Low Latency and High-Reliability Resource Scheduling Method in Big Data Streaming Computing Environment

It is quite important for the application of big data to ensure the high-reliability and low-latency of processing the stream data.In this paper,the data stream graph (DSG) is used to describe and model the application process of big data streaming by taking DSG as an extended Petri net.And then,this paper proposes a computing algorithm of resource group reliability via the resource entropy that is based on the average changing rate of CPU utilization.Furthermore,a resource scheduling algorithm,termed as TRS-SCHE,is introduced to attain the high-reliability and low-latency.Finally,through simulation experiments of soda traffic big data analysis,it is shown that compared with the Storm isolated scheduling,the proposed TRS-SCHE scheduling algorithm has more advantages in response time,failure rate and the time complexity.