Maestro: The Analysis-Simulation Integrated Framework for Mixed Reality
Jingyu Lee, Hyunsoo Kim, Minjae Kim, Byung-Gon Chun, Youngki Lee. To appear in MobiSys 2024.
Blaze: Holistic Caching for Iterative Data Processing
Won Wook Song, Jeongyoon Eo, Taegeon Um, Myeongjae Jeon, Byung-Gon Chun. EuroSys 2024, April 2024.
Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service
Hyeonmin Ha, Jihye Lee, Wookje Han, Byung-Gon Chun. EMNLP 2023 (Findings), December 2023.
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models
Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun. ICML 2023 (Oral), July 2023.
Sponge: Fast Reactive Scaling for Stream Processing with Serverless Frameworks
Won Wook Song, Taegeon Um, Sameh Elnikety, Myeongjae Jeon, Byung-Gon Chun. USENIX ATC 2023, July 2023.
Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning.
Hyeonmin Ha, Soyoung Jung, Jinsol Park, Minjoon Seo, Seung-won Hwang, Byung-Gon Chun. ACL 2023 (Findings), July 2023.
FlowKV: A Semantic-Aware Store for Large-Scale State Management of Stream Processing Engines
Gyewon Lee, Jaewoo Maeng, Jinsol Park, Jangho Seo, Haeyoon Cho, Youngseok Yang, Taegeon Um, Jongsung Lee, Jae W. Lee, Byung-Gon Chun. EuroSys 2023, May 2023.
Hippo: Sharing Computations in Hyper-Parameter Optimization [pdf]
Ahnjae Shin, Joo Seong Jeong, Do Yoon Kim, Soyoung Jung, Byung-Gon Chun. VLDB 2022.
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Model [pdf]
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi. VLDB 2022.
SWAN: WAN-aware Stream Processing on Geographically-distributed Clusters [pdf]
Won Wook Song, Myeongjae Jeon, Byung-Gon Chun. APSys 2022.
Orca: A Distributed Serving System for Transformer-Based Generative Models [pdf]
Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, Byung-Gon Chun. OSDI 2022.
OpenNetLab: Open Platform for RL-based Congestion Control for Real-Time Communications [pdf]
Jeongyoon Eo, Zhixiong Niu, Wenxue Cheng, Francis Y. Yan, Rui Gao, Jorina Kardhashi, Scott Inglis, Michael Revow, Byung-Gon Chun, Peng Cheng, Yongqiang Xiong. APNet 2022.
Band: Coordinated Multi-DNN Inference on Heterogeneous Processors [pdf]
Joo Seong Jeong, Jingyu Lee, Donghyun Kim, Changmin Jeon, Changjin Jeong, Youngki Lee, Byung-Gon Chun. MobiSys 2022.
SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search [pdf]
Hyeonmin Ha, Ji-Hoon Kim, Semin Park, Byung-Gon Chun. ICLR 2022.
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs [pdf]
Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), December 2021.
SCOPA: Soft Code-Switching and Pairwise Alignment for Zero-shot Cross-lingual Transfer
Dohyeon Lee, Jaeseong Lee, Gyewon Lee, Byung-Gon Chun, Seung-Won Hwang. 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), November 2021.
Apache Nemo: A Framework for Optimizing Distributed Data Processing [pdf]
Won Wook Song, Youngseok Yang, Jeongyoon Eo, Jangho Seo, Joo Yeon Kim, Sanha Lee, Gyewon Lee, Taegeon Um, Haeyoon Cho, Byung-Gon Chun. ACM Transactions on Computer Systems (TOCS), November 2021.
Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing [pdf]
Youngseok Yang, Taesoo Kim, Byung-Gon Chun. 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2021), July 2021.
Refurbish Your Training Data: Reusing Partially Augmented Samples for Faster Deep Neural Network Training [pdf]
Gyewon Lee, Irene Lee, Hyeonmin Ha, Kyunggeun Lee, Hwarim Hyun, Ahnjae Shin, Byung-Gon Chun. 2021 Annual Technical Conference (ATC 2021), July 2021.
Pluto: High-Performance IoT-Aware Stream Processing
Taegeon Um, Gyewon Lee, Byung-Gon Chun. IEEE 41th International Conference on Distributed Computing Systems (ICDCS 2021), July 2021.
Harmony: A Scheduling Framework Optimized for Multiple Distributed Machine Learning Jobs
Woo-Yeon Lee, Yunseong Lee, Wonwook Song, Youngseok Yang, Joo Yeon Kim, Byung-Gon Chun. IEEE 41th International Conference on Distributed Computing Systems (ICDCS 2021), July 2021.
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning [pdf]
Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun (*equal contribution). 34th Conference on Neural Information Processing Systems (NeurIPS 2020) (Spotlight), December 2020.
A Tensor-based Approach for One-size-fits-all ML Prediction Serving [pdf]
Supun Nakandalam, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi. 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2020), November 2020.
Accelerating Multi-Model Inference by Merging DNNs of Different Weights [pdf]
Accelerating Multi-Model Inference by Merging DNNs of Different Weights. arXiv:2009.13062, September 2020.
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees [pdf]
Ahnjae Shin, Do Yoon Kim, Joo Seong Jeong, Byung-Gon Chun. Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees. arXiv:2006.11972, June 2020.
Stage-based Hyper-parameter Optimization for Deep Learning [pdf]
Ahnjae Shin, Dong-Jin Shin, Sungwoo Cho, Do Yoon Kim, Eunji Jeong, Gyeong-In Yu, Byung-Gon Chun. Stage-based Hyper-parameter Optimization for Deep Learning. Systems for ML Workshop at NeurIPS 2019, December 2019.
Compiling Classical ML Pipelines into Tensor Computations for One-size-fits-all Prediction Serving
Supun Nakandala, Gyeong-In Yu, Markus Weimer, Matteo Interlandi. Systems for ML Workshop at NeurIPS 2019, December 2019.
Knowledge Extraction with No Observable Data
Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. Knowledge Extraction with No Observable Data. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), December 2019.
Speculative Symbolic Graph Execution of Imperative Deep Learning Programs [pdf]
Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dong-Jin Shin, Taebum Kim, Byung-Gon Chun. Speculative Symbolic Graph Execution of Imperative Deep Learning Programs. SIGOPS Operating Systems Review, July 2019.
Apache Nemo: A Framework for Building Distributed Dataflow Optimization Policies [pdf]
Youngseok Yang, Jeongyoon Eo, Geon-Woo Kim, Joo Yeon Kim, Sanha Lee, Jangho Seo, Won Wook Song, Byung-Gon Chun. Apache Nemo: A Framework for Building Distributed Dataflow Optimization Policies. 2019 Annual Technical Conference (ATC 2019), July 2019.
Automating System Configuration of Distributed Machine Learning [pdf]
Woo-Yeon Lee, Yunseong Lee, Joo Seong Jeong, Gyeong-In Yu, Joo Yeon Kim, Ho Jin Park, Beomyeol Jeon, Wonwook Song, Gunhee Kim, Markus Weimer, Brian Cho, Byung-Gon Chun. Automating System Configuration of Distributed Machine Learning. 39th International Conference on Distributed Computing Systems (ICDCS 2019), July 2019.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach [pdf]
Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi. Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. arXiv:1906.03822, June 2019.
Demonstration of JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs [pdf]
Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Byung-Gon Chun. Demonstration of JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. SysML Demo 2019, April 2019.
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks. [pdf]
Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun. Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks. 14th European Conference on Computer Systems (EuroSys 2019), March 2019.
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs [pdf]
Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Byung-Gon Chun. JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2019), February 2019.
From the Edge to the Cloud: Model Serving in ML.NET [pdf]
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Markus Weimer, Matteo Interlandi. From the Edge to the Cloud: Model Serving in ML.NET. IEEE Data Engineering Bulletin, December 2018.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach [pdf]
Gyeong-In Yu, Saeed Amizadeh, Byung-Gon Chun, Markus Weimer, Matteo Interlandi. Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. Systems for ML Workshop at NIPS 2018, December 2018.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems [pdf]
Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, Matteo Interlandi. PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems. 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2018), October 2018.
High-Performance Stateful Stream Processing on Solid-State Drives [pdf]
Gyewon Lee, Jeongyoon Eo, Jangho Seo, Taegeon Um, Byung-Gon Chun. High-Performance Stateful Stream Processing on Solid-State Drives. 9th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2018), August 2018.
Parallax: Automatic Data-Parallel Training of Deep Neural Networks [pdf]
Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun. Parallax: Automatic Data-Parallel Training of Deep Neural Networks, arXiv:1808.02621, August 2018.
Improving the Expressiveness of Deep Learning Frameworks with Recursion [pdf]
Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun (*equal contribution). Improving the Expressiveness of Deep Learning Frameworks with Recursion. 13th European Conference on Computer Systems (EuroSys 2018), April 2018.
Towards High-Performance Prediction Serving Systems [pdf]
Yunseong Lee, Alberto Scolari, Matteo Interlandi, Markus Weimer, Byung-Gon Chun. Towards High-Performance Prediction Serving Systems. SysML Conference, February 2018.
Towards High-Performance Prediction Serving Systems [pdf]
Yunseong Lee, Alberto Scolari, Matteo Interlandi, Markus Weimer, Byung-Gon Chun. Towards High-Performance Prediction Serving Systems. ML Systems Workshop at NIPS 2017, December 2017.
Auto-Parallelizing Deep Learning for Multi-machine, Multi-GPU Environments [pdf] Soojeong Kim, Eunji Jeong, Joo Seong Jeong, Gyeongin Yu, Hojin Park, Byung-Gon Chun. Auto-Parallelizing Deep Learning for Multi-machine, Multi-GPU Environments. Workshop on AI Systems at Symposium on Operating Systems Principles 2017, October 2017.
Apache REEF: Retainable Evaluator Execution Framework [pdf] Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Matteo Interlandi, Beomyeol Jeon, Joo Seong Jeong, Gye-Won Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Tae-Geon Um, Julia Wang, Markus Weimer, Markus Weimer, Youngseok Yang. Apache REEF: Retainable Evaluator Execution Framework. ACM Transactions on Computer Systems (TOCS 2017), October 2017.
Scaling Up IoT Stream Processing [pdf] Taegeon Um, Gyewon Lee, Sanha Lee, Kyungtae Kim, Byung-Gon Chun. Scaling Up IoT Stream Processing. 8th ACM SIGOPS Asia-Pacific Workshop on Systems (APSys 2017), September 2017.
Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters [pdf] Youngseok Yang, Geon-Woo Kim, Won Wook Song, Yunseong Lee, Andrew Chung, Zhengping Qian, Brian Cho, Byung-Gon Chun. Pado: A Data Processing Engine for Harnessing Transient Resources in Datacenters. 12th European Conference on Computer Systems (EuroSys 2017), April 2017.
Breaking Ad-hoc Runtime Integrity Protection Mechanisms in Android Financial Apps [pdf] Taehun Kim, Hyeonmin Ha, Seoyoon Choi, Jaeyeon Jung, Byung-Gon Chun. Breaking Ad-hoc Runtime Integrity Protection Mechanisms in Android Financial Apps. ACM Asia Conference on Computer and Communications Security (ASIACCS 2017), April 2017.
Dolphin: Runtime Optimization for Distributed Machine Learning [pdf] Byung-Gon Chun, Brian Cho, Beomyeol Jeon, Joo Seong Jeong, Gunhee Kim, Joo Yeon Kim, Woo-Yeon Lee, Yun Seong Lee, Markus Weimer, Gyeong-In Yu. Dolphin: Runtime Optimization for Distributed Machine Learning. ICML ML Sys ‘16 workshop, June 2016.
Collaborative Analytics for Data Silos Jinkyu Kim, Heonseok Ha, Byung-Gon Chun, Sungroh Yoon, Sang K. Cha. Collaborative Analytics for Data Silos. 32nd IEEE International Conference on Data Engineering (ICDE 2016), May 2016.
Characterizing Conversation Patterns in Reddit: From the Perspectives of Content Properties and User Participation Behaviors
Daejin Choi, Jinyoung Han, Taejoong Chung, Yong-Yeol Ahn, Byung-Gon Chun, Ted “Taekyoung” Kwon. Characterizing Conversation Patterns in Reddit: From the Perspectives of Content Properties and User Participation Behaviors. ACM Conference on Online Social Networks (COSN 2015), November 15.
Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices
Sanguine Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-Gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, Yunheung Paek. Mantis: Efficient Predictions of Execution Time, Energy Usage, Memory Usage and Network Usage on Smart Mobile Devices. IEEE Transactions on Mobile Computing, August 2015.
REEF: Retainable Evaluator Execution Framework [pdf] Markus Weimer, Yingda Chen, Byung-Gon Chun, Tyson Condie, Carlo Curino, Chris Douglas, Yunseong Lee, Tony Majestro, Dahlia Malkhi , Sergiy Matusevych, Brandon Myers, Shravan Narayanamurthy, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Julia Wang. REEF: Retainable Evaluator Execution Framework.ACM SIGMOD, June 2015.
Elastic Memory: Bring Elasticity Back To In-Memory Big Data Analytics [pdf]
Joo Seong Jeong, Woo-Yeon Lee, Yunseong Lee, Youngseok Yang, Brian Cho, Byung-Gon Chun. Elastic Memory: Bring Elasticity Back To In-Memory Big Data Analytics. 15th Workshop on Hot Topics in Operating Systems (HotOS 2015), May 2015.
Making Sense of Performance in Data Analytics Frameworks [pdf]
Kay Ousterhout, Ryan Rasti, Sylvia Ratnasamy, Scott Shenker, Byung-Gon Chun. Making Sense of Performance in Data Analytics Frameworks. 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2015), May 2015.
TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones
William Enck, Peter Gilbert, Seungyeop Han, Vasant Tendulkar, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, Anmol Sheth. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones. ACM Transactions on Computer Systems (TOCS 2014), June 2014.
Collecting, Organizing and Sharing Pins in Pinterest: Interest-driven or Social-driven? Jinyoung Han, Daejin Choi, Byung-Gon Chun, Ted “Taekyoung” Kwon, Hyun-chul Kim, Yanghee Choi. Collecting, Organizing and Sharing Pins in Pinterest: Interest-driven or Social-driven? ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS 2014), June 2014.
TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones
William Enck, Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, Anmol N. Sheth. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones. Communications of the ACM (CACM 2014), Research Highlights, March 2014.
Reducing Energy Consumption of Smartphones Using User-Perceived Response Time Analysis
Wook Song, Nosub Sung, Byung-Gon Chun, Jihong Kim. Reducing Energy Consumption of Smartphones Using User-Perceived Response Time Analysis. 15th Workshop on Mobile Computing Systems and Applications (HotMobile 2014), February 2014.
REEF: Retainable Evaluator Execution Framework (Demo Paper)
Byung-Gon Chun, Tyson Condie, Carlo Curino, Chris Douglas, Sergiy Matusevych, Brandon Myers, Shravan Narayanamurthy, Raghu Ramakrishnan, Sriram Rao, Josh Rosen, Russell Sears, Markus Weimer. REEF: Retainable Evaluator Execution Framework (Demo Paper). 39th International Conference on Very Large Data Bases (VLDB 2013), August 2013.
Mantis: Automatic Performance Prediction for Smartphone Applications
Yongin Kwon, Sangmin Lee, Hayoon Yi, Donghyun Kwon, Seungjun Yang, Byung-Gon Chun, Ling Huang, Petros Maniatis, Mayur Naik, Yunheung Paek. Mantis: Automatic Performance Prediction for Smartphone Applications. 2013 USENIX Annual Technical Conference (ATC 2013), June 2013.
MegaPipe: A New Programming Interface for Scalable Network I/O
Sangjin Han, Scott Marshall, Byung-Gon Chun, Sylvia Ratnasamy. MegaPipe: A New Programming Interface for Scalable Network I/O. 10th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2012), October 2012.
Mobius: Unified Messaging and Data Service for Mobile Apps
Byung-Gon Chun, Carlo Curino, Russell Sears, Alexander Shraer, Samuel Madden, Raghu Ramakrishnan. Mobius: Unified Messaging and Data Service for Mobile Apps. 10th International Conference on Mobile Systems, Applications, and Services (MobiSys 2012), June 2012.
Automated Security Validation of Mobile Apps at App Markets
Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung. Automated Security Validation of Mobile Apps at App Markets. 2nd International Workshop on Mobile Cloud Computing and Services (MCS 2011), June 2011.
Secure Data Preservers for Web Services
Jayanthkumar Kannan, Petros Maniatis, Byung-Gon Chun. Secure Data Preservers for Web Services. 2nd USENIX Conference on Web Application Development (WebApps 2011), June 2011.
Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud
Gunho Lee, Byung-Gon Chun, Randy Katz. Heterogeneity-Aware Resource Allocation and Scheduling in the Cloud. 3rd Workshop on Hot Topics in Cloud Computing (HotCloud 2011), June 2011.
Making Programs Forget: Enforcing Lifetime For Sensitive Data
Jayanthkumar Kannan, Gautam Altekar, Petros Maniatis, Byung-Gon Chun. Making Programs Forget: Enforcing Lifetime For Sensitive Data. 13th Workshop on Hot Topics in Operating Systems (HotOS 2011), May 2011.
CloneCloud: Elastic Execution between Mobile Device and Cloud
Byung-Gon Chun, Sunghwan Ihm, Petros Maniatis, Mayur Naik, Ashwin Patti. CloneCloud: Elastic Execution between Mobile Device and Cloud. 6th European Conference on Computer Systems (EuroSys 2011), April 2011.
Small Trusted Primitives for Dependable Systems
Petros Maniatis, Byung-Gon Chun. Small Trusted Primitives for Dependable Systems. SIGOPS Operating Systems Review, January 2011.
Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
Ling Huang, Jinzhu Jia, Bin Yu, Byung-Gon Chun, Petros Maniatis, Mayur Naik. Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression. 24th Annual Conference on Neural Information Processing Systems (NIPS 2010).
TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones
William Enck, Peter Gilbert, Byung-Gon Chun, Landon P. Cox, Jaeyeon Jung, Patrick McDaniel, Anmol N. Sheth. TaintDroid: An Information-Flow Tracking System for Realtime Privacy Monitoring on Smartphones. 9th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2010), October 2010.
Dynamically Partitioning Applications between Weak Devices and Clouds
Byung-Gon Chun, Petros Maniatis. Dynamically Partitioning Applications between Weak Devices and Clouds. 1st ACM Workshop on Mobile Cloud Computing and Services (MCS 2010), June 2010.
An Energy Case for Hybrid Datacenters
Byung-Gon Chun, Gianluca Iannaccone, Giuseppe Iannaccone, Randy Katz, Gunho Lee, and Luca Niccolini. An Energy Case for Hybrid Datacenters. SIGOPS Operating Systems Review, March 2010.
Macroscope: End-Point Approach to Networked Application Dependency Discovery
Lucian Popa, Byung-Gon Chun, Ion Stoica, Jaideep Chandrashekar, and Nina Taft. Macroscope: End-Point Approach to Networked Application Dependency Discovery. 5th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2009).
RouteBricks: Exploiting Parallelism to Scale Software Routers
Mihai Dobrescu, Norbert Egi, Katerina Argyraki, Byung-Gon Chun, Kevin Fall, Gianluca Iannaccone, Allan Knies, Maziar Manesh, and Sylvia Ratnasamy. RouteBricks: Exploiting Parallelism to Scale Software Routers. 22nd ACM Symposium on Operating Systems Principles (SOSP 2009), October 2009. Awarded Best Paper!
An Energy Case for Hybrid Datacenters
Byung-Gon Chun, Gianluca Iannaccone, Giuseppe Iannaccone, Randy Katz, Gunho Lee, and Luca Niccolini. An Energy Case for Hybrid Datacenters. 2nd Workshop on Hot Topics in Power-Aware Computing and Systems (HotPower 2009), October 2009.
Augmented Smart Phone Applications Through Clone Cloud Execution
Byung-Gon Chun, Petros Maniatis. Augmented Smart Phone Applications Through Clone Cloud Execution. 12th Workshop on Hot Topics in Operating Systems (HotOS 2009), May 2009.
Tiered Fault Tolerance for Long-Term Integrity
Byung-Gon Chun, Petros Maniatis, Scott Shenker, and John Kubiatowicz. Tiered Fault Tolerance for Long-Term Integrity. 7th USENIX Conference on File and Storage Technologies (FAST 2009), February 2009.
Minuet: Rethinking Concurrency Control in Storage Area Networks
Andrey Ermolinskiy, Daekyeong Moon, Byung-Gon Chun, and Scott Shenker. Minuet: Rethinking Concurrency Control in Storage Area Networks. 7th USENIX Conference on File and Storage Technologies (FAST 2009), February 2009.
Can Software Routers Scale?
Katerina Argyraki, Salman Baset, Byung-Gon Chun, Kevin Fall, Gianluca Iannaccone, Allan Knies, Eddie Kohler, Maziar Manesh, Sergiu Nedveschi, and Sylvia Ratnasamy. Can Software Routers Scale? ACM Special Interest Group on Data Communication Workshop on Programmable Routers for Extensible Services of Tomorrow (PRESTO 2008), August 2008.
Diverse Replication for Single-Machine Byzantine-Fault Tolerance
Byung-Gon Chun, Petros Maniatis, and Scott Shenker. Diverse Replication for Single-Machine Byzantine-Fault Tolerance. USENIX Annual Technical Conference (ATC 2008), June 2008.
NetComplex: A Complexity Metric for Networked System Designs
Byung-Gon Chun, Sylvia Ratnasamy, and Eddie Kohler. NetComplex: A Complexity Metric for Networked System Designs. 5th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2008), April 2008.
Attested Append-Only Memory: Making Adversaries Stick to their Word
Byung-Gon Chun, Petros Maniatis, Scott Shenker, and John Kubiatowicz. Attested Append-Only Memory: Making Adversaries Stick to their Word. 21st ACM Symposium on Operating Systems Principles (SOSP 2007), October 2007.
A Data-Oriented (and Beyond) Network Architecture
Teemu Koponen, Mohit Chawla, Byung-Gon Chun, Andrey Ermolinskiy, Kye Hyun Kim, Scott Shenker, and Ion Stoica. A Data-Oriented (and Beyond) Network Architecture. ACM Special Interest Group on Data Communication (SIGCOMM 2007), August 2007.
Resolving Inter-Domain Policy Disputes
Cheng Tien Ee, Byung-Gon Chun, Vijay Ramachandran, Kaushik Lakshminarayanan, and Scott Shenker. Resolving Inter-Domain Policy Disputes. ACM Special Interest Group on Data Communication (SIGCOMM 2007), August 2007.
Antiquity: Exploiting a Secure Log for Wide-Area Distributed Storage
Hakim Weatherspoon, Patrick Eaton, Byung-Gon Chun, and John Kubiatowicz. Antiquity: Exploiting a Secure Log for Wide-Area Distributed Storage. 2nd European Conference on Computer Systems (EuroSys 2007), March 2007.
Efficient Replica Maintenance for Distributed Storage Systems
Byung-Gon Chun, Frank Dabek, Andreas Haeberlen, Emil Sit, Hakim Weatherspoon, M. Frans Kaashoek, John Kubiatowicz, and Robert Morris. Efficient Replica Maintenance for Distributed Storage Systems. 3rd Symposium on Networked Systems Design and Implementation (NSDI 2006), May 2006.
ChunkCast: An Anycast Service for Large Content Distribution
Byung-Gon Chun, Peter Wu, Hakim Weatherspoon, and John Kubiatowicz. ChunkCast: An Anycast Service for Large Content Distribution. International Workshop on Peer-to-Peer Systems (IPTPS 2006), February 2006.
Proactive Replication for Data Durability
Emil Sit, Andreas Haeberlen, Frank Dabek, Byung-Gon Chun, Hakim Weatherspoon, Robert Morris, M. Frans Kaashoek, and John Kubiatowicz. Proactive Replication for Data Durability. International Workshop on Peer-to-Peer Systems (IPTPS 2006), February 2006.
Fixing the Embarrassing Slowness of OpenDHT on PlanetLab
Sean Rhea, Byung-Gon Chun, John Kubiatowicz, and Scott Shenker. Fixing the Embarrassing Slowness of OpenDHT on PlanetLab. USENIX Workshop on Real, Large Distributed Systems (WORLDS 2005), Dec. 2005. Awarded Best Paper!
Impact of Neighbor Selection on Performance and Resilience of Structured P2P Networks
Byung-Gon Chun, Ben Y. Zhao, and John Kubiatowicz. Impact of Neighbor Selection on Performance and Resilience of Structured P2P Networks. International Workshop on Peer-to-Peer Systems (IPTPS 2005), February 2005.
Selfish Caching in Distributed Systems: A Game-Theoretic Analysis
Byung-Gon Chun, Kamalika Chaudhuri, Hoeteck Wee, Marco Barreno, Christos H. Papadimitriou, and John Kubiatowicz. Selfish Caching in Distributed Systems: A Game-Theoretic Analysis. ACM Symposium on Principles of Distributed Computing (PODC 2004), July 2004.
Characterizing Selfishly Constructed Overlay Routing Networks
Byung-Gon Chun, Rodrigo Fonseca, Ion Stoica, and John Kubiatowicz. Characterizing Selfishly Constructed Overlay Routing Networks. IEEE International Conference on Computer Communications (INFOCOM 2004), March 2004.
Evaluation of Packet Scheduling Algorithms in Mobile Ad Hoc Networks
Byung-Gon Chun and Mary Baker. Evaluation of Packet Scheduling Algorithms in Mobile Ad Hoc Networks. ACM Mobile Computing and Communications Review (MC2R 2002), Volume 6, Number 3, July 2002.
Auxiliary Timeout and Selective Packet Discard Schemes to Improve TCP Performance in PCN Environment
Byung-Gon Chun and Byeong Gi Lee. Auxiliary Timeout and Selective Packet Discard Schemes to Improve TCP Performance in PCN Environment. IEEE International Conference on Communications (ICC 1997), June 1997.
Alleviating Garbage Collection Overhead in Java Virtual Machine-Based Data Processing System by Utilizing Off-Heap Memory
Haeyoon Cho, Gyewon Lee and Byung-Gon Chun, KSC, December 2019
A Survey on Pruning and Sharing DNN Parameters for Compression and Acceleration
Hyeonmin Ha and Byung-Gon Chun, KSC, December 2019
Lambda Executor: extend Apache Nemo Executor with serverless functions
Zhiyuan Gao, Taegeon Um and Byung-Gon Chun, KSC, December 2019
Performance Analysis on Varying Parallelism for Different Distributed Data Processing Workloads
Won Wook Song and Byung-Gon Chun, KSC, December 2019
Survey on Solutions to Communication Bottleneck in Data-Parallel Distributed Deep Neural Network Training
Kyunggeun Lee and Byung-Gon Chun, KSC, December 2019
Challenges in Memory Optimization for Deep Learning Model Training
Jeongyoon Eo, Taegeon Um, and Byung-Gon Chun, KSC, December 2019
Serverless Computing: Pitfalls and Solutions Zhiyuan Gao, Taegeon Um, and Byung-Gon Chun, KCC, June 2019
Investigating Inefficiencies and Proposing Improvements towards Real-Time Action Recognition with Two-Stream Networks Haa Kyung Lee, Yunseong Lee, and Byung-Gon Chun, KCC, June 2019
Our Experiences on Enabling Advanced Storage and Network I/O via Apache Crail for Distributed Data Processing System Haeyoon Cho, Jeongyoon Eo, and Byung-Gon Chun, KCC, June 2019
Effectiveness of Using Off-heap Memory in Java Virtual Machine-based Stream Processing System Jangho Seo, Won Wook Song, and Byung-Gon Chun, KCC, June 2019
Latency and Completeness Compare Between Event-time Processing and Processing-time Processing on Stream Processing System Jueun Lee, Gyewon Lee, and Byung-Gon Chun, KCC, June 2019
Performance Analysis of Apache Flink with RocksDB on Stream Queries with List State Gyewon Lee and Byung-Gon Chun, KSC, December 2018
Survey on Distributed Training in Imperative Deep Learning Frameworks DongJin Shin and Byung-Gon Chun, KSC, December 2018
Recent Trends in Resource Management Systems for Distributed Deep Learning Training Sungwoo Cho and Byung-Gon Chun, KSC, December 2018
A Survey on Model-Parallel Techniques for Deep Learning Models Soojeong Kim and Byung-Gon Chun, KSC, December 2018
Dynamic Optimization Policies in Distributed Data Processing Frameworks Youngseok Yang and Byung-Gon Chu, KSC, December 2018
Survey of Deep Learning Workload Support in Apache Spark Woo-Yeon Lee and Byung-Gon Chun, KSC, December 2018
Our Experiences on Creating an Apache Beam Runner for Distributed Data Processing Won Wook Song and Byung-Gon Chun, KSC, December 2018
Impact of Compressing Shuffle Data on Data Analytics Workload Jangho Seo and Byung-Gon Chun, KSC, December 2018
Mitigating the Cold-Start Problem of Serverless Computation Taegeon Um and Byung-Gon Chun, KSC, December 2018
Adversarial Example Generation for DNNs with Non-differentiable Objective Functions Hyeonmin Ha and Byung-Gon Chun, KSC, December 2018
Recent Trends in Resource Disaggregation for Datacenters Jeongyoon Eo and Byung-Gon Chun, KCC, June 2018
Optimizing Shuffle Performance for Small-scale Data Analysis using Linux zRAM JangHo Seo and Byung-Gon Chun, KCC, June 2018
Comparison of Programming Interfaces of Distributed Data Processing Systems
Won Wook Song and Byung-Gon Chun, KCC, June 2018
A Comparison of Resource Scheduling Strategies for Mutiple Machine Learning Training Jobs in a Shared Cluster Woo Yeon Lee and Byung-Gon Chun, KCC, June 2018
Reducing Memory Overhead in JVM-based Data Processing Systems Through Merging Small Data Elements Youngseok Yang and Byung-Gon Chun, KCC, June 2018
A Comparison of Metadata Handling Methods for a Disaggregated Storage Environment in a Distributed Data Processing System Sanha Lee and Byung-Gon Chun, KCC, June 2018
A survey on fault-recovery techniques on distributed stream processing systems Gyewon Lee and Byung-Gon Chun, KCC, June 2018
A Comparison of Pull and Push Dataflow Models for Small Analytical Workload Scales
Jooyeon Lee and Byung-Gon Chun, KSC, December 2017