Exploring serverless computing for stream analytic

作     者：成英超 Hao Zhifeng Cai Ruichu Cheng Yingchao;Hao Zhifeng;Cai Ruichu

作者机构：School of Computer Science and TechnologyGuangdong University of TechnologyGuangzhou 510006P.R.China School of Mathematics and Big DataFoshan UniversityFoshan 528000P.R.China Department of StatisticsTexas A&M UniversityCollege Station 77840USA 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2020年第26卷第1期

页      面：17-24页

核心收录：

学科分类：08[工学] 0710[理学-生物学] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 0807[工学-动力工程及工程热物理] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 0836[工学-生物工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：Suported by the National Natural Science Foundation of China(No.61472089,61572143) NSFC-Guangdong Joint Found(No.U1501254) China Scholarship Council(No.201608440336)。 

主　　题：serverless steam processing HPC cloud auto-scaling function-as-a-service(FaaS) 

摘      要：This work proposes ARS(FaaS) serverless framework scheduling and provisioning resources for streaming applications autonomously, which ensures real-time response on unpredictable and fluctuating streaming data. A HPC cloud platform is used as a de facto platform, on which serverless computing for stream analytic is explored. This work enables application developers to build and run steaming applications without worrying about servers, which means that the developers are able to focus on application features instead of scheduling and provisioning resources of the infrastructure. The serverless computing framework, ARS(FaaS), provides function-as-a-service to make the developers write code in discrete event-driven functions. ARS(FaaS) is capable of running and scaling the developer s code automatically, according to the throughput of streaming events. The major contribution of this serverless framework is effective and efficient autonomous resource scheduling for real-time streaming analytic, which enables the developers to build applications faster with autonomous resource scheduling. ARS(FaaS) framework is appropriate for real-time and stream analytic on event-driven data with spiky and variable compute requirements.