首页> 外文会议>Distributed Computing Systems Workshops (ICDCSW), 2012 32nd International Conference on >Flow Inspection Router Assignment (FIRA) in Access/Aggregation Network Clouds
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Flow Inspection Router Assignment (FIRA) in Access/Aggregation Network Clouds

机译:访问/聚合网络云中的流检查路由器分配(FIRA)

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Sufficient computing resources available at future generation routers in the access/aggregation networks can be pooled to form a cloud at edge, which we call Access/Aggregation Network Cloud (or ANC). An ANC can be used to handle some of the information processing conventionally done inside the enterprise networks. ANC computing can also result in a much less delay and more flexibility than if such information processing is done centrally in a data center in the core. In this paper, we formulate two representative optimization problems related to Flow Inspection Router Assignment (FIRA) in ANCs. Their objectives are to minimize the (weighted) number of flows that still need to be inspected inside the enterprise network when only some flows can be assigned to the ANC due to the additional flow inspection delay in the ANC, and when all the flows can be assigned to the ANC, to minimize the additional flow inspection delay, respectively. We prove the NP-hardness of these two new problems, study their performance bounds and propose efficient heuristics. We further extend the formulation and solution to support multicast flows. Simulation shows that our heuristic algorithms can perform close to their performance bounds, implying their near optimality and the tightness of the bounds. In addition, simulation also demonstrates how inspection computing resources can be reduced for multicast flows.
机译:可以将接入/汇聚网络中下一代路由器可用的足够计算资源合并到边缘形成云,我们将其称为接入/汇聚网络云(或ANC)。 ANC可用于处理企业网络内部常规完成的某些信息处理。与在核心数据中心集中进行此类信息处理相比,ANC计算还可以减少很多延迟,并提高灵活性。在本文中,我们制定了两个与ANC中的流检查路由器分配(FIRA)有关的代表性优化问题。他们的目标是当由于ANC中额外的流检查延迟而只能将一些流分配给ANC时,以及当所有流都可以分配给ANC时,将仍然需要在企业网络内部检查的(加权)流数量减到最少。分配给ANC,以分别最大程度地减少额外的流量检查延迟。我们证明了这两个新问题的NP难点,研究了它们的性能界限并提出了有效的启发式方法。我们进一步扩展了公式和解决方案,以支持多播流。仿真表明,我们的启发式算法可以在接近性能极限的情况下执行,这表明它们具有接近最优的性能和紧密的边界。此外,仿真还演示了如何减少多播流的检查计算资源。

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