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FDRC: Flow-driven rule caching optimization in software defined networking

机译:FDRC:软件定义网络中的流驱动规则缓存优化

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With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. Software defined network (SDN) provides a scalable and flexible structure to simplify network traffic management. It has been shown that Ternary Content Addressable Memory (TCAM) management plays an important role on the performance of SDN. However, previous literatures, in point of view on rule placement strategies, are still insufficient to provide high scalability for processing large flow sets with a limited TCAM size. So caching is a brand new method for TCAM management which can provide better performance than rule placement. In this paper, we propose FDRC, an efficient flow-driven rule caching algorithm to optimize the cache replacement in SDN-based networks. Different from the previous packet-driven caching algorithm, FDRC is characterized by trying to deal with the challenges of limited cache size constraint and unpredictable flows. In particular, we design a caching algorithm with low-complexity to achieve high cache hit ratio by prefetching and special replacement strategy for predictable and unpredictable flows, respectively. By conducting extensive simulations, we demonstrate that our proposed caching algorithm significantly outperforms FIFO and least recently used (LRU) algorithms under various network settings.
机译:随着云服务及其组合的急剧增长,近年来数据中心网络流量的规模不可避免地呈爆炸性增长。软件定义网络(SDN)提供了可扩展且灵活的结构,以简化网络流量管理。已经表明,三态内容可寻址存储器(TCAM)管理在SDN的性能中起着重要作用。但是,从规则放置策略的角度来看,以前的文献仍不足以为处理TCAM大小受限的大型流集提供高可伸缩性。因此,缓存是TCAM管理的全新方法,与规则放置相比,缓存可以提供更好的性能。在本文中,我们提出了FDRC,这是一种有效的流驱动规则缓存算法,用于优化基于SDN的网络中的缓存替换。与以前的数据包驱动的缓存算法不同,FDRC的特征在于试图应对缓存大小受限和流量不可预测的挑战。特别是,我们设计了一种低复杂度的缓存算法,分别通过针对可预测和不可预测的流进行预取和特殊替换策略来实现高缓存命中率。通过进行广泛的仿真,我们证明了我们提出的缓存算法在各种网络设置下明显优于FIFO和最近最少使用的(LRU)算法。

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