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Year 2020

ATC 2020 [Program]

  • Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • Soroush Bateni and Cong Liu, University of Texas at Dallas
  • PERCIVAL: Making In-Browser Perceptual Ad Blocking Practical with Deep Learning [Paper]
    • 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 [Workshop]

MLsys 2020 [All Papers]

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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, Srinidhi Viswanatha (Microsoft Research India)
  • Experiences of Landing Machine Learning onto Market-Scale Mobile Malware Detection [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • Guangyuan Piao, Patrick K. Nicholson, Diego Lugones (Nokia Bell Labs)

Year 2019

MLSys 2019 [Proceedings]

ATC 2019

  • The Design and Operation of CloudLab [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • Yizhi Liu, Yao Wang, Ruofei Yu, Mu Li, Vin Sharma, and Yida Wang, Amazon
  • Accelerating Rule-matching Systems with Learned Rankers [Paper]
    • 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 [Paper]
    • 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 [Paper]
    • 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