Chin-Jung Hsu

Ph.D Candidate

Computer Science, North Carolina State University

Biography

I am a Ph.D. student, advised by Dr. Vincent W. Freeh, of Computer Science at North Carolina State University, My research primarily focuses on optimizing system performance of large-scale distributed systems. Due to the growing complexity of systems, I am passionate about applying machine learning techniques to address problems in systems such as cloud computing and software-defined storage.

Before starting my Ph.D., I received M.S. in Computer Science from National Tsing Hua University in Taiwan. After graduation, I was an Android engineer at Acer Inc. In 2011, I started my journey in the states. I was a performance measurement intern and a research intern in NetApp for the summer of 2013 and 2014, and in 2015, I interned at AT&T Labs Research, working on software-defined storage.

Interests

Distributed Systems
Storage Systems
System Performance
Artificial Intelligence

Education

Ph.D Candidate in Computer Science, 2018

North Carolina State University
M.S. in Computer Science (Information Systems and Applications), 2009

National Tsing Hua University, Taiwan
B.S. in Computer Science and Information Technology, 2007

Fu Jen Catholic University, Taiwan

Selected Publications

Scout: An Experienced Guide to Find the Best Cloud Configuration

This work proposes an efficient, effective and robust method to find the best configuration in the cloud. SCOUT identifies resource requirements using low-level performance metrics and searches only the spotlight region (configuration space). Our evaluation shows SCOUT is several times better than the state-of-the-art methods.

Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent W. Freeh

In USENIX ATC’18 (submitted), 2018.

Preprint

Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

This work identifies the fragility problem in applying Bayesian Optimization in searching for the best cloud configuration. We propose a low-level augmented Bayesian Optimization method to alleviate the fragility problem. Based on this work, we conclude that it is often insufficient to use general-purpose off-the-shelf methods for configuring cloud instances without augmenting those methods with essential systems knowledge such as CPU utilization, working memory size and I/O wait time.

Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh, Tim Menzies

In ICDCS 2018 (submitted), 2017.

Preprint

Inside-Out: Reliable Performance Prediction for Distributed Storage Systems in the Cloud

Software-defined storage requires to meet users’ performance requirements. Machine learning techniques are used to create reliable performance models from low-level system metrics collected at runtime. The accurate performance model enables service providers to provision storage resources in a more fine-grained way.

Chin-Jung Hsu, Rajesh K Panta, Moo-Ryong Ra, Vincent W. Freeh

In SRDS (Best Paper Award), 2016.

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Recent Publications

Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent W. Freeh. Scout: An Experienced Guide to Find the Best Cloud Configuration. In USENIX ATC’18 (submitted), 2018.

Preprint

Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh, Tim Menzies. Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM. In ICDCS 2018 (submitted), 2017.

Preprint

Chin-Jung Hsu, Vincent W. Freeh, Flavio Villanustre. Trilogy: Data Placement to Improve Performance and Robustness of Cloud Computing. In SCDM, 2017.

PDF

Chin-Jung Hsu, Rajesh K Panta, Moo-Ryong Ra, Vincent W. Freeh. Inside-Out: Reliable Performance Prediction for Distributed Storage Systems in the Cloud. In SRDS (Best Paper Award), 2016.

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Kamal Kc, Chin-Jung Hsu, Vincent W. Freeh. Evaluation of MapReduce in a large cluster. In CLOUD, 2015.

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Chin-Jung Hsu, Vincent W. Freeh. Flow Scheduling: An Efficient Scheduling Method for MapReduce Framework in the Decoupled Architecture. In TR, 2014.

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Chin-Jung Hsu, Wu-Chun Chung, Kuan-Chou Lai, Kuan-Ching Li, Yeh-Ching Chung. A Novel Approach for Cooperative Overlay-Maintenance in Multi-Overlay Environments . In CloudCom, 2010.

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