The Application of Wireless Network Technology in the Capacity Building of Anhui Industry Associations Based on the Characteristics of Big Data

Abstract

Recent advances of wireless networks and communication technologies, integrated into the considerable development in the field of environmental policies and public participation, have resulted in the development of wireless network-enabled big data environment in the capacity building of Anhui industry association. At the same time, effective task scheduling in the cloud server based big data environment is essential to schedule the tasks accurately and rapidly. There are thousands of jobs to be executed by the resources available on cloud data centers to achieve minimum time, high performance, and the proper utilization of CPU and resources. The necessity to fulfil user requirements is the main reason of having studies for optimizing the cloud computing of big data in terms of latency, bandwidth, execution time, and resource utilization. Therefore, this work presents a crow search optimization-based task scheduling scheme (CSO-TSS) for capacity building of Anhui industry association on wireless network-enabled big data environment. The proposed CSO-TSS technique mainly intends to schedule the tasks exist in the wireless network-enabled big data environment. Besides, the CSO-TSS technique is executed on a MapReduce environment in order to proficiently handle the big data. In addition, the CSO-TSS technique derives an objective function intending to maximize resource utilization and minimize execution time of the tasks. For examining the improved task scheduling performance of the CSO-TSS technique, a wide range of simulations were carried out and the results are investigated under several aspects. The comparative result analysis stated the better outcomes of the CSO-TSS technique over the recent approaches in terms of different measures.