Daniel Crankshaw

According to our database, Daniel Crankshaw authored at least 15 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Solving Max-Min Fair Resource Allocations Quickly on Large Graphs.

Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj

Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation, 2024

2023

Mitigating the Performance Impact of Network Failures in Public Clouds.

Pooria Namyar, Behnaz Arzani, Daniel Crankshaw, Daniel S. Berger, Kevin Hsieh, Srikanth Kandula, Ramesh Govindan

CoRR, 2023

2022

SOL: safe on-node learning in cloud platforms.

Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel S. Berger, Christos Kozyrakis, Ricardo Bianchini

Proceedings of the ASPLOS '22: 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Lausanne, Switzerland, 28 February 2022, 2022

2020

InferLine: latency-aware provisioning and scaling for prediction serving pipelines.

Daniel Crankshaw, Gur-Eyal Sela, Xiangxi Mo, Corey Zumar, Ion Stoica, Joseph Gonzalez, Alexey Tumanov

Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019

The Design and Implementation of Low-Latency Prediction Serving Systems.

Daniel Crankshaw

PhD thesis, 2019

2018

InferLine: ML Inference Pipeline Composition Framework.

Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov

CoRR, 2018

Research for practice: prediction-serving systems.

Daniel Crankshaw, Joseph Gonzalez, Peter Bailis

Commun. ACM, 2018

IDK Cascades: Fast Deep Learning by Learning not to Overthink.

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2017

Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning.

Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg

CoRR, 2017

IDK Cascades: Fast Deep Learning by Learning not to Overthink.

Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Joseph E. Gonzalez

CoRR, 2017

Clipper: A Low-Latency Online Prediction Serving System.

Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica

Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation, 2017

2015

The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox.

Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan

Proceedings of the Seventh Biennial Conference on Innovative Data Systems Research, 2015

2014

GraphX: Unifying Data-Parallel and Graph-Parallel Analytics.

Reynold S. Xin, Daniel Crankshaw, Ankur Dave, Joseph E. Gonzalez, Michael J. Franklin, Ion Stoica

CoRR, 2014

GraphX: Graph Processing in a Distributed Dataflow Framework.

Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, Ion Stoica

Proceedings of the 11th USENIX Symposium on Operating Systems Design and Implementation, 2014

2013

Inverted indices for particle tracking in petascale cosmological simulations.

Daniel Crankshaw, Randal C. Burns, Bridget Falck, Tamas Budavari, Alexander S. Szalay, Jie Wang

Proceedings of the Conference on Scientific and Statistical Database Management, 2013