real-time scenarios.