Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

作     者：Zhanyang Xu Dawei Zhu Jinhui Chen Baohua Yu Zhanyang Xu;Dawei Zhu;Jinhui Chen;Baohua Yu

作者机构：School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina College of Information Science and TechnologyShihezi UniversityXinjiangChina 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2022年第8卷第4期

页      面：476-484页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps under grant no.2020DB005 and no.2017DB005 

主　　题：IoT-based power system Cloud Workflow Machine learning NSDE 

摘      要：Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things(IoT),machine learning technology has been adopted to deal with the data-intensive power electronics applications in *** feeding previous power electronic data into the learning model,accurate information is drawn,and the quality of IoT-based power services is ***,the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow *** efficient execution of this data-intensive Power Workflow(PW)needs massive computing resources,which are available in the cloud ***,the execution efficiency of PW decreases due to inappropriate sub-task and data *** addition,the power consumption explodes due to massive data *** address these challenges,a PW placement method named PWP is ***,the Non-dominated Sorting Differential Evolution(NSDE)is used to generate placement *** simulation experiments show that PWP achieves the best trade-off among data acquisition time,power consumption,load distribution and privacy preservation,confirming that PWP is effective for the placement problem.