Minimizing the Number of Rules to Mitigate Link Congestion in SDN-based Datacenters

Minimizing the Number of Rules to Mitigate Link Congestion in SDN-based Datacenters
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DOI:
10.1109/nas51552.2021.9605365
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发表时间:
2021-10
期刊:
2021 IEEE International Conference on Networking, Architecture and Storage (NAS)
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通讯作者:
Rajorshi Biswas;Jie Wu
Rajorshi Biswas;Jie Wu
中科院分区:
其他
文献类型:
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作者:
Rajorshi Biswas;Jie Wu

文献摘要

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常规流量引起的链路拥塞和链路泛洪攻击(LFA)是数据中心面临的两大问题。近年来,软件定义网络(SDN)在数据中心的应用越来越多,它支持动态和方便的配置管理,可以轻松地重新配置网络以减轻LFA。重定向某些流量的重新配置可以通过两种方式完成:最短的替代路径和最小的规则路径更改。SDN交换机存储规则的容量有限,当存储的规则数量增加时,性能会急剧下降。此外,SDN交换机的变化需要一定的时间来适应,这会导致流量中断。在本文中,我们的目标是最小化规则更改的数量,同时重定向来自拥塞链路的一些流量。我们制定了两个问题,以尽量减少重定向流量的规则更改数量。第一个问题是基本的,它考虑了一个拥塞的链接和一个流向。我们提供了基于dijkstra和基于规则合并的问题解决方案。第二个问题考虑了多个流,我们提出了基于流分组和规则合并的解决方案。为了支持我们的模型,我们在数据中心进行了大量的模拟和实验。
Link congestion due to regular traffic and link flooding attacks (LFA) are two major problems in datacenters. Recent usage growth of software defined networking (SDN) in datacenters enables dynamic and convenient configuration management that makes it easy to reconfigure the network to mitigate the LFA. The reconfiguration that redirects some of the traffic can be done in two ways: the shortest alternative path and the minimum changes in rule path. The SDN switches have a limited capacity for the rules and the performance dramatically drops when the number of stored rules is higher. Besides, it takes some time to adopt the changes by the SDN switches which causes interruption in flow. In this paper, we aim at minimizing the number of rule changes while redirecting some of the traffic from the congested link. We formulate two problems to minimize the number of rule changes to redirect traffic. The first problem is the basic and it considers a congested link and a flow to direct. We provide a Dijkstra-based and a rule merging based solution to the problems. The second problem considers multiple flows and we propose flow grouping and rule merging based solutions. We conduct extensive simulations and experiments in our datacenter to support our model.