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

Abstract

Cloud computing platforms are being deployed 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. 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.