| Sage: practical and scalable ML-driven performance debugging in microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou Proceedings of the 26th ACM International Conference on Architectural …, 2021 | 134 | 2021 |
| Sage: Leveraging ml to diagnose unpredictable performance in cloud microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou arXiv preprint arXiv:2112.06263, 2021 | 21 | 2021 |
| Sage: Using unsupervised learning for scalable performance debugging in microservices Y Gan, M Liang, S Dev, D Lo, C Delimitrou arXiv preprint arXiv:2101.00267, 2021 | 6 | 2021 |
| Autonomous Warehouse-Scale Computers S Dev, D Lo, L Cheng, P Ranganathan DAC 2020, 2020 | 6 | 2020 |
| Practical and Scalable ML-Driven Cloud Performance Debugging With Sage Y Gan, M Liang, S Dev, D Lo, C Delimitrou IEEE Micro 42 (4), 27-36, 2022 | 3 | 2022 |
| Autonomous warehouse-scale computers D Lo, L Cheng, P Ranganathan, SJ Dev US Patent 11,960,936, 2024 | 1 | 2024 |
| The Importance of Generalizability in Machine Learning for Systems V Gohil, S Dev, G Upasani, D Lo, P Ranganathan, C Delimitrou IEEE Computer Architecture Letters, 2024 | 1 | 2024 |
| Enabling practical cloud performance debugging with unsupervised learning Y Gan, M Liang, S Dev, D Lo, C Delimitrou ACM SIGOPS Operating Systems Review 56 (1), 34-41, 2022 | 1 | 2022 |
David LoGoogle在 alumni.stanford.edu 的电子邮件经过验证
