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In recent years, many techniques have been developed to improve the performance and efficiency of data center networks.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
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Packet routing in dynamically changing networks: A reinforcement learning approach
J. A. Boyan, M. L. Littman, et al · 1994
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A scalable, commodity data center network architecture
M. Al-Fares, A. Loukissas, and A. Vahdat · 2008
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A scalable, commodity data center network architecture
M. Al-Fares, A. Loukissas, and A. Vahdat · 2008
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The cost of a cloud: Research problems in data center networks
A. Greenberg, J. Hamilton, D. A. Maltz, and P. Patel · 2008
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Reducing network energy consumption via sleeping and rate-adaptation
S. Nedevschi, L. Popa, G. Iannaccone, S. Ratnasamy, and D. Wetherall · 2008
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Vl2: A scalable and flexible data center network
A. Greenberg, J. R. Hamilton, N. Jain, S. Kandula, C. Kim, P. Lahiri, D. A. Maltz, P. Patel, and S. Sengupta · 2009
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Hedera: Dynamic flow scheduling for data center networks
M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang, and A. Vahdat · 2010
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Helios: A hybrid electrical/optical switch architecture for modular data centers
N. Farrington, G. Porter, S. Radhakrishnan, H. H. Bazzaz, V. Subramanya, Y. Fainman, G. Papen, and A. Vahdat · 2010
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Elastictree: Saving energy in data center networks
B. Heller, S. Seetharaman, P. Mahadevan, Y. Yiakoumis, P. Sharma, S. Banerjee, and N. McKeown · 2010
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c-through: Part-time optics in data centers
G. Wang, D. G. Andersen, M. Kaminsky, K. Papagiannaki, T. Ng, M. Kozuch, and M. Ryan · 2010
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Microte: The case for fine-grained traffic engineering in data centers
T. Benson, A. An, A. Akella, and M. Zhang · 2011
Cited alongside, same era.
Frenetic: A network programming language
N. Foster, R. Harrison, M. J. Freedman, C. Monsanto, J. Rexford, A. Story, and D. Walker · 2011
Cited alongside, same era.
Augmenting data center networks with multi-gigabit wireless links
D. Halperin, S. Kandula, J. Padhye, P. Bahl, and D. Wetherall · 2011
Cited alongside, same era.
A compiler and run-time system for network programming languages
C. Monsanto, N. Foster, R. Harrison, and D. Walker · 2012
Cited alongside, same era.
Transparent and flexible network management for big data processing in the cloud
A. Das, C. Lumezanu, Y. Zhang, V. K. Singh, G. Jiang, and C. Yu · 2013
Cited alongside, same era.
Covisor: A compositional hypervisor for software-defined networks
X. Jin, J. Gossels, J. Rexford, and D. Walker · 2015
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High-dimensional continuous control using generalized advantage estimation
J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel · 2015
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Simplifying software-defined network optimization using sol
V. Heorhiadi, M. K. Reiter, and V. Sekar · 2016
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Morpheus: Towards automated slos for enterprise clusters
S. A. Jyothi, C. Curino, I. Menache, S. M. Narayanamurthy, A. Tumanov, J. Yaniv, R. Mavlyutov, I. Goiri, S. Krishnan, J. Kulkarni, and S. Rao · 2016
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Resource management with deep reinforcement learning
H. Mao, M. Alizadeh, I. Menache, and S. Kandula · 2016
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V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
Cited alongside, same era.
Corybantic: Towards the modular composition of sdn control programs
J. C. Mogul, A. AuYoung, S. Banerjee, L. Popa, J. Lee, J. Mudigonda, P. Sharma, and Y. Turner · 2013
Cited alongside, same era.
Democratic resolution of resource conflicts between sdn control programs
A. AuYoung, Y. Ma, S. Banerjee, J. Lee, P. Sharma, Y. Turner, C. Liang, and J. C. Mogul · 2014
Cited alongside, same era.
Firefly: A reconfigurable wireless data center fabric using free-space optics
N. Hamedazimi, Z. Qazi, H. Gupta, V. Sekar, S. R. Das, J. P. Longtin, H. Shah, and A. Tanwer · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
Micro load balancing in data centers with drill
S. Ghorbani, B. Godfrey, Y. Ganjali, and A. Firoozshahian · 2015
Cited alongside, same era.
https://blog.gigaspaces.com/amazon-found -every-100ms-of-latency-cost-them-1-in- sales/
Amazon found every 100ms of latency cost them 1% in sales
Cited in the paper.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
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N. Usunier, G. Synnaeve, Z. Lin, and S. Chintala · 2016
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Let me rephrase that: Transparent optimization in sdns
S. Prabhu, M. Dong, T. Meng, P. B. Godfrey, and M. Caesar · 2017
Closest in time.
Understanding and mitigating packet corruption in data center networks
D. Zhuo, M. M. Ghobadi, R. Mahajan, K.-T. Forster, A. Krishnamurthy, and T. Anderson · 2017
Closest in time.