基于流特征的数据中心非对称流负载均衡方法

Asymmetric Flow Load Balancing Method Based on Flow Characteristics in Data Center Network

数据中心边界广泛部署的地址转换技术产生的非对称流为负载均衡系统的设计带来了挑战.为了解决软件负载均衡系统不能充分发挥多核处理器和网卡硬件能力的问题,提出一种基于流特征的非对称流负载均衡方法.首先,分析网卡的数据包散列机制,提出数据包调度算法,将数据包调度至预期的CPU核;然后,基于会话报文序列的时间与空间特征,构建大象流识别算法;最后,基于识别结果,提出负载均衡方法.实验结果表明,非对称流负载均衡方法可以正确处理非对称流的负载均衡,平均吞吐率提升约14.5%.

The asymmetric flow generated by the widely deployed address translation technology brings challenges to the design of load balancing system.To solve the problem of insufficient use of multi-core processors and network card hardware capabilities by software load balancers,an asymmetric flow load balancing method based on flow characteristics is proposed.Firstly,a data packet dispatching algorithm to dispatch packets into expected CPU core via hardware is proposed.Then,an elephant flow detection algorithm is constructed by analyzing of the temporal and spatial characteristics of packet sequences.Finally,based on detected results,a load balance offloading method is proposed.The experimental results show that,asymmetric flow load balancing method can correctly handle the asymmetric flow.Meanwhile,the average throughput rate increases by 14.5%.