An Enhanced List Based Packet Classifier for Performance Isolation in Internet Protocol Storage Area Networks

Josephine Kithinji, Makau S. Mutua, Gitonga D. M wathi
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引用次数: 1

Abstract

Consolidation of storage into IP SANs (Internet protocol storage area network) has led to a combination of multiple workloads of varying demands and importance. To ensure that users get their Service level objective (SLO) a technique for isolating workloads is required. Solutions that exist include cache partitioning and throttling of workloads. However, all these techniques require workloads to be classified in order to be isolated. Previous works on performance isolation overlooked the classification process as a source of overhead in implementing performance isolation. However, it’s known that linear search based classifiers search linearly for rules that match packets in order to classify flows which results in delays among other problems especially when rules are many. This paper looks at the various limitation of list based classifiers. In addition, the paper proposes a technique that includes rule sorting, rule partitioning and building a tree rule firewall to reduce the cost of matching packets to rules during classification. Experiments were used to evaluate the proposed solution against the existing solutions and proved that the linear search based classification process could result in performance degradation if not optimized. The results of the experiments showed that the proposed solution when implemented would considerably reduce the time required for matching packets to their classes during classification as evident in the throughput and latency experienced.
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一种用于Internet协议存储区域网络性能隔离的增强型基于列表的数据包分类器
将存储整合到IP san (Internet协议存储区域网络)导致了不同需求和重要性的多个工作负载的组合。为了确保用户获得他们的服务水平目标(SLO),需要一种隔离工作负载的技术。现有的解决方案包括缓存分区和工作负载调节。但是,所有这些技术都需要对工作负载进行分类,以便进行隔离。以前关于性能隔离的工作忽略了分类过程,将其作为实现性能隔离的开销来源。然而,众所周知,基于线性搜索的分类器线性搜索与数据包匹配的规则,以便对流进行分类,这会导致延迟和其他问题,特别是当规则很多时。本文研究了基于列表的分类器的各种局限性。此外,本文还提出了一种包含规则排序、规则划分和构建树状规则防火墙的技术,以降低分类过程中数据包与规则匹配的成本。实验结果表明,如果不进行优化,基于线性搜索的分类过程可能会导致性能下降。实验结果表明,所提出的解决方案在实现时将大大减少在分类过程中将数据包与其类匹配所需的时间,这一点从吞吐量和延迟中可以看出。
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