Daniel Crankshaw

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 13

Top Topics

Web Prefetching

Resource Allocation

Top Venues

Publications

Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. NSDI (2024)
Pooria Namyar, Behnaz Arzani, Daniel Crankshaw, Daniel S. Berger, Kevin Hsieh, Srikanth Kandula, Ramesh Govindan
Mitigating the Performance Impact of Network Failures in Public Clouds. CoRR (2023)
Pooria Namyar, Behnaz Arzani, Srikanth Kandula, Santiago Segarra, Daniel Crankshaw, Umesh Krishnaswamy, Ramesh Govindan, Himanshu Raj
Solving Max-Min Fair Resource Allocations Quickly on Large Graphs. CoRR (2023)
Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel S. Berger, Christos Kozyrakis, Ricardo Bianchini
SOL: Safe On-Node Learning in Cloud Platforms. CoRR (2022)
Yawen Wang, Daniel Crankshaw, Neeraja J. Yadwadkar, Daniel S. Berger, Christos Kozyrakis, Ricardo Bianchini
SOL: safe on-node learning in cloud platforms. ASPLOS (2022)
Daniel Crankshaw, Gur-Eyal Sela, Xiangxi Mo, Corey Zumar, Ion Stoica, Joseph Gonzalez, Alexey Tumanov
InferLine: latency-aware provisioning and scaling for prediction serving pipelines. SoCC (2020)
Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Fisher Yu, Joseph E. Gonzalez
IDK Cascades: Fast Deep Learning by Learning not to Overthink. UAI (2018)
Daniel Crankshaw, Joseph Gonzalez, Peter Bailis
Research for practice: prediction-serving systems. Commun. ACM 61 (8) (2018)
Daniel Crankshaw, Gur-Eyal Sela, Corey Zumar, Xiangxi Mo, Joseph E. Gonzalez, Ion Stoica, Alexey Tumanov
InferLine: ML Inference Pipeline Composition Framework. CoRR (2018)
Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica
Clipper: A Low-Latency Online Prediction Serving System. NSDI (2017)
Xin Wang, Yujia Luo, Daniel Crankshaw, Alexey Tumanov, Joseph E. Gonzalez
IDK Cascades: Fast Deep Learning by Learning not to Overthink. CoRR (2017)
Richard Liaw, Sanjay Krishnan, Animesh Garg, Daniel Crankshaw, Joseph E. Gonzalez, Ken Goldberg
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning. CoRR (2017)
Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, Ion Stoica
Clipper: A Low-Latency Online Prediction Serving System. CoRR (2016)
Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CIDR (2015)
Joseph E. Gonzalez, Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, Ion Stoica
GraphX: Graph Processing in a Distributed Dataflow Framework. OSDI (2014)
Daniel Crankshaw, Peter Bailis, Joseph E. Gonzalez, Haoyuan Li, Zhao Zhang, Michael J. Franklin, Ali Ghodsi, Michael I. Jordan
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox. CoRR (2014)
Reynold S. Xin, Daniel Crankshaw, Ankur Dave, Joseph E. Gonzalez, Michael J. Franklin, Ion Stoica
GraphX: Unifying Data-Parallel and Graph-Parallel Analytics. CoRR (2014)
Daniel Crankshaw, Randal C. Burns, Bridget Falck, Tamas Budavari, Alexander S. Szalay, Jie Wang
Inverted indices for particle tracking in petascale cosmological simulations. SSDBM (2013)