基于聚合的FlowRadar网络数据采集加速模型设计

Design of aggregation-based FlowRadar acceleration model for network data collection

网络大数据采集是研究网络行为的基础,可为理解网络行为特征提供真实有效的数据基础。已有的FlowRadar测量方法可针对所有流实现流量编码和解码,且占用较小内存和具备恒定的计数更新时间,但却需要对每个报文进行哈希计算,存在内存访问次数多和计算开销高的问题。针对该问题,基于协议无关交换架构(PISA),采用P4语言设计实现了报文聚合模块和流逐出模块,并通过仿真实验验证了加速模型在吞吐量和网络延时等方面的性能优化。

Network big data collection is the basis of network behavior research,which can provide real and effective data basis for understanding the characteristics of network behavior.The existing FlowRadar measurement methods can encode and decode traffic for all streams,with less memory and constant count update time.However,each packet needs to be hashed,which has the problems of high memory access times and high computation cost.To solve this problem,this paper designs and implements the packet aggregation module and the flow evict module with P4 language based on Protocol Independent Switch Arch(PISA).Simulation results verify the optimization performance of the acceleration model in terms of throughput and network delay.