Indexing

• 8/25: Yandong Mao, Eddie Kohler, Robert Morris. Cache Craftiness for Fast Multicore Key-Value Storage. In Proceedings of the EuroSys Conference (EuroSys’12), Bern, Switzerland, April 2012.
• 8/30: Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. The Case for Learned Index Structures. In Proceedings of the 2018 International Conference on Management of Data (SIGMOD’18), Houston, TX, USA, June 2018.
• 9/1: Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska and Thomas Neumann. SOSD: A Benchmark for Learned Indexes. Proceedings of the VLDB Endowment, Vol. 14, No. 1 (VLDB '21), 2021.
• 9/6: Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Hantian Zhang, Yinan Li, Jaeyoung Do, Donald Kossmann, Johannes Gehrke, David Lomet, Badrish Chandramouli and Tim Kraska. ALEX: An Updatable Adaptive Learned Index. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (SIGMOD '20), June 2020.
• Optional:
  • Ryan Marcus, Emily Zhang and Tim Kraska. CDFShop: Exploring and Optimizing Learned Index Structures. 2020 ACM SIGMOD/PODS (demo).
  • Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Jialin Ding, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, and Vikram Nathan. SageDB: A Learned Database System. CIDR 2019.
  • Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, Tim Kraska. FITing-Tree: A Data-aware Index Structure. Proceedings of the 2019 ACM SIGMOD International Conference on Management of Data (SIGMOD '19), 2019.
  • Chuzhe Tang, Youyun Wang, Zhiyuan Dong, Gansen Hu, Zhaoguo Wang, Minjie Wang, and Haibo Chen. XIndex: A Scalable Learned Index for Multicore Data Storage. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, pages 308–320, 2020.
  • Paolo Ferragina and Giorgio Vinciguerra. The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds. Proceedings of the VLDB Endowment, 13(10):11621175, Jun 2020.
  • Evgenios M. Kornaropoulos, Silei Ren, Roberto Tamassia. The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures. arXiv 2021.

Write-optimized Indexes

• 9/8: Michael A. Bender, Martin Farach-Colton, William Jannen, Rob Johnson, Bradley C. Kuszmaul, Donald E. Porter, Jun Yuan, and Yang Zhan. An Introduction to Bε-trees and Write-optimization. ;login: Operating Systems and Sysadmin, (5):23–28, Oct 2015.
• 9/8: Patrick O'Neil, Edward Cheng, Dieter Gawlick, Elizabeth O'Neil. The Log-Structured Merge-Tree (LSM-Tree). Acta Informatica.
• 9/13: Yifan Dai, Yien Xu, Aishwarya Ganesan, Ramnatthan Alagappan, Brian Kroth, Andrea C. Arpaci-Dusseau, and Remzi H. Arpaci-Dusseau. From Wisckey to Bourbon: A Learned Index for Log-structured Merge Trees. In Proceedings of the 14th Symposium on Operating System and Design Implementation, OSDI ’20, November 2020.
• 9/15: Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. Bigtable: A Distributed Storage System for Structured Data. In Seventh Symposium on Operating System Design and Implementation (OSDI), Seattle, WA, November, 2006.
• 9/15: Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou (Steve) Li, Andy Ly, Christopher Olston. Learned Indexes for a Google-scale Disk-based Database. ML for Systems Workshop 2020.

Remote KV stores

• 9/20: Christopher Mitchell, Kate Montgomery, Lamont Nelson, Siddhartha Sen, and Jinyang Li. Balancing CPU and Network in the Cell Distributed B-Tree Store. In Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC 16), 2016.
• 9/22: Xingda Wei, Rong Chen, and Haibo Chen. Fast RDMA-based Ordered Key-Value Store using Remote Learned Cache. In Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20), 2020.
• Optional:
  • Anuj Kalia, Michael Kaminsky, and David Andersen. Datacenter RPCs can be General and Fast. In 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI 19), pp. 1-16, 2019.

Concurrency control

• 9/27: Xiangyao Yu, Andrew Pavlo, Daniel Sanchez, and Srinivas Devadas. TicToc: Time Traveling Optimistic Concurrency Control. In Proceedings of SIGMOD 16, 2016.
• 9/29: Yangjun Sheng, Anthony Tomasic, Tieying Zhang, Andrew Pavlo. Scheduling OLTP Transactions via Learned Abort Prediction. aiDM19. 2019.
• 10/4: Jiachen Wang, Ding Ding, Huan Wang, Conrad Christensen, Zhaoguo Wang, Haibo Chen, and Jinyang Li. Polyjuice: High-Performance Transactions via Learned Concurrency Control. In Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21), 2021.
• Optional:
  • Yangjun Sheng, Anthony Tomasic, Tieying Zhang, Andrew Pavlo. Scheduling OLTP Transactions via Machine Learning. arXiv 2019.

Scheduling

• 10/6: Robert Grandl, Srikanth Kandula, Sriram Rao, Aditya Akella, Janardhan Kulkarni. GRAPHENE: Packing and Dependency-Aware Scheduling for Data-Parallel Clusters. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 2016.
• 10/11: Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. Learning Scheduling Algorithms for Data Processing Clusters. In Proceedings of SIGCOMM 19. 2019.
• Optional:
  • Di Zhang, Dong Dai, Youbiao He, Forrest Sheng Bao, and Bing Xie. RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning. SC 20.
  • Ravichandra Addanki, Shaileshh Bojja Venkatakrishnan, Shreyan Gupta, Hongzi Mao, and Mohammad Alizadeh. Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning. Neurips 19.

Caching

• 10/13: Zhenyu Song, Daniel S. Berger, Kai Li, and Wyatt Lloyd Learning Relaxed Belady for Content Distribution Network Caching. NSDI20.
• 10/18: Liana Rodriguez, Steven Lyons, Eysler Paz, Raju Rangaswami, Jason Liu, Ming Zhao, and Giri Narasimhan, Learning Cache Replacement with CACHEUS. FAST21.
• 10/18: Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, and Junwhan Ahn. An Imitation Learning Approach for Cache Replacement. ICML20.
• Optional:
  • Thodoris Lykouris, Sergei Vassilvitskii. Competitive Caching with Machine Learned Advice. ICML 2018.
  • Haonan Wang, Hao He, Mohammad Alizadeh, Hongzi Mao. Learning Caching Policies with Subsampling. NeurIPS 2019.

SSDs and storage

• 10/20: Pan Yang, Ni Xue, Yuqi Zhang, Yangxu Zhou, Li Sun, Wenwen Chen, Zhonggang Chen, Wei Xia, Junke Li, and Kihyoun Kwon. Reducing Garbage Collection Overhead in SSD Based on Workload Prediction.. HotStorage19.
• 10/20: Assaf Eisenman, Asaf Cidon, Evgenya Pergament, Or Haimovich, Ryan Stutsman, Mohammad Alizadeh, and Sachin Katti. Flashield: a Hybrid Key-value Cache that Controls Flash Write Amplification.. NSDI19.
• 10/25: Mingzhe Hao, Levent Toksoz, Nanqinqin Li, Edward Edberg Halim, Henry Hoffmann, and Haryadi S. Gunawi LinnOS: Predictability on Unpredictable Flash Storage with a Light Neural Network. OSDI20.
• 10/27: Jisung Park, Jeonggyun Kim, Yeseong Kim, Sungjin Lee, and Onur Mutlu. DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression. FAST22.

Learning in OS

• 11/1: Sepideh Goodarzy, Maziyar Nazari, Richard Han, Eric Keller, and Eric Rozner. SmartOS: Towards Automated Learning and User-adaptive Resource Allocation in Operating Systems.. ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), 2021.
• 11/1: Artemiy Margaritov, Dmitrii Ustiugov, Edouard Bugnion, and Boris Grot. Virtual Address Translation via Learned Page Table Indexes.. Neurips18.
• 11/1: Yiming Qiu, Hongyi Liu, Thomas Anderson, Yingyan Lin, and Ang Chen. Toward reconfigurable kernel datapaths with learned optimizations.. HotOS21.
• Optional:
  • Yiying Zhang and Yutong Huang. “Learned” Operating Systems. Operating Systems Review (OSR19).
  • Ibrahim Umit Akgun, Ali Selman Aydin, and Erez Zadok. KMLIB: Towards Machine Learning for Operating Systems.. In Proceedings of the On-Device Intelligence Workshop, co-located with the MLSys Conference 2020

Other topics

• 11/3 Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. Neural Adaptive Video Streaming with Pensieve. SIGCOMM 17.
• 11/3 Nathan Jay, Noga H. Rotman, P. Brighten Godfrey, Michael Schapira, and Aviv Tamar. A Deep Reinforcement Learning Perspective on Internet Congestion Control. ICML 19.
• 11/10 Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, and Ming Zhang. CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. NSDI17.
• 11/10 Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, and Nesime Tatbul. Neo: A Learned Query Optimizer. VLDB 19.
• 11/15 Yu Gan, Mingyu Liang, Sundar Dev, David Lo, and Christina Delimitrou. Sage: Practical and Scalable ML-Driven Performance Debugging in Microservices. ASPLOS 21.
• 11/15 MSebastien Levy, Randolph Yao, Youjiang Wu, Yingnong Dang, Peng HuangZheng Mu, Pu Zhao, Tarun Ramani, Naga Govindaraju, Xukun LiQingwei Lin, Gil Lapid Shafriri, and Murali Chintalapati. Predictive and Adaptive Failure Mitigation to Avert Production Cloud VM Interruptions. OSDI 20.
• 11/17 Martin Maas, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, and Colin Raffel Learning-based Memory Allocation for C++ Server Workloads. ASPLOS 20.
• 11/17 Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, and Parthasarathy Ranganathan. Learning Memory Access Patterns. ICML 18.
• Optional:
  • Soheil Abbasloo, Chen-Yu Yen, and H. Jonathan Chao. Classic Meets Modern: a Pragmatic Learning-Based Congestion Control for the Internet. SIGCOMM 20.
  • Hongzi Mao, Malte Schwarzkopf, Hao He, and Mohammad Alizadeh. Towards Safe Online Reinforcement Learning in Computer Systems. Neurips 19.
  • Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein. Learning in situ: a randomized experiment in video streaming. NSDI20.
  • Vojislav Ðuki´c, Sangeetha Abdu Jyothi, Bojan Karlaš, Muhsen Owaida, Ce Zhang, and Ankit Singla. Is advance knowledge of flow sizes a plausible assumption?. NSDI19.
  • Yanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou, G. Edward Suh, and Christina Delimitrou. Sinan: ML-Based and QoS-Aware Resource Management for Cloud Microservices. ASPLOS 21.
  • Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska.Bao: Making Learned Query Optimizers Practical. SIGMOD 21.