Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments
作者机构：Division of Intelligence and ComputingTianjin UniversityTianjin300072China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream DataNorth China University of TechnologyBeijing10009China Cloud Computing Research CenterNorth China University of TechnologyBeijing10009China
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6期
页 面：1014-1026页
核心收录：
学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0839[工学-网络空间安全] 08[工学] 0835[工学-软件工程] 081001[工学-通信与信息系统] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the General Program of National Natural Science Fouddation of China:Analytical Method Reserach of Loop and Recursion(No.61872262/F020106) the Key Project of the National Natural Science Foundation of China:Research on Big Service Theory and Methods in Big Data Environment(No.61832004)
主 题：IoT stream processing Edge computing Out-of-Range stream fluctuation Dynamic service deployment
摘 要：The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the *** this connection,a key issue is to determine when and where services should be *** service deployment strategies used to be static based on the rules defined at the design ***,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not *** this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream *** effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.
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