{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:20:02Z","timestamp":1774466402898,"version":"3.50.1"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2018,6,13]],"date-time":"2018-06-13T00:00:00Z","timestamp":1528848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CNS 1526843"],"award-info":[{"award-number":["CNS 1526843"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61422206"],"award-info":[{"award-number":["61422206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2017YFB1010002"],"award-info":[{"award-number":["2017YFB1010002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Meas. Anal. Comput. Syst."],"published-print":{"date-parts":[[2018,6,13]]},"abstract":"<jats:p>The emerging optical\/wireless topology reconfiguration technologies have shown great potential in improving the performance of data center networks. However, it also poses a big challenge on how to find the best topology configurations to support the dynamic traffic demands. In this work, we present xWeaver, a traffic-driven deep learning solution to infer the high-performance network topology online. xWeaver supports a powerful network model that enables the topology optimization over different performance metrics and network architectures. With the design of properly-structured neural networks, it can automatically derive the critical traffic patterns from data traces and learn the underlying mapping between the traffic patterns and topology configurations specific to the target data center. After offline training, xWeaver generates the optimized (or near-optimal) topology configuration online, and can also smoothly update its model parameters for new traffic patterns. We build an optical-circuit-switch-based testbed to demonstrate the function and transmission efficiency of our proposed solution. We further perform extensive simulations to show the significant performance gain of xWeaver, in supporting higher network throughput and smaller flow completion time.<\/jats:p>","DOI":"10.1145\/3224421","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T14:23:33Z","timestamp":1529072613000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Neural Network Meets DCN"],"prefix":"10.1145","volume":"2","author":[{"given":"Mowei","family":"Wang","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Yong","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Shihan","family":"Xiao","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Beijing, China"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Stony Brook University, New York, USA"}]},{"given":"Dan","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2018,6,13]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2012. Grante library. http:\/\/www.nowozin.net\/sebastian\/grante.  2012. Grante library. http:\/\/www.nowozin.net\/sebastian\/grante."},{"key":"e_1_2_1_2_1","unstructured":"2017. Floodlight. http:\/\/www.projectfloodlight.org.  2017. Floodlight. http:\/\/www.projectfloodlight.org."},{"key":"e_1_2_1_3_1","unstructured":"2017. iperf tool. https:\/\/iperf.fr.  2017. iperf tool. https:\/\/iperf.fr."},{"key":"e_1_2_1_4_1","unstructured":"2017. NS-2 Simulator. https:\/\/www.nsnam.org.  2017. NS-2 Simulator. https:\/\/www.nsnam.org."},{"key":"e_1_2_1_5_1","unstructured":"2018. UGM library. http:\/\/www.cs.ubc.ca\/~schmidtm\/Software\/UGM.html.  2018. UGM library. http:\/\/www.cs.ubc.ca\/~schmidtm\/Software\/UGM.html."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1402946.1402967"},{"key":"e_1_2_1_7_1","volume-title":"Hedera: Dynamic Flow Scheduling for Data Center Networks.. In NSDI.","author":"Al-Fares Mohammad","year":"2010"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851182.1851192"},{"key":"e_1_2_1_9_1","unstructured":"Wei Bai Li Chen Kai Chen and Haitao Wu. 2016. Enabling ECN in multi-service multi-queue data centers. In NSDI.   Wei Bai Li Chen Kai Chen and Haitao Wu. 2016. Enabling ECN in multi-service multi-queue data centers. In NSDI."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1879141.1879175"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1672308.1672325"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2896377.2901479"},{"key":"e_1_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Kai Chen Anubhav Singla Ashutosh Singh Kishore Ramachandran Lei Xu Yueping Zhang Xitao Wen and Yan Chen. 2014. OSA: an optical switching architecture for data center networks with unprecedented flexibility. In NSDI.   Kai Chen Anubhav Singla Ashutosh Singh Kishore Ramachandran Lei Xu Yueping Zhang Xitao Wen and Yan Chen. 2014. OSA: an optical switching architecture for data center networks with unprecedented flexibility. In NSDI.","DOI":"10.1109\/TNET.2013.2253120"},{"key":"e_1_2_1_14_1","unstructured":"Li Chen Kai Chen Joshua Zhu Minlan Yu George Porter Chunming Qiao and Shan Zhong. 2017. Enabling WideSpread Communications on Optical Fabric with MegaSwitch. In NSDI.   Li Chen Kai Chen Joshua Zhu Minlan Yu George Porter Chunming Qiao and Shan Zhong. 2017. Enabling WideSpread Communications on Optical Fabric with MegaSwitch. In NSDI."},{"key":"e_1_2_1_15_1","unstructured":"Trishul Chilimbi Yutaka Suzue Johnson Apacible and Karthik Kalyanaraman. 2014. Project adam: Building an efficient and scalable deep learning training system. In OSDI.   Trishul Chilimbi Yutaka Suzue Johnson Apacible and Karthik Kalyanaraman. 2014. Project adam: Building an efficient and scalable deep learning training system. In OSDI."},{"key":"e_1_2_1_16_1","unstructured":"Yong Cui Shihan Xiao Xin Wang Zhenjie Yang Chao Zhu Xiangyang Li Liu Yang and Ning Ge. 2016. Diamond: nesting the data center network with wireless rings in 3D space. In NSDI.   Yong Cui Shihan Xiao Xin Wang Zhenjie Yang Chao Zhu Xiangyang Li Liu Yang and Ning Ge. 2016. Diamond: nesting the data center network with wireless rings in 3D space. In NSDI."},{"key":"e_1_2_1_17_1","volume-title":"trees, and flowers. Canadian Journal of mathematics 17, 3","author":"Edmonds Jack","year":"1965"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851275.1851223"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934911"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043164.2018477"},{"key":"e_1_2_1_21_1","volume-title":"Large Minibatch SGD: Training ImageNet in 1 Hour","author":"Goyal Priya","year":"2017"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1594977.1592576"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2043164.2018442"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626328"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934872.2934904"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1644893.1644918"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.223"},{"key":"e_1_2_1_30_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas"},{"key":"e_1_2_1_31_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (NIPS). 1097--1105.   Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (NIPS). 1097--1105."},{"key":"e_1_2_1_32_1","unstructured":"Sergey Legtchenko Nicholas Chen Daniel Cletheroe Antony Rowstron Hugh Williams and Xiaohan Zhao. 2016. XFabric: a reconfigurable in-rack network for rack-scale computers. In NSDI.   Sergey Legtchenko Nicholas Chen Daniel Cletheroe Antony Rowstron Hugh Williams and Xiaohan Zhao. 2016. XFabric: a reconfigurable in-rack network for rack-scale computers. In NSDI."},{"key":"e_1_2_1_33_1","unstructured":"He Liu Feng Lu Alex Forencich Rishi Kapoor Malveeka Tewari Geoffrey M Voelker George Papen Alex C Snoeren and George Porter. 2014. Circuit switching under the radar with reactor. In NSDI.   He Liu Feng Lu Alex Forencich Rishi Kapoor Malveeka Tewari Geoffrey M Voelker George Papen Alex C Snoeren and George Porter. 2014. Circuit switching under the radar with reactor. In NSDI."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2716281.2836126"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2619239.2626332"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098838"},{"key":"e_1_2_1_37_1","volume-title":"Datacenter Traffic Control: Understanding Techniques and Trade-offs","author":"Noormohammadpour Mohammad","year":"2017"},{"key":"e_1_2_1_39_1","unstructured":"Naoaki Okazaki. 2007. CRFsuite: a fast implementation of Conditional Random Fields (CRFs). http:\/\/www.chokkan. org\/software\/crfsuite\/  Naoaki Okazaki. 2007. CRFsuite: a fast implementation of Conditional Random Fields (CRFs). http:\/\/www.chokkan. org\/software\/crfsuite\/"},{"key":"e_1_2_1_40_1","unstructured":"Kay Ousterhout Ryan Rasti Sylvia Ratnasamy Scott Shenker and Byung-Gon Chun. 2015. Making sense of performance in data analytics frameworks. In NSDI.   Kay Ousterhout Ryan Rasti Sylvia Ratnasamy Scott Shenker and Byung-Gon Chun. 2015. Making sense of performance in data analytics frameworks. In NSDI."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486007"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787472"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787476"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_1_46_1","volume-title":"Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al.","author":"Silver David","year":"2016"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/2785956.2787508"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000013"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_2_1_50_1","unstructured":"Vincent Vanhoucke Andrew Senior and Mark Z Mao. 2011. Improving the speed of neural networks on CPUs. In NIPS.  Vincent Vanhoucke Andrew Senior and Mark Z Mao. 2011. Improving the speed of neural networks on CPUs. In NIPS."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/1851182.1851222"},{"key":"e_1_2_1_52_1","volume-title":"Machine Learning for Networking: Workflow, Advances and Opportunities","author":"Wang Mowei","year":"2017"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3098822.3098837"},{"key":"e_1_2_1_54_1","volume-title":"Link Prediction Based on Graph Neural Networks. arXiv preprint arXiv:1802.09691","author":"Zhang Muhan","year":"2018"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2342356.2342440"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639140"}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3224421","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3224421","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3224421","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:39:06Z","timestamp":1750210746000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3224421"}},"subtitle":["Traffic-driven Topology Adaptation with Deep Learning"],"short-title":[],"issued":{"date-parts":[[2018,6,13]]},"references-count":55,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,6,13]]}},"alternative-id":["10.1145\/3224421"],"URL":"https:\/\/doi.org\/10.1145\/3224421","relation":{},"ISSN":["2476-1249"],"issn-type":[{"value":"2476-1249","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,13]]},"assertion":[{"value":"2018-06-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}