Is Machine Learning Ready for Traffic Engineering Optimization?
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Albert Cabellos-Aparicio | Pere Barlet-Ros | Shihan Xiao | Xiangle Cheng | Bo Wu | Jos'e Su'arez-Varela | Guillermo Bern'ardez | Albert L'opez | A. Cabellos-Aparicio | P. Barlet-Ros | Albert Lopez | Bo-Xi Wu | Shihan Xiao | Xiangle Cheng | Guillermo Bern'ardez | Jos'e Su'arez-Varela | Guillermo Bernárdez | José Suárez-Varela
[1] John Moy,et al. OSPF Version 2 , 1998, RFC.
[2] Toby Walsh,et al. Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.
[3] Albert Cabellos-Aparicio,et al. Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN , 2019, SOSR.
[4] T. V. Lakshman,et al. Optimized network traffic engineering using segment routing , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).
[5] Georg Carle,et al. Learning and Generating Distributed Routing Protocols Using Graph-Based Deep Learning , 2018, Big-DAMA@SIGCOMM.
[6] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[7] L. Freeman,et al. Centrality in valued graphs: A measure of betweenness based on network flow , 1991 .
[8] Ming Zhang,et al. MicroTE: fine grained traffic engineering for data centers , 2011, CoNEXT '11.
[9] Viktor Prasanna,et al. GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms , 2019, FPGA.
[10] Ning Wang,et al. An overview of routing optimization for internet traffic engineering , 2008, IEEE Communications Surveys & Tutorials.
[11] Pierre Schaus,et al. REPETITA: Repeatable Experiments for Performance Evaluation of Traffic-Engineering Algorithms , 2017, ArXiv.
[12] Xin Wang,et al. Machine Learning for Networking: Workflow, Advances and Opportunities , 2017, IEEE Network.
[13] Matthieu Geist,et al. What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study , 2021, ICLR.
[14] Marivi Higuero,et al. A Survey on the Contributions of Software-Defined Networking to Traffic Engineering , 2017, IEEE Communications Surveys & Tutorials.
[15] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[16] Nikos A. Vlassis,et al. Optimal and Approximate Q-value Functions for Decentralized POMDPs , 2008, J. Artif. Intell. Res..
[17] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[18] Mikkel Thorup,et al. Increasing Internet Capacity Using Local Search , 2004, Comput. Optim. Appl..
[19] Dafna Shahaf,et al. Learning to Route , 2017, HotNets.
[20] Mikkel Thorup,et al. Internet traffic engineering by optimizing OSPF weights , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).
[21] Jakob N. Foerster,et al. Deep multi-agent reinforcement learning , 2018 .
[22] Albert Cabellos-Aparicio,et al. Challenging the generalization capabilities of Graph Neural Networks for network modeling , 2019, SIGCOMM Posters and Demos.
[23] Mung Chiang,et al. Link-State Routing with Hop-by-Hop Forwarding Can Achieve Optimal Traffic Engineering , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.
[24] Albert Cabellos-Aparicio,et al. A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization , 2017, ArXiv.
[25] Olivier Bonaventure,et al. A Declarative and Expressive Approach to Control Forwarding Paths in Carrier-Grade Networks , 2015, SIGCOMM.
[26] Stefano Vissicchio,et al. Expect the unexpected: Sub-second optimization for segment routing , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.
[27] Kun Cao,et al. A Survey of Deployment Solutions and Optimization Strategies for Hybrid SDN Networks , 2019, IEEE Communications Surveys & Tutorials.
[28] Alec Radford,et al. Proximal Policy Optimization Algorithms , 2017, ArXiv.
[29] Hongyan Li,et al. Packet Routing Against Network Congestion: A Deep Multi-agent Reinforcement Learning Approach , 2020, 2020 International Conference on Computing, Networking and Communications (ICNC).
[30] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[31] Albert Cabellos-Aparicio,et al. Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case. , 2020 .
[32] Tiejun Huang,et al. Graph Convolutional Reinforcement Learning , 2020, ICLR.
[33] Daniel O. Awduche,et al. Requirements for Traffic Engineering Over MPLS , 1999, RFC.
[34] Stephen C. Adams,et al. Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication , 2020, ArXiv.
[35] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[36] Chi Harold Liu,et al. Experience-driven Networking: A Deep Reinforcement Learning based Approach , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[37] A. Basu,et al. Stability issues in OSPF routing , 2001, SIGCOMM '01.
[38] Olivier Bonaventure,et al. CG4SR: Near Optimal Traffic Engineering for Segment Routing with Column Generation , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[39] Roch Guérin,et al. Achieving near-optimal traffic engineering solutions for current OSPF/IS-IS networks , 2005, IEEE/ACM Transactions on Networking.
[40] Mingwei Xu,et al. A Multi-agent Reinforcement Learning Perspective on Distributed Traffic Engineering , 2020, 2020 IEEE 28th International Conference on Network Protocols (ICNP).
[41] Clarence Filsfils,et al. The Segment Routing Architecture , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).
[42] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[43] Edith Cohen,et al. Optimal oblivious routing in polynomial time , 2003, STOC '03.
[44] Matthew Roughan,et al. Simplifying the synthesis of internet traffic matrices , 2005, CCRV.
[45] Demis Hassabis,et al. Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.
[46] Razvan Pascanu,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[47] Angela L. Chiu,et al. Overview and Principles of Internet Traffic Engineering , 2002, RFC.