sdn中支持nfv的组播的逼近和在线算法

Zichuan Xu, W. Liang, Meitian Huang, M. Jia, Song Guo, A. Galis
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引用次数: 51

摘要

多播是网络的一项基本功能,适用于许多应用程序,包括在线会议、事件监控、视频流和数据中心的系统监控。为了确保组播的可靠性、安全性和可扩展性,通常需要一个由网络功能(如防火墙、入侵检测系统和转码器)组成的服务链与每个组播请求相关联。这样的组播请求称为启用nfv的组播请求。在本文中,我们研究了软件定义网络(SDN)中启用nfv的组播,目的是在网络资源容量限制的情况下,最小化每个启用nfv的组播请求的实现成本,或最大化一系列启用nfv的请求的网络吞吐量。我们首先提出了新的支持nfv的多播和在线支持nfv的多播问题。然后,如果用于实现每个请求的网络功能的服务器数量不超过常数K(1),则我们设计了具有2K近似比率的第一个近似算法,用于支持nfv的多播问题。我们还研究了支持nfv的多播请求的动态接收,而不知道未来的请求到达,目标是最大化网络吞吐量。为此,我们提出了一种在线算法,当K = 1时,竞争比为O(log n),其中n为网络中的节点数。最后,我们通过实验模拟来评估所提出算法的性能。实验结果表明,该算法优于现有的启发式算法。
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Approximation and Online Algorithms for NFV-Enabled Multicasting in SDNs
Multicasting is a fundamental functionality of networks for many applications including online conferencing, event monitoring, video streaming, and system monitoring in data centers. To ensure multicasting reliable, secure and scalable, a service chain consisting of network functions (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) usually is associated with each multicast request. Such a multicast request is referred to as an NFV-enabled multicast request. In this paper we study NFV-enabled multicasting in a Software-Defined Network (SDN) with the aims to minimize the implementation cost of each NFV-enabled multicast request or maximize the network throughput for a sequence of NFV-enabled requests, subject to network resource capacity constraints. We first formulate novel NFV-enabled multicasting and online NFV-enabled multicasting problems. We then devise the very first approximation algorithm with an approximation ratio of 2K for the NFV-enabled multicasting problem if the number of servers for implementing the network functions of each request is no more than a constant K (1). We also study dynamic admissions of NFV-enabled multicast requests without the knowledge of future request arrivals with the objective to maximize the network throughput, for which we propose an online algorithm with a competitive ratio of O(log n) when K = 1, where n is the number of nodes in the network. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms outperform other existing heuristics.
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