Chapter 02

Year 2020

ATC 2020(https://www.usenix.org/conference/atc20/technical-sessions)

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider(https://www.usenix.org/conference/atc20/presentation/shahrad)
- Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini, Microsoft Azure and Microsoft Research
- Summary: Since many ML services are stateless, FaaS is a good chance to reduce cost. This paper analyzes the real-wolrd workload on FaaS and provides a good dataset.
Lessons Learned from the Chameleon Testbed(https://www.usenix.org/conference/atc20/presentation/keahey)
- Kate Keahey, Argonne National Laboratory; Jason Anderson and Zhuo Zhen, University of Chicago; Pierre Riteau, StackHPC Ltd; Paul Ruth, RENCI UNC Chapel Hill; Dan Stanzione, Texas Advanced Computing Center; Mert Cevik, RENCI UNC Chapel Hill; Jacob Colleran and Haryadi S. Gunawi, University of Chicago; Cody Hammock, Texas Advanced Computing Center; Joe Mambretti, Northwestern University; Alexander Barnes, François Halbah, Alex Rocha, and Joe Stubbs, Texas Advanced Computing Center
Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads(https://www.usenix.org/conference/atc20/presentation/yuan)
- Gina Yuan, Shoumik Palkar, Deepak Narayanan, and Matei Zaharia, Stanford University
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism(https://www.usenix.org/conference/atc20/presentation/park)
- Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, and Seungmin Lee, UNIST; Jaesik Choi, KAIST; Sam H. Noh and Young-ri Choi, UNIST
AutoSys: The Design and Operation of Learning-Augmented Systems(https://www.usenix.org/conference/atc20/presentation/liang-mike)
- Chieh-Jan Mike Liang, Hui Xue, Mao Yang, and Lidong Zhou, Microsoft Research; Lifei Zhu, Peking University and Microsoft Research; Zhao Lucis Li and Zibo Wang, University of Science and Technology of China and Microsoft Research; Qi Chen and Quanlu Zhang, Microsoft Research; Chuanjie Liu, Microsoft Bing Platform; Wenjun Dai, Microsoft Bing Ads
Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training(https://www.usenix.org/conference/atc20/presentation/zhu-hongyu)
- Hongyu Zhu, University of Toronto & Vector Institute; Amar Phanishayee, Microsoft Research; Gennady Pekhimenko, University of Toronto & Vector Institute
ALERT: Accurate Learning for Energy and Timeliness(https://www.usenix.org/conference/atc20/presentation/wan)
- Chengcheng Wan, Muhammad Santriaji, Eri Rogers, Henry Hoffmann, Michael Maire, and Shan Lu, University of Chicago
NeuOS: A Latency-Predictable Multi-Dimensional Optimization Framework for DNN-driven Autonomous Systems(https://www.usenix.org/conference/atc20/presentation/bateni)
- Soroush Bateni and Cong Liu, University of Texas at Dallas
PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning(https://www.usenix.org/conference/atc20/presentation/din)
- Zainul Abi Din, UC Davis; Panagiotis Tigas, University of Oxford; Samuel T. King, UC Davis, Bouncer Technologies; Benjamin Livshits, Brave Software, Imperial College London

ICLR 2020: Challenges in Deploying and Monitoring Machine Learning Systems(https://icml.cc/Conferences/2020/Schedule?showEvent=5738)

MLsys 2020(https://mlsys.org/Conferences/2020/ScheduleMultitrack?text=&session=&event_type=&day=)

Most of the papers deserve to be read.

NSDI 2020

Gandalf: An Intelligent, End-To-End Analytics Service for Safe Deployment in Large-Scale Cloud Infrastructure(https://www.usenix.org/system/files/nsdi20-paper-li.pdf)
- Ze Li, Qian Cheng, Ken Hsieh, and Yingnong Dang, Microsoft Azure; Peng Huang, Johns Hopkins University; Pankaj Singh and Xinsheng Yang, Microsoft Azure; Qingwei Lin, Microsoft Research; Youjiang Wu, Sebastien Levy, and Murali Chintalapati, Microsoft Azure
Telekine: Secure Computing with Cloud GPUs(https://www.usenix.org/system/files/nsdi20-paper-hunt.pdf)
- Tyler Hunt, Zhipeng Jia, Vance Miller, Ariel Szekely, and Yige Hu, The University of Texas at Austin; Christopher J. Rossbach, The University of Texas at Austin and VMware Research; Emmett Witchel, The University of Texas at Austin
Rex: Preventing Bugs and Misconfiguration in Large Services Using Correlated Change Analysis(https://www.usenix.org/system/files/nsdi20-paper-mehta.pdf)
- Sonu Mehta, Ranjita Bhagwan, and Rahul Kumar, Microsoft Research India; Chetan Bansal, Microsoft Research; Chandra Maddila and B. Ashok, Microsoft Research India; Sumit Asthana, University of Michigan; Christian Bird, Microsoft Research; Aditya Kumar
Themis: Fair and Efficient GPU Cluster Scheduling(https://www.usenix.org/system/files/nsdi20-paper-mahajan.pdf)
- Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, and Aditya Akella, University of Wisconsin-Madison; Amar Phanishayee, Microsoft Research; Shuchi Chawla, University of Wisconsin-Madison

Eurosys 2020

Balancing Efficiency and Fairness in Heterogeneous GPU Clusters for Deep Learning(https://dl.acm.org/doi/pdf/10.1145/3342195.3387555)
- Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Srinidhi Viswanatha (Microsoft Research India)
Experiences of Landing Machine Learning onto Market-Scale Mobile Malware Detection(https://dl.acm.org/doi/pdf/10.1145/3342195.3387530)
- Liangyi Gong, Zhenhua Li (Tsinghua University), Feng Qian (University of Minnesota, Twin Cities), Zifan Zhang (Tsinghua University & Tencent Co. LTD), Qi Alfred Chen, Zhiyun Qian (University of California, Riverside), Hao Lin (Tsinghua University), Yunhao Liu (Tsinghua University & Michigan State University)
Autopilot: workload autoscaling at Google(https://dl.acm.org/doi/pdf/10.1145/3342195.3387524?download=false)
- Krzysztof Rzadca (Google, University of Warsaw), Pawel Findeisen, Jacek Swiderski, Przemyslaw Zych, Przemyslaw Broniek, Jarek Kusmierek, Pawel Nowak, Beata Strack, Piotr Witusowski, Steven Hand, John Wilkes (Google)
Borg: the Next Generation(https://dl.acm.org/doi/pdf/10.1145/3342195.3387517)
- Muhammad Tirmazi (Harvard University), Adam Barker (Google and University of St Andrews), Nan Deng, Md Ehtesam Haque, Zhijing Gene Qin, Steven Hand (Google), Mor Harchol-Balter (Carnegie Mellon University), John Wilkes (Google)
AlloX: Compute Allocation in Hybrid Clusters(https://www.mosharaf.com/wp-content/uploads/allox-eurosys20.pdf)
- Tan N. Le (SUNY Korea, Stony Brook University), Xiao Sun (Stony Brook University), Mosharaf Chowdhury (University of Michigan), Zhenhua Liu (Stony Brook University)
Env2Vec: Accelerating VNF Testing with Deep Learning(https://dl.acm.org/doi/abs/10.1145/3342195.3387525)
- Guangyuan Piao, Patrick K. Nicholson, Diego Lugones (Nokia Bell Labs)

Year 2019

MLSys 2019(https://proceedings.mlsys.org/book/2019)

ATC 2019

The Design and Operation of CloudLab(https://www.usenix.org/system/files/atc19-duplyakin_0.pdf)
- Dmitry Duplyakin, Robert Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, Leigh Stoller, Mike Hibler, David Johnson, and Kirk Webb, University of Utah; Aditya Akella, University of Wisconsin—Madison; Kuangching Wang, Clemson University; Glenn Ricart, US Ignite; Larry Landweber, University of Wisconsin—Madison; Chip Elliott, Raytheon; Michael Zink and Emmanuel Cecchet, University of Massachusetts Amherst; Snigdhaswin Kar and Prabodh Mishra, Clemson University
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs(https://www.usenix.org/system/files/atc19-ma_0.pdf)
- Lingxiao Ma and Zhi Yang, Peking University; Youshan Miao, Jilong Xue, Ming Wu, and Lidong Zhou, Microsoft Research; Yafei Dai, Peking University
Optimizing CNN Model Inference on CPUs(https://www.usenix.org/system/files/atc19-liu-yizhi.pdf)
- Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang, Amazon
Accelerating Rule-matching Systems with Learned Rankers(https://www.usenix.org/system/files/atc19-li-zhao-lucas.pdf)
- Zhao Lucis Li, University of Science and Technology China; Chieh-Jan Mike Liang and Wei Bai, Microsoft Research; Qiming Zheng, Shanghai Jiao Tong University; Yongqiang Xiong, Microsoft Research; Guangzhong Sun, University of Science and Technology China
MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving(https://www.usenix.org/system/files/atc19-zhang-chengliang.pdf)
- Chengliang Zhang, Minchen Yu, and Wei Wang, Hong Kong University of Science and Technology; Feng Yan, University of Nevada, Reno
Cross-dataset Time Series Anomaly Detection for Cloud Systems(https://www.usenix.org/system/files/atc19-zhang-xu.pdf)
- Xu Zhang, Microsoft Research, Nanjing University; Qingwei Lin, Yong Xu, and Si Qin, Microsoft Research; Hongyu Zhang, The University of Newcastle; Bo Qiao, Microsoft Research; Yingnong Dang, Xinsheng Yang, Qian Cheng, Murali Chintalapati, Youjiang Wu, and Ken Hsieh, Microsoft; Kaixin Sui, Xin Meng, Yaohai Xu, and Wenchi Zhang, Microsoft Research; Furao Shen, Nanjing University; Dongmei Zhang, Microsoft Research

Year 2020

Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider(https://www.usenix.org/conference/atc20/presentation/shahrad)

Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini, Microsoft Azure and Microsoft Research
Summary: Since many ML services are stateless, FaaS is a good chance to reduce cost. This paper analyzes the real-wolrd workload on FaaS and provides a good dataset.

Lessons Learned from the Chameleon Testbed(https://www.usenix.org/conference/atc20/presentation/keahey)

Kate Keahey, Argonne National Laboratory; Jason Anderson and Zhuo Zhen, University of Chicago; Pierre Riteau, StackHPC Ltd; Paul Ruth, RENCI UNC Chapel Hill; Dan Stanzione, Texas Advanced Computing Center; Mert Cevik, RENCI UNC Chapel Hill; Jacob Colleran and Haryadi S. Gunawi, University of Chicago; Cody Hammock, Texas Advanced Computing Center; Joe Mambretti, Northwestern University; Alexander Barnes, François Halbah, Alex Rocha, and Joe Stubbs, Texas Advanced Computing Center

Offload Annotations: Bringing Heterogeneous Computing to Existing Libraries and Workloads(https://www.usenix.org/conference/atc20/presentation/yuan)

Gina Yuan, Shoumik Palkar, Deepak Narayanan, and Matei Zaharia, Stanford University

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism(https://www.usenix.org/conference/atc20/presentation/park)

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, and Seungmin Lee, UNIST; Jaesik Choi, KAIST; Sam H. Noh and Young-ri Choi, UNIST

AutoSys: The Design and Operation of Learning-Augmented Systems(https://www.usenix.org/conference/atc20/presentation/liang-mike)

Chieh-Jan Mike Liang, Hui Xue, Mao Yang, and Lidong Zhou, Microsoft Research; Lifei Zhu, Peking University and Microsoft Research; Zhao Lucis Li and Zibo Wang, University of Science and Technology of China and Microsoft Research; Qi Chen and Quanlu Zhang, Microsoft Research; Chuanjie Liu, Microsoft Bing Platform; Wenjun Dai, Microsoft Bing Ads

Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training(https://www.usenix.org/conference/atc20/presentation/zhu-hongyu)

Hongyu Zhu, University of Toronto & Vector Institute; Amar Phanishayee, Microsoft Research; Gennady Pekhimenko, University of Toronto & Vector Institute

ALERT: Accurate Learning for Energy and Timeliness(https://www.usenix.org/conference/atc20/presentation/wan)

Chengcheng Wan, Muhammad Santriaji, Eri Rogers, Henry Hoffmann, Michael Maire, and Shan Lu, University of Chicago

NeuOS: A Latency-Predictable Multi-Dimensional Optimization Framework for DNN-driven Autonomous Systems(https://www.usenix.org/conference/atc20/presentation/bateni)

Soroush Bateni and Cong Liu, University of Texas at Dallas

PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning(https://www.usenix.org/conference/atc20/presentation/din)

Zainul Abi Din, UC Davis; Panagiotis Tigas, University of Oxford; Samuel T. King, UC Davis, Bouncer Technologies; Benjamin Livshits, Brave Software, Imperial College London

Most of the papers deserve to be read.

NSDI 2020

Gandalf: An Intelligent, End-To-End Analytics Service for Safe Deployment in Large-Scale Cloud Infrastructure(https://www.usenix.org/system/files/nsdi20-paper-li.pdf)

Ze Li, Qian Cheng, Ken Hsieh, and Yingnong Dang, Microsoft Azure; Peng Huang, Johns Hopkins University; Pankaj Singh and Xinsheng Yang, Microsoft Azure; Qingwei Lin, Microsoft Research; Youjiang Wu, Sebastien Levy, and Murali Chintalapati, Microsoft Azure

Telekine: Secure Computing with Cloud GPUs(https://www.usenix.org/system/files/nsdi20-paper-hunt.pdf)

Tyler Hunt, Zhipeng Jia, Vance Miller, Ariel Szekely, and Yige Hu, The University of Texas at Austin; Christopher J. Rossbach, The University of Texas at Austin and VMware Research; Emmett Witchel, The University of Texas at Austin

Rex: Preventing Bugs and Misconfiguration in Large Services Using Correlated Change Analysis(https://www.usenix.org/system/files/nsdi20-paper-mehta.pdf)

Sonu Mehta, Ranjita Bhagwan, and Rahul Kumar, Microsoft Research India; Chetan Bansal, Microsoft Research; Chandra Maddila and B. Ashok, Microsoft Research India; Sumit Asthana, University of Michigan; Christian Bird, Microsoft Research; Aditya Kumar

Themis: Fair and Efficient GPU Cluster Scheduling(https://www.usenix.org/system/files/nsdi20-paper-mahajan.pdf)

Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, and Aditya Akella, University of Wisconsin-Madison; Amar Phanishayee, Microsoft Research; Shuchi Chawla, University of Wisconsin-Madison

Eurosys 2020

Balancing Efficiency and Fairness in Heterogeneous GPU Clusters for Deep Learning(https://dl.acm.org/doi/pdf/10.1145/3342195.3387555)

Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Srinidhi Viswanatha (Microsoft Research India)

Experiences of Landing Machine Learning onto Market-Scale Mobile Malware Detection(https://dl.acm.org/doi/pdf/10.1145/3342195.3387530)

Liangyi Gong, Zhenhua Li (Tsinghua University), Feng Qian (University of Minnesota, Twin Cities), Zifan Zhang (Tsinghua University & Tencent Co. LTD), Qi Alfred Chen, Zhiyun Qian (University of California, Riverside), Hao Lin (Tsinghua University), Yunhao Liu (Tsinghua University & Michigan State University)

Autopilot: workload autoscaling at Google(https://dl.acm.org/doi/pdf/10.1145/3342195.3387524?download=false)

Krzysztof Rzadca (Google, University of Warsaw), Pawel Findeisen, Jacek Swiderski, Przemyslaw Zych, Przemyslaw Broniek, Jarek Kusmierek, Pawel Nowak, Beata Strack, Piotr Witusowski, Steven Hand, John Wilkes (Google)

Borg: the Next Generation(https://dl.acm.org/doi/pdf/10.1145/3342195.3387517)

Muhammad Tirmazi (Harvard University), Adam Barker (Google and University of St Andrews), Nan Deng, Md Ehtesam Haque, Zhijing Gene Qin, Steven Hand (Google), Mor Harchol-Balter (Carnegie Mellon University), John Wilkes (Google)

AlloX: Compute Allocation in Hybrid Clusters(https://www.mosharaf.com/wp-content/uploads/allox-eurosys20.pdf)

Tan N. Le (SUNY Korea, Stony Brook University), Xiao Sun (Stony Brook University), Mosharaf Chowdhury (University of Michigan), Zhenhua Liu (Stony Brook University)

Env2Vec: Accelerating VNF Testing with Deep Learning(https://dl.acm.org/doi/abs/10.1145/3342195.3387525)

Guangyuan Piao, Patrick K. Nicholson, Diego Lugones (Nokia Bell Labs)

Year 2019

ATC 2019

The Design and Operation of CloudLab(https://www.usenix.org/system/files/atc19-duplyakin_0.pdf)

Dmitry Duplyakin, Robert Ricci, Aleksander Maricq, Gary Wong, Jonathon Duerig, Eric Eide, Leigh Stoller, Mike Hibler, David Johnson, and Kirk Webb, University of Utah; Aditya Akella, University of Wisconsin—Madison; Kuangching Wang, Clemson University; Glenn Ricart, US Ignite; Larry Landweber, University of Wisconsin—Madison; Chip Elliott, Raytheon; Michael Zink and Emmanuel Cecchet, University of Massachusetts Amherst; Snigdhaswin Kar and Prabodh Mishra, Clemson University

NeuGraph: Parallel Deep Neural Network Computation on Large Graphs(https://www.usenix.org/system/files/atc19-ma_0.pdf)

Lingxiao Ma and Zhi Yang, Peking University; Youshan Miao, Jilong Xue, Ming Wu, and Lidong Zhou, Microsoft Research; Yafei Dai, Peking University

Optimizing CNN Model Inference on CPUs(https://www.usenix.org/system/files/atc19-liu-yizhi.pdf)

Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang, Amazon

Accelerating Rule-matching Systems with Learned Rankers(https://www.usenix.org/system/files/atc19-li-zhao-lucas.pdf)

Zhao Lucis Li, University of Science and Technology China; Chieh-Jan Mike Liang and Wei Bai, Microsoft Research; Qiming Zheng, Shanghai Jiao Tong University; Yongqiang Xiong, Microsoft Research; Guangzhong Sun, University of Science and Technology China

MArk: Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving(https://www.usenix.org/system/files/atc19-zhang-chengliang.pdf)

Chengliang Zhang, Minchen Yu, and Wei Wang, Hong Kong University of Science and Technology; Feng Yan, University of Nevada, Reno

Cross-dataset Time Series Anomaly Detection for Cloud Systems(https://www.usenix.org/system/files/atc19-zhang-xu.pdf)

Xu Zhang, Microsoft Research, Nanjing University; Qingwei Lin, Yong Xu, and Si Qin, Microsoft Research; Hongyu Zhang, The University of Newcastle; Bo Qiao, Microsoft Research; Yingnong Dang, Xinsheng Yang, Qian Cheng, Murali Chintalapati, Youjiang Wu, and Ken Hsieh, Microsoft; Kaixin Sui, Xin Meng, Yaohai Xu, and Wenchi Zhang, Microsoft Research; Furao Shen, Nanjing University; Dongmei Zhang, Microsoft Research