HierTopo: Towards High-Performance and Efficient Topology Optimization for Dynamic Networks
HierTopo: Towards High-Performance and Efficient Topology Optimization for Dynamic Networks
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DOI：
10.1109/iwqos52092.2021.9521261
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Jing Chen;Zili Meng;Yaning Guo;Mingwei Xu;Hongxin Hu
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文献类型：
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作者：
Jing Chen;Zili Meng;Yaning Guo;Mingwei Xu;Hongxin Hu
动态网络使得动态调整网络拓扑结构以满足实时流量需求成为可能。然而，由于拓扑优化的复杂性，现有的解决方案在性能和效率之间存在着权衡，要么存在较大的优化差距，要么优化开销过大。为了突破这种权衡，我们的关键观察是，我们可以将优化过程卸载到每个网络节点来处理复杂性。为此，我们提出了一种针对动态网络的层次化拓扑优化方法--HierTopo，该方法同时获得了高性能和高效率。HierTopo首先在每个网络节点上运行局部策略，将网络信息聚合成低维特征，然后利用这些特征做出全局拓扑决策。对实际网络的评估表明，HierTopo算法的性能比现有的算法高11.52-38.91%，决策延迟仅为毫秒级，泛化能力也更强。
Dynamic networks have enabled dynamically adapting the network topology to meet the need of real-time traffic demands. However, due to the complexity of topology optimization, existing solutions suffer from a trade-off between performance and efficiency, which either have large optimality gaps or excessive optimization overhead. To break through this trade-off, our key observation is that we could offload the optimization procedure to every network node to handle the complexity. Thus, we propose HierTopo, a hierarchical topology optimization method for dynamic networks that achieves both high performance and efficiency. HierTopo firstly runs a local policy on each network node to aggregate network information into low-dimension features, then uses these features to make global topology decisions. Evaluation on real-world network traces shows that HierTopo outperforms the state-of-the-art solutions by 11.52-38.91% with only milliseconds of decision latency, and is also superior in generalization ability.