Enabling Heterogeneous Network Function Chaining
Enabling Heterogeneous Network Function Chaining
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DOI：
10.1109/tpds.2018.2871845
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发表时间：
2019-04
影响因子：
5.3
通讯作者：
Lin Cui;Fung Po Tso;Song Guo;Weijia Jia;Kaimin Wei;Wei Zhao
中科院分区：
文献类型：
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作者：
Lin Cui;Fung Po Tso;Song Guo;Weijia Jia;Kaimin Wei;Wei Zhao
当今的数据中心运营商在物理（例如，中间盒，交换机）和虚拟化（例如，通用服务器上的虚拟机）网络功能盒（NFB），它们驻留在网络的不同点，以分别利用它们的效率和灵活性。然而，这种异质性导致大量的独立网络节点可以动态地生成和实现不一致和冲突的网络策略，使得正确的策略实现成为一个难以解决的问题。由于这些节点具有不同的功能，因此在其上运行的服务也面临着严重的性能不可预测性。在本文中，我们提出了一个异构的集群策略执行（HOPE）计划，以克服这些挑战。HOPE保证实现策略链的网络功能（NF）被最佳地放置在异构NFB上，使得策略的网络成本最小化。我们首先通过实验证明，NFB的处理能力是主要的性能因素。这个观察，然后用来制定异构网络的策略放置问题，这是NP-难。为了有效地解决该问题，提出了一种在线算法。我们的实验结果表明，HOPE实现了相同的最优分支定界优化，但3个数量级更有效。
Today’s data center operators deploy network policies in both physical (e.g., middleboxes, switches) and virtualized (e.g., virtual machines on general purpose servers) network function boxes (NFBs), which reside in different points of the network, to exploit their efficiency and agility respectively. Nevertheless, such heterogeneity has resulted in a great number of independent network nodes that can dynamically generate and implement inconsistent and conflicting network policies, making correct policy implementation a difficult problem to solve. Since these nodes have varying capabilities, services running atop are also faced with profound performance unpredictability. In this paper, we propose a Heterogeneous netwOrk Policy Enforcement (HOPE) scheme to overcome these challenges. HOPE guarantees that network functions (NFs) that implement a policy chain are optimally placed onto heterogeneous NFBs such that the network cost of the policy is minimized. We first experimentally demonstrate that the processing capacity of NFBs is the dominant performance factor. This observation is then used to formulate the Heterogeneous Network Policy Placement problem, which is shown to be NP-Hard. To solve the problem efficiently, an online algorithm is proposed. Our experimental results demonstrate that HOPE achieves the same optimality as Branch-and-bound optimization but is 3 orders of magnitude more efficient.