Jun Woo Park
Carnegie Mellon University
7 Papers
Jun Woo Park is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Scheduling (computing). The author has an hindex of 6, co-authored 7 publications.
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Papers
Scaling distributed machine learning with the parameter server
Mu Li,David G. Andersen,Jun Woo Park,Alexander J. Smola,Amr Ahmed,Vanja Josifovski,James Long,Eugene J. Shekita,Bor-Yiing Su +8 more
- 06 Oct 2014
TL;DR: In this paper, the authors propose a parameter server framework for distributed machine learning problems, where both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matrices.
Stratus: cost-aware container scheduling in the public cloud
Andrew Chung,Jun Woo Park,Gregory R. Ganger +2 more
- 11 Oct 2018
TL;DR: Simulation experiments based on cluster workload traces from Google and TwoSigma show that Stratus reduces cost by 17-44% compared to state-of-the-art approaches to virtual cluster scheduling.
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3Sigma: distribution-based cluster scheduling for runtime uncertainty
Jun Woo Park,Alexey Tumanov,Angela H. Jiang,Michael Kozuch,Gregory R. Ganger +4 more
- 23 Apr 2018
TL;DR: Analysis of job traces from three different large-scale cluster environments shows that, while the runtimes of many jobs can be predicted well, even state-of-the-art predictors have wide error profiles, and the performance of 3Sigma approaches the end-to-end performance of a scheduler based on a hypothetical, perfect runtime predictor.
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Estimating what US residential customers are willing to pay for resilience to large electricity outages of long duration
TL;DR: In this paper, the authors developed a method to estimate residential willingness-to-pay for back-up electricity services in the event of a large 10-day blackout during very cold winter weather, and then survey a sample of 483 residential customers across northeast USA using that method.
•Proceedings Article
On the diversity of cluster workloads and its impact on research results
George Amvrosiadis,Jun Woo Park,Gregory R. Ganger,Garth A. Gibson,Elisabeth Baseman,Nathan DeBardeleben +5 more
- 11 Jul 2018
TL;DR: An analysis of the private and HPC cluster traces that spans job characteristics, workload heterogeneity, resource utilization, and failure rates shows that the private cluster workloads, consisting of data analytics jobs expected to be more closely related to the Google workload, display more similarity to the HPC Cluster workloads.