info: C. Streiffer, H. Chen, T. Benson, and A. Kadav, “DeepConfig: Automating Data Center Network Topologies Management with Machine Learning.” arXiv, Dec. 11, 2017. Accessed: Aug. 06, 2023. [Online]. Available: http://arxiv.org/abs/1712.03890
info: H. Kim, T. Benson, A. Akella, and N. Feamster, “The evolution of network configuration: a tale of two campuses,” in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, Berlin Germany: ACM, Nov. 2011, pp. 499–514. doi: 10.1145/2068816.2068863.
info: T. Benson, A. Akella, and D. A. Maltz, “Mining policies from enterprise network configuration,” in Proceedings of the 9th ACM SIGCOMM conference on Internet measurement, Chicago Illinois USA: ACM, Nov. 2009, pp. 136–142. doi: 10.1145/1644893.1644909.
info: T. Benson, A. Akella, and D. Maltz, “Unraveling the Complexity of Network Management,” in Proceedings of the 6th USENIX Symposium on Networked Systems Design and Implementation, in NSDI’09. USA: USENIX Association, 2009, pp. 335–348. doi: 10.5555/1558977.1559000.
1.1 背景
- 企业网络配置非常复杂，不同网络之间配置差异很大。
- 企业网络中，配置错误是造成网络故障的一个重要原因。
info: B. Li et al., “Random sketch learning for deep neural networks in edge computing,” Nat Comput Sci, vol. 1, no. 3, pp. 221–228, Mar. 2021, doi: 10.1038/s43588-021-00039-6.
1.1 背景
深度神经网络对计算和存储资源需求巨大，这给它们在边缘设备上的部署带来困难。最近，轻量级深度学习受到了极大关注，其目的是通过网络剪枝、低秩近似(LRA)、权重量化和网络架构转换(NAT)等压缩大型DNN模型。有工作基于矩阵逼近理论近似相对更低秩和稀疏的DNN模型的权重矩阵，从而得到一个轻量的紧凑模型。