首页> 外文会议>Network Infrastructure and Digital Content, 2009. IC-NIDC 2009 >On the performance analysis of traffic splitting on load imbalancing and packet reordering of bursty traffic
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On the performance analysis of traffic splitting on load imbalancing and packet reordering of bursty traffic

机译:基于突发流量的负载分担和报文重排序的流量分割性能分析

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Owing to the heterogeneity and high degree of connectivity of various networks, there likely exist multiple available paths between a source and a destination. To be able to simultaneously and efficiently use such parallel paths, it is essential to facilitate high quality network services at high speeds. So, traffic splitting, having a significant impact on quality of services (QoS), is an important means to achieve load balancing. In general, most existing models can be classified into flow-based or packet-based models. Unfortunately, both classes exhibit some drawbacks, such as low efficiency under the high variance of flow size in flow-based models and the phenomenon of packet reordering in packet-based models. In contrast, Table-based Hashing with Reassignment (THR) and Flowlet Aware Routing Engine (FLARE), both belonging to the class of flow-based models, attempt to achieve both efficient bandwidth utilization and packet order preservation. An original flow can be split into several paths. As compared to the traditional flow-based models, load balancing deviation from ideal distribution decreases while the risk of packet reordering increases. In this paper, we introduce analytical models of THR and FLARE, and derive the probabilities of traffic splitting and packet reordering for each model. Our analysis shows that FLARE is superior to THR in packet order preservation. Also, the performance of FLARE on bursty traffic is demonstrated and discussed.
机译:由于各种网络的异构性和高度的连通性,在源和目的地之间可能存在多个可用路径。为了能够同时有效地使用这种并行路径,必须以高速促进高质量的网络服务。因此,对服务质量(QoS)产生重大影响的流量拆分是实现负载平衡的重要手段。通常,大多数现有模型可以分为基于流的模型或基于分组的模型。不幸的是,这两种类别都表现出一些缺点,例如在基于流的模型中在流大小的高变化下效率低下,以及在基于包的模型中对包进行重新排序的现象。相比之下,属于基于流的模型类别的基于表的具有重新分配的散列(THR)和基于流的感知路由引擎(FLARE)试图同时实现有效的带宽利用率和包顺序保留。原始流可以分为多个路径。与传统的基于流的模型相比,与理想分布的负载平衡偏差减少了,而数据包重新排序的风险却增加了。在本文中,我们介绍了THR和FLARE的分析模型,并推导了每种模型的流量拆分和数据包重新排序的概率。我们的分析表明,FLARE在分组顺序保存方面优于THR。此外,还演示并讨论了FLARE在突发流量上的性能。

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