Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers
Traffic-Optimal Virtual Network Function Placement and Migration in Dynamic Cloud Data Centers
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DOI：
10.1109/ipdps53621.2022.00094
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Vincent Tran;Jingsong Sun;Bin Tang;Deng Pan
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文献类型：
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作者：
Vincent Tran;Jingsong Sun;Bin Tang;Deng Pan
我们提出了一种新的算法框架，用于策略保留数据中心（PPDC）的流量最优虚拟网络功能（VNF）的放置和迁移。由于动态虚拟机（VM）流量必须遍历PPDC中的一系列VNF，因此与传统数据中心相比，它会生成更多的网络流量，消耗更高的带宽，并导致额外的流量延迟。我们设计了最佳的，近似的，启发式的流量感知VNF的放置和迁移算法，以尽量减少PPDC中的总网络流量。特别是，我们提出了第一个流量感知的恒定因子近似算法的VNF布局，一个帕累托最优的VNF迁移的解决方案，和一套有效的动态编程（DP）为基础的算法，进一步提高了近似的解决方案。在我们的框架的核心是两个新的图论问题，还没有被研究。使用生产数据中心中发现的流特征和实际流量模式，我们表明a）我们的VNF迁移技术在减轻PPDC中的动态流量方面是有效的，将总流量成本降低高达73%，B）我们的VNF放置算法产生的流量成本比现有技术小56%至64%，以及c）我们的VNF迁移算法在减少动态网络流量方面比最先进的VM迁移算法高出高达63%。
We propose a new algorithmic framework for traffic-optimal virtual network function (VNF) placement and migration for policy-preserving data centers (PPDCs). As dynamic virtual machine (VM) traffic must traverse a sequence of VNFs in PPDCs, it generates more network traffic, consumes higher bandwidth, and causes additional traffic delays than a traditional data center. We design optimal, approximation, and heuristic traffic-aware VNF placement and migration algorithms to minimize the total network traffic in the PPDC. In particular, we propose the first traffic-aware constant-factor approximation algorithm for VNF placement, a Pareto-optimal solution for VNF migration, and a suite of efficient dynamic-programming (DP)-based heuristics that further improves the approximation solution. At the core of our framework are two new graph-theoretical problems that have not been studied. Using flow characteristics found in production data centers and realistic traffic patterns, we show that a) our VNF migration techniques are effective in mitigating dynamic traffic in PPDCs, reducing the total traffic cost by up to 73%, b) our VNF placement algorithms yield traffic costs 56% to 64% smaller than those by existing techniques, and c) our VNF migration algorithms outperform the state-of-the-art VM migration algorithms by up to 63% in reducing dynamic network traffic.