面向HPC的函数计算冷启动优化

Cold start optimization on function computingfor high performance computing

High performance computing problems usually have the characteristics of parallelization of subtasks, and a lot of computing resources are consumed in the process of execution. It has been proved that traditional cloud computing based on virtual machine can deal with such problems, but the management of distributed environment and the distributed design of solutions make the processing more complex. Function computing is a new type of serverless cloud computing paradigm, its automatic expansion and considerable computing resources can be well combined with HPC problems. However, the cold start delay is an unavoidable problem on the public cloud function computing platform, especially in the task of HPC problems having high concurrent jobs of which delay will be further magnified. In this paper, we first analyze the completion time of a simple HPC task under cold start and hot start conditions, and analyze the causes of additional delay. According to these analyses, we combine the time series ana lysis tools and the platform's automatic expansion mechanism to propose an effective preheating method, which can effectively reduce the cold start delay of HPC tasks on the function computing platform.