Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Crankshaw, Daniel"'
Autor:
Namyar, Pooria, Arzani, Behnaz, Kandula, Srikanth, Segarra, Santiago, Crankshaw, Daniel, Krishnaswamy, Umesh, Govindan, Ramesh, Raj, Himanshu
We consider the max-min fair resource allocation problem. The best-known solutions use either a sequence of optimizations or waterfilling, which only applies to a narrow set of cases. These solutions have become a practical bottleneck in WAN traffic
Externí odkaz:
http://arxiv.org/abs/2310.09699
Autor:
Namyar, Pooria, Arzani, Behnaz, Crankshaw, Daniel, Berger, Daniel S., Hsieh, Kevin, Kandula, Srikanth, Govindan, Ramesh
Some faults in data center networks require hours to days to repair because they may need reboots, re-imaging, or manual work by technicians. To reduce traffic impact, cloud providers \textit{mitigate} the effect of faults, for example, by steering t
Externí odkaz:
http://arxiv.org/abs/2305.13792
Autor:
Wang, Yawen, Crankshaw, Daniel, Yadwadkar, Neeraja J., Berger, Daniel, Kozyrakis, Christos, Bianchini, Ricardo
Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfortunately, their behavior is typically based on pre-defined static heur
Externí odkaz:
http://arxiv.org/abs/2201.10477
Autor:
Hauck, Annalisa G V *, van der Vaart, Marianne *, Adams, Eleri, Baxter, Luke, Bhatt, Aomesh, Crankshaw, Daniel, Dhami, Amraj, Evans Fry, Ria, Freire, Marina B O, Hartley, Caroline, Mansfield, Roshni C, Marchant, Simon, Monk, Vaneesha, Moultrie, Fiona, Peck, Mariska, Robinson, Shellie, Yong, Jean, Poorun, Ravi, Cobo, Maria M, Slater, Rebeccah *
Publikováno v:
In The Lancet Child & Adolescent Health April 2024 8(4):259-269
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Crankshaw, Daniel, Sela, Gur-Eyal, Zumar, Corey, Mo, Xiangxi, Gonzalez, Joseph E., Stoica, Ion, Tumanov, Alexey
Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction betwe
Externí odkaz:
http://arxiv.org/abs/1812.01776
Autor:
Liaw, Richard, Krishnan, Sanjay, Garg, Animesh, Crankshaw, Daniel, Gonzalez, Joseph E., Goldberg, Ken
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This paper reports results from experiments using Deep Reinforcement Learning o
Externí odkaz:
http://arxiv.org/abs/1711.01503
Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that fora majority of real-world inputs, the recent advances in deep learnin
Externí odkaz:
http://arxiv.org/abs/1706.00885
Autor:
Crankshaw, Daniel, Wang, Xin, Zhou, Giulio, Franklin, Michael J., Gonzalez, Joseph E., Stoica, Ion
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployme
Externí odkaz:
http://arxiv.org/abs/1612.03079
Autor:
Crankshaw, Daniel, Bailis, Peter, Gonzalez, Joseph E., Li, Haoyuan, Zhang, Zhao, Franklin, Michael J., Ghodsi, Ali, Jordan, Michael I.
To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused heavily on the design of systems to support training complex models on larg
Externí odkaz:
http://arxiv.org/abs/1409.3809