| Abstract: | 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. |
