在商品硬件之上构建DDoS缓解流量策略

Erkin Kirdan, Daniel Raumer, Paul Emmerich, G. Carle
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引用次数: 2

摘要

流量监管是通过流量整形等方法确保网络流量符合其策略的过程。由于DDoS攻击中涉及的源的分布与web服务的典型客户分布有很大不同,因此可以在DDoS缓解中使用流量塑造器和策略。在过去,基于软件的中间件,比如流量整形器,很容易过载,因此成为DDoS攻击的一个漏洞。尽管商用硬件上网络堆栈设计的最新进展提高了性能,但网络堆栈之上的软件还需要提供足够的吞吐量和可伸缩性,以应对有限子网的数量。因此,我们构建了一个名为MoonPol的高性能可扩展流量管理器,并在DDoS缓解场景中对其进行了评估。MoonPol可以在任何商用硬件上运行,利用底层框架DPDK,并将其与适当的算法和数据结构相结合。有效查找的数据结构与令牌桶算法一起实现,以监督细粒度IP地址范围的流量。基准测试结果显示,在3.2 GHz CPU上运行的策略的单核吞吐量为6.5 Mpps,限制了100万个子网,即每个数据包492个CPU周期。25k子网覆盖全球所有国家,吞吐量为6.66 Mpps。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building a Traffic Policer for DDoS Mitigation on Top of Commodity Hardware
Traffic policing is the process of ensuring that network traffic complies with its policies with methods like traffic shaping. As the distribution of sources involved in a DDoS attack differs significantly from the typical distribution of customers for web services, traffic shapers and policers can be used in DDoS mitigation. In the past, software-based middleboxes, like traffic shapers, easily became overloaded and therefore a vulnerability for DDoS attacks. Although recent advances in network stack design on commodity hardware increased the performance, the software on top of the network stack also needs to provide adequate throughput and scalability regarding the number of limited subnets. Therefore, we build a high-performance and scalable traffic policer called MoonPol and evaluated it in a DDoS mitigation scenario. MoonPol runs on any commodity hardware, takes advantage of the underlying framework, DPDK, and combines it with appropriate algorithms and data structures. Data structures for efficient lookups are implemented together with the token bucket algorithm to police a traffic of fine-grained IP address ranges. Benchmarking results show that the single core throughput of the policer running on a 3.2 GHz CPU, is 6.5 Mpps with limiting 1 Million subnets, i.e., 492 CPU cycles per packet. With 250K subnets of all countries in the world, the throughput is 6.66 Mpps.
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