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Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

Authors:

Francis Y. Yan,

Justin T. Chiu,

Alexander M. Rush,

Minlan YuAuthors Info & Claims

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

Pages 378 - 393

https://doi.org/10.1145/3603269.3604857

Published: 01 September 2023 Publication History

Abstract

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance.

We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links.

We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625× speedups.

References

[1]

Parallelism in LP and MIP, August 2020. https://cdn.gurobi.com/wp-content/uploads/2020/08/How-to-Exploit-Parallelism-in-Linear-and-Mixed-Integer-Programming.pdf.

[2]

Firas Abuzaid, Srikanth Kandula, Behnaz Arzani, Ishai Menache, Matei Zaharia, and Peter Bailis. Contracting Wide-area Network Topologies to Solve Flow Problems Quickly. In Proceedings of USENIX NSDI, pages 175--200, 2021.

[3]

Dimitri P. Bertsekas. Constrained Optimization and Lagrange Multiplier Methods. Academic press, 2014.

[4]

Jeremy Bogle, Nikhil Bhatia, Manya Ghobadi, Ishai Menache, Nikolaj Bjørner, Asaf Valadarsky, and Michael Schapira. TEAVAR: Striking the Right Utilization-Availability Balance in WAN Traffic Engineering. In Proceedings of ACM SIGCOMM. ACM, 2019.

[5]

Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al. Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers. Foundations and Trends® in Machine learning, 3(1):1--122, 2011.

Digital Library

[6]

CAIDA. The CAIDA AS Relationships Dataset, 2022.

[7]

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. cuDNN: Efficient Primitives for Deep Learning. arXiv preprint arXiv:1410.0759, 2014.

[8]

Anwar Elwalid, Cheng Jin, Steven Low, and Indra Widjaja. MATE: MPLS Adaptive Traffic Engineering. In Proceedings of IEEE INFOCOM, volume 3, pages 1300--1309 vol.3, 2001.

[9]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. Graph Neural Networks for Social Recommendation. In International world Wide Web Conference, pages 417--426, 2019.

Digital Library

[10]

Lisa K. Fleischer. Approximating Fractional Multicommodity Flow Independent of the Number of Commodities. SIAM Journal on Discrete Mathematics, 13(4):505--520, 2000.

Digital Library

[11]

Jakob Foerster, Ioannis Alexandros Assael, Nando De Freitas, and Shimon Whiteson. Learning to Communicate with Deep Multi-Agent Reinforcement Learning. Advances in Neural Information Processing Systems, 29, 2016.

[12]

Jakob Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson. Counterfactual Multi-Agent Policy Gradients. In Proceedings of AAAI conference on artificial intelligence, volume 32, 2018.

[13]

Bernard Fortz, Jennifer Rexford, and Mikkel Thorup. Traffic Engineering with Traditional IP Routing Protocols. IEEE Communications Magazine, 40(10):118--124, 2002.

Digital Library

[14]

Nan Geng, Mingwei Xu, Yuan Yang, Chenyi Liu, Jiahai Yang, Qi Li, and Shize Zhang. Distributed and Adaptive Traffic Engineering with Deep Reinforcement Learning. In Proceedings of IEEE/ACM International Symposium on Quality of Service (IWQOS), pages 1--10, 2021.

[15]

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning, pages 1263--1272. PMLR, 2017.

[16]

Google Cloud. Cloud Tensor Processing Units (TPUs), 2022.

[17]

Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2022.

[18]

William L. Hamilton, Rex Ying, and Jure Leskovec. Representation Learning on Graphs: Methods and Applications. arXiv preprint arXiv:1709.05584, 2017.

[19]

Tamir Hazan, Joseph Keshet, and David McAllester. Direct Loss Minimization for Structured Prediction. Advances in Neural Information Processing Systems, 23, 2010.

[20]

Geoffrey E. Hinton and Sam Roweis. Stochastic neighbor embedding. Advances in Neural Information Processing Systems, 15, 2002.

[21]

Chi-Yao Hong, Srikanth Kandula, Ratul Mahajan, Ming Zhang, Vijay Gill, Mohan Nanduri, and Roger Wattenhofer. Achieving High Utilization with Software-Driven WAN. ACM SIGCOMM Computer Communication Review, 43(4):15--26, August 2013.

Digital Library

[22]

Chi-Yao Hong, Subhasree Mandal, Mohammad A. Alfares, Min Zhu, Rich Alimi, Kondapa Naidu Bollineni, Chandan Bhagat, Sourabh Jain, Jay Kaimal, Jeffrey Liang, Kirill Mendelev, Steve Padgett, Faro Thomas Rabe, Saikat Ray, Malveeka Tewari, Matt Tierney, Monika Zahn, Jon Zolla, Joon Ong, and Amin Vahdat. B4 and After: Managing Hierarchy, Partitioning, and Asymmetry for Availability and Scale in Google's Software-Defined WAN. In Proceedings of ACM SIGCOMM, 2018.

[23]

IBM. CPLEX Optimizer, 2022.

[24]

GPU-Based Deep Learning Inference and Based Deep Learning. A Performance and Power Analysis. Nvidia Whitepaper, Nov, 2015.

[25]

Sushant Jain, Alok Kumar, Subhasree Mandal, Joon Ong, Leon Poutievski, Arjun Singh, Subbaiah Venkata, Jim Wanderer, Junlan Zhou, Min Zhu, et al. B4: Experience with A Globally-Deployed Software Defined WAN. ACM SIGCOMM Computer Communication Review, 43(4):3--14, 2013.

Digital Library

[26]

Nathan Jay, Noga Rotman, Brighten Godfrey, Michael Schapira, and Aviv Tamar. A Deep Reinforcement Learning Perspective on Internet Congestion Control. In International Conference on Machine Learning, pages 3050--3059. PMLR, 2019.

[27]

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM international conference on Multimedia, pages 675--678, 2014.

[28]

Srikanth Kandula, Dina Katabi, Bruce Davie, and Anna Charny. Walking the Tightrope: Responsive Yet Stable Traffic Engineering. ACM SIGCOMM Computer Communication Review, 35(4):253--264, 2005.

Digital Library

[29]

Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980, 2014.

[30]

Simon Knight, Hung X. Nguyen, Nickolas Falkner, Rhys Bowden, and Matthew Roughan. The Internet Topology Zoo. IEEE Journal on Selected Areas in Communications, 29(9):1765--1775, 2011.

[31]

Vijay Konda and John Tsitsiklis. Actor-Critic Algorithms. Advances in Neural Information Processing Systems, 12, 1999.

[32]

Mario Köppen. The Curse of Dimensionality. In Proceedings of Online World Conference on Soft Computing in Industrial Applications (WSC), volume 1, pages 4--8, 2000.

[33]

Umesh Krishnaswamy, Rachee Singh, Nikolaj Bjørner, and Himanshu Raj. Decentralized Cloud Wide-Area Network Traffic Engineering with BLASTSHIELD. In Proceedings of USENIX NSDI, pages 325--338, Renton, WA, April 2022. USENIX Association.

[34]

Umesh Krishnaswamy, Rachee Singh, Paul Mattes, Paul-Andre C. Bissonnette, Nikolaj Bjørner, Zahira Nasrin, Sonal Kothari, Prabhakar Reddy, John Abeln, Srikanth Kandula, et al. OneWAN Is Better than Two: Unifying a Split WAN Architecture. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), pages 515--529, 2023.

[35]

Jitendra Kumar and Ashutosh Kumar Singh. Cloud Resource Demand Prediction Using Differential Evolution Based Learning. In Proceedings of IEEE International Conference on Smart Computing & Communications (ICSCC), pages 1--5. IEEE, 2019.

[36]

Oliver Lange and Luis Perez. Traffic Prediction with Advanced Graph Neural Networks, 2020.

[37]

Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan, and Jaime G. Carbonell. Enforcing Output Constraints via SGD: A Step Towards Neural Lagrangian Relaxation. In Proceedings of NeurIPS Workshop on Automated Knowledge Base Construction (AKBC), 2017.

[38]

Hongqiang Harry Liu, Srikanth Kandula, Ratul Mahajan, Ming Zhang, and David Gelernter. Traffic Engineering with Forward Fault Correction. In Fabián E. Bustamante, Y. Charlie Hu, Arvind Krishnamurthy, and Sylvia Ratnasamy, editors, Proceedings of ACM SIGCOMM, pages 527--538. ACM, 2014.

[39]

Libin Liu, Li Chen, Hong Xu, and Hua Shao. Automated Traffic Engineering in SDWAN: Beyond Reinforcement Learning. In IEEE INFOCOM WKSHPS Workshops, pages 430--435, 2020.

[40]

Tanwi Mallick, Mariam Kiran, Bashir Mohammed, and Prasanna Balaprakash. Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks. In Proceedings of IEEE International Conference on Big Data (Big Data), pages 1--10. IEEE, 2020.

[41]

Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. Neural Adaptive Video Streaming with Pensieve. In Proceedings of ACM SIGCOMM, pages 197--210, 2017.

[42]

Abduallah Mohamed, Kun Qian, Mohamed Elhoseiny, and Christian Claudel. Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction. In Proceedings of IEEE/CVF CVPR, pages 14424--14432, 2020.

[43]

Bashir Mohammed, Mariam Kiran, and Nandini Krishnaswamy. DeepRoute on Chameleon: Experimenting with Large-Scale Reinforcement Learning and SDN on Chameleon Testbed. In Proceedings of IEEE International Conference on Network Protocols (ICNP), pages 1--2, 2019.

[44]

Vinod Nair, Sergey Bartunov, Felix Gimeno, Ingrid von Glehn, Pawel Lichocki, Ivan Lobov, Brendan O'Donoghue, Nicolas Sonnerat, Christian Tjandraatmadja, Pengming Wang, et al. Solving Mixed Integer Programs Using Neural Networks. arXiv preprint arXiv:2012.13349, 2020.

[45]

Pooria Namyar, Behnaz Arzani, Ryan Beckett, Santiago Segarra, Himanshu Raj, and Srikanth Kandula. Minding the Gap Between Fast Heuristics and Their Optimal Counterparts. In Proceedings of the 21st ACM Workshop on Hot Topics in Networks, pages 138--144, 2022.

[46]

Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid, Peter Kraft, Akshay Agrawal, Srikanth Kandula, Stephen Boyd, and Matei Zaharia. Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP. In Proceedings of ACM SOSP, pages 521--537, 2021.

[47]

John C. Nash. The (Dantzig) Simplex Method for Linear Programming. Computing in Science and Engg., 2(1):29--31, jan 2000.

Digital Library

[48]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems, pages 8024--8035, 2019.

[49]

Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch, Srikanth Kandula, Ishai Menache, Michael Schapira, and Aviv Tamar. DOTE: Rethinking (Predictive) Wan Traffic Engineering. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23), pages 1557--1581, 2023.

[50]

Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce, and Alexander B. Wiltschko. A Gentle Introduction to Graph Neural Networks. Distill, 2021. https://distill.pub/2021/gnn-intro.

[51]

Brandon Schlinker, Hyojeong Kim, Timothy Cui, Ethan Katz-Bassett, Harsha V Madhyastha, Italo Cunha, James Quinn, Saif Hasan, Petr Lapukhov, and Hongyi Zeng. Engineering Egress with Edge Fabric: Steering Oceans of Content to the World. In Proceedings of ACM SIGCOMM, pages 418--431. ACM, 2017.

Digital Library

[52]

Rachee Singh, Sharad Agarwal, Matt Calder, and Paramvir Bahl. Cost-Effective Cloud Edge Traffic Engineering With Cascara. In Proceedings of USENIX NSDI, pages 201--216, 2021.

[53]

Rachee Singh, Manya Ghobadi, Klaus-Tycho Foerster, Mark Filer, and Phillipa Gill. RADWAN: Rate Adaptive Wide Area Network. In Proceedings of ACM SIGCOMM, page 547--560, New York, NY, USA, 2018. Association for Computing Machinery.

[54]

Yang Song, Alexander Schwing, Raquel Urtasun, et al. Training Deep Neural Networks via Direct Loss Minimization. In International Conference on Machine Learning, pages 2169--2177. PMLR, 2016.

Digital Library

[55]

Richard S. Sutton, David McAllester, Satinder Singh, and Yishay Mansour. Policy Gradient Methods for Reinforcement Learning with Function Approximation. Advances in Neural Information Processing Systems, 12, 1999.

[56]

Tensorflow. An End-to-End Open Source Machine Learning Platform, 2022.

[57]

The Linux Foundation. Open Neural Network Exchange, 2022.

[58]

Asaf Valadarsky, Michael Schapira, Dafna Shahaf, and Aviv Tamar. Learning to Route. In Proceedings of ACM HotNets, pages 185--191, 2017.

Digital Library

[59]

Asaf Valadarsky, Michael Schapira, Dafna Shahaf, and Aviv Tamar. Learning to route with deep RL. In NIPS Deep Reinforcement Learning Symposium, 2017.

[60]

Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning. Nature, 575(7782):350--354, 2019.

[61]

Hao Wang, Haiyong Xie, Lili Qiu, Yang Richard Yang, Yin Zhang, and Albert Greenberg. COPE: Traffic Engineering in Dynamic Networks. In Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications, pages 99--110, 2006.

[62]

David H. Wolpert and Kagan Tumer. Optimal Payoff Functions for Members of Collectives. In Modeling Complexity in Economic and Social Systems, pages 355--369. World Scientific, 2002.

[63]

Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S. Yu Philip. A Comprehensive Survey on Graph Neural Networks. IEEE transactions on neural networks and learning systems, 32(1):4--24, 2020.

[64]

Xipeng Xiao, A. Hannan, B. Bailey, and L. M. Ni. Traffic Engineering with MPLS in the Internet. IEEE Network, 14(2):28--33, March 2000.

Digital Library

[65]

Zhiyuan Xu, Jian Tang, Jingsong Meng, Weiyi Zhang, Yanzhi Wang, Chi Harold Liu, and Dejun Yang. Experience-Driven Networking: A Deep Reinforcement Learning Based Approach. CoRR, abs/1801.05757, 2018.

[66]

Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein. Learning in Situ: A Randomized Experiment in Video Streaming. In Proceedings of USENIX NSDI, pages 495--511, Santa Clara, CA, February 2020. USENIX Association.

[67]

Francis Y. Yan, Jestin Ma, Greg D. Hill, Deepti Raghavan, Riad S. Wahby, Philip Levis, and Keith Winstein. Pantheon: the Training Ground for Internet Congestion-Control Research. In Proceedings of USENIX ATC, pages 731--743, Boston, MA, July 2018. USENIX Association.

[68]

Kok-Kiong Yap, Murtaza Motiwala, Jeremy Rahe, Steve Padgett, Matthew Holliman, Gary Baldus, Marcus Hines, Taeeun Kim, Ashok Narayanan, Ankur Jain, et al. Taking the Edge off with Espresso: Scale, Reliability and Programmability for Global Internet Peering. In Proceedings of ACM SIGCOMM, pages 432--445, 2017.

[69]

Junjie Zhang, Minghao Ye, Zehua Guo, Chen-Yu Yen, and H. Jonathan Chao. CFR-RL: Traffic Engineering with Reinforcement Learning in SDN. IEEE Journal on Selected Areas in Communications, 38(10):2249--2259, 2020.

[70]

Zhizhen Zhong, Manya Ghobadi, Alaa Khaddaj, Jonathan Leach, Yiting Xia, and Ying Zhang. ARROW: Restoration-Aware Traffic Engineering. In Proceedings of ACM SIGCOMM, page 560--579, New York, NY, USA, 2021. Association for Computing Machinery.

[71]

Hang Zhu, Varun Gupta, Satyajeet Singh Ahuja, Yuandong Tian, Ying Zhang, and Xin Jin. Network Planning with Deep Reinforcement Learning. In Proceedings of ACM SIGCOMM, pages 258--271, 2021.

Cited By

Yan SKan NLi CDai WZou JXiong HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Task-Oriented Multi-Bitstream Optimization for Image Compression and Transmission via Optimal TransportProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681523(3695-3703)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681523
Liu HHuang SQin SYang TYang TXiang QLiu X(2024)Keep Your Paths Free: Toward Scalable Learning-Based Traffic EngineeringProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665813(189-191)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665813
Liu XZhao SCui YWang XSekar VYu MSeneviratne AVeitch D(2024)FIGRET: Fine-Grained Robustness-Enhanced Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672258(117-135)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672258
Show More Cited By

Index Terms

Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Traffic engineering algorithms

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cover image ACM Conferences

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

September 2023

1217 pages

ISBN:9798400702365

DOI:10.1145/3603269

Chairs:
Henning Schulzrinne,
Vishal Misra,
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Eddie Kohler,
David Maltz

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Published: 01 September 2023

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Cited By

Yan SKan NLi CDai WZou JXiong HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Task-Oriented Multi-Bitstream Optimization for Image Compression and Transmission via Optimal TransportProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681523(3695-3703)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681523
Liu HHuang SQin SYang TYang TXiang QLiu X(2024)Keep Your Paths Free: Toward Scalable Learning-Based Traffic EngineeringProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665813(189-191)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665813
Liu XZhao SCui YWang XSekar VYu MSeneviratne AVeitch D(2024)FIGRET: Fine-Grained Robustness-Enhanced Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672258(117-135)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672258
Miao CZhong ZXiao YYang FZhang SJiang YBai ZLu CGeng JHe ZWang YZou XYang CSekar VYu MSeneviratne AVeitch D(2024)MegaTE: Extending WAN Traffic Engineering to Millions of Endpoints in Virtualized CloudProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672242(103-116)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672242
AlQiam AYao YWang ZAhuja SZhang YRao SRibeiro BTawarmalani MSekar VYu MSeneviratne AVeitch D(2024)Transferable Neural WAN TE for Changing TopologiesProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672237(86-102)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672237
Gui FWang SLi DChen LGao KMin CWang YSekar VYu MSeneviratne AVeitch D(2024)RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672231(71-85)Online publication date: 4-Aug-2024
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Nance-Hall MBarford PFoerster KDurairajan R(2024)Improving Scalability in Traffic Engineering via Optical Topology ProgrammingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.333589821:2(1581-1600)Online publication date: Apr-2024
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Nougnanke BLoye JBaffier JFerlin SBruyere MLabit Y(2024)gPerfIsol: GNN-Based Rate-Limits Allocation for Performance Isolation in Multi-Tenant Cloud2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494419(194-201)Online publication date: 11-Mar-2024
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