| Cheng Yingchao(成英超)* ***,Hao Zhifeng * **,Cai Ruichu *.[J].高技术通讯(英文),2020,26(1):17~24 |
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| Exploring serverless computing for stream analytic |
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| DOI：doi：10.3772/j.issn.1006-6748.2020.01.003 |
| 中文关键词: |
| 英文关键词: serverless,steam processing,HPC cloud,auto-scaling,function-as-a-service(FaaS) |
| 基金项目: |
| Author Name | Affiliation | | Cheng Yingchao(成英超)* *** | （*School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, P.R.China)
（***Department of Statistics, Texas A&M University, College Station 77840, USA) | | Hao Zhifeng * ** | （*School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, P.R.China)
（**School of Mathematics and Big Data, Foshan University, Foshan 528000, P.R.China) | | Cai Ruichu * | （*School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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