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Recent networking research has identified that data-driven congestion control (CC) can be more efficient than traditional CC in TCP.
Packet routing in dynamically changing networks: A reinforcement learning approach
Boyan, J. A., and Littman, M. L · 1993
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Introduction to Reinforcement Learning
Sutton, R. S., and Barto, A. G · 1998
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D., Singh, S., and Mansour, Y · 1999
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Rem: Active queue management
Athuraliya, S., Low, S. H., Li, V. H., and Yin, Q · 2001
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Network emulation with netem
Hemminger, S · 2005
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Tcp new vegas: improving the performance of tcp vegas over high latency links
Sing, J., and Soh, B · 2005
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Managing power consumption and performance of computing systems using reinforcement learning
Tesauro, G., Das, R., Chan, H., Kephart, J., Levine, D., Rawson, F., and Lefurgy, C · 2008
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The nature of data center traffic: Measurements & analysis
Kandula, S., Sengupta, S., Greenberg, A., Patel, P., and Chaiken, R · 2009
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Hedera: Dynamic flow scheduling for data center networks
Al-Fares, M., Radhakrishnan, S., Raghavan, B., Huang, N., and Vahdat, A · 2010
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Data center tcp (dctcp)
Alizadeh, M., Greenberg, A., Maltz, D. A., Padhye, J., Patel, P., Prabhakar, B., Sengupta, S., and Sridharan, M · 2010
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Network traffic characteristics of data centers in the wild
Benson, T., Akella, A., and Maltz, D. A · 2010
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A network in a laptop: Rapid prototyping for software-defined networks
Lantz, B., Heller, B., and McKeown, N · 2010
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A machine learning approach to tcp throughput prediction
Mirza, M., Sommers, J., Barford, P., and Zhu, X · 2010
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Microte: Fine grained traffic engineering for data centers
Benson, T., Anand, A., Akella, A., and Zhang, M · 2011
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Protecting against evaluation overfitting in empirical reinforcement learning
Whiteson, S., Tanner, B., Taylor, M. E., and Stone, P · 2011
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Better never than late: Meeting deadlines in datacenter networks
Wilson, C., Ballani, H., Karagiannis, T., and Rowtron, A · 2011
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Deadline-aware datacenter tcp (d2tcp)
Vamanan, B., Hasan, J., and Vijaykumar, T · 2012
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Tcp ex machina: Computer-generated congestion control
Winstein, K., and Balakrishnan, H · 2013
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Conga: Distributed congestion-aware load balancing for datacenters
Alizadeh, M., Edsall, T., Dharmapurikar, S., Vaidyanathan, R., Chu, K., Fingerhut, A., Lam, V. T., Matus, F., Pan, R., Yadav, N., and Varghese, G · 2014
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Escape: Extensible service chain prototyping environment using mininet, click, netconf and pox
Csoma, A., Sonkoly, B., Csikor, L., Németh, F., Gulyas, A., Tavernier, W., and Sahhaf, S · 2014
Cited alongside, same era.
Rethinking congestion control architecture: Performance-oriented congestion control
Dong, M., Li, Q., Zarchy, D., Godfrey, B., and Schapira, M · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J · 2014
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Reinforcement Learning in Robotics: A Survey
Kober, J., and Peters, J · 2014
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., and Abbeel, P · 2016
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Resource management with deep reinforcement learning
Mao, H., Alizadeh, M., Menache, I., and Kandula, S · 2016
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Medicine: Rapid prototyping of production-ready network services in multi-pop environments
Peuster, M., Karl, H., and van Rossem, S · 2016
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Credit-scheduled delay-bounded congestion control for datacenters
Cho, I., Jang, K., and Han, D · 2017
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A unified game-theoretic approach to multiagent reinforcement learning
Lanctot, M., Zambaldi, V. F., Gruslys, A., Lazaridou, A., Tuyls, K., Pérolat, J., Silver, D., and Graepel, T · 2017
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An experimental study of the learnability of congestion control
Sivaraman, A., Winstein, K., Thaker, P., and Balakrishnan, H · 2014
Cited alongside, same era.
Queues don’t matter when you can jump them!
Grosvenor, M. P., Schwarzkopf, M., Gog, I., Watson, R. N. M., Moore, A. W., Hand, S., and Crowcroft, J · 2015
Cited alongside, same era.
High speed networks need proactive congestion control
Jose, L., Yan, L., Alizadeh, M., Varghese, G., McKeown, N., and Katti, S · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
Cited alongside, same era.
Improved empirical methods in reinforcement-learning evaluation
Marivate, V. N · 2015
Cited alongside, same era.
Timely: Rtt-based congestion control for the datacenter
Mittal, R., Lam, V. T., Dukkipati, N., Blem, E., Wassel, H., Ghobadi, M., Vahdat, A., Wang, Y., Wetherall, D., and Zats, D · 2015
Cited alongside, same era.
Fastpass: A centralized zero-queue datacenter network
Perry, J., Ousterhout, A., Balakrishnan, H., Shah, D., and Fugal, H · 2015
Cited alongside, same era.
Leike, J., Martic, M., Krakovna, V., Ortega, P. A., Everitt, T., Lefrancq, A., Orseau, L., and Legg, S · 2017
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Can deep reinforcement learning solve erdos-selfridge-spencer games?
Raghu, M., Irpan, A., Andreas, J., Kleinberg, R., Le, Q. V., and Kleinberg, J. M · 2017
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Deep reinforcement learning framework for autonomous driving
Sallab, A. E., Abdou, M., Perot, E., and Yogamani, S · 2017
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Congestion-control throwdown
Schapira, M., and Winstein, K · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Deepconfig: Automating data center network topologies management with machine learning
Streiffer, C., Chen, H., Benson, T., and Kadav, A · 2017
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Learning to route
Valadarsky, A., Schapira, M., Shahaf, D., and Tamar, A · 2017
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Copa: Practical delay-based congestion control for the internet
Arun, V., and Balakrishnan, H · 2018
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Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Chen, L., Lingys, J., Chen, K., and Liu, F · 2018
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2018
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RLlib: Abstractions for distributed reinforcement learning
Liang, E., Liaw, R., Nishihara, R., Moritz, P., Fox, R., Goldberg, K., Gonzalez, J., Jordan, M., and Stoica, I · 2018
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Pantheon: the training ground for internet congestion-control research
Yan, F. Y., Ma, J., Hill, G. D., Raghavan, D., Wahby, R. S., Levis, P., and Winstein, K · 2018
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A study on overfitting in deep reinforcement learning
Zhang, C., Vinyals, O., Munos, R., and Bengio, S · 2018
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