DarwinGame: Playing Tournaments for Tuning Applications in Noisy Cloud Environments
Rohan Basu Roy, Vijay Gadepally, Devesh Tiwari
摘要
This work introduces a new subarea of performance tuning -- performance tuning in a shared interference-prone computing environment. We demonstrate that existing tuners are significantly suboptimal by design because of their inability to account for interference during tuning. Our solution, DarwinGame, employs a tournament-based design to systematically compare application executions with different tunable parameter configurations, enabling it to identify the relative performance of different tunable parameter configurations in a noisy environment. Compared to existing solutions, DarwinGame achieves more than 27% reduction in execution time, with less than 0.5% performance variability. DarwinGame is the first performance tuner that will help developers tune their applications in shared, interference-prone, cloud environments.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper7
- The hidden cost of the edge: a performance comparison of edge and cloud latenciesAhmed Ali-Eldin, Bin Wang, Prashant J. ShenoySC 2021 · 被引用 51 次
- GPTune: multitask learning for autotuning exascale applicationsYang Liu, Wissam M. Sid-Lakhdar, Osni Marques, Xinran Zhu 等PPoPP 2021 · 被引用 45 次
- Bliss: auto-tuning complex applications using a pool of diverse lightweight learning modelsRohan Basu Roy, Tirthak Patel, Vijay Gadepally, Devesh TiwariPLDI 2021 · 被引用 41 次
- High-density Multi-tenant Bare-metal CloudXiantao Zhang, Xiao Zheng, Zhi Wang, Hang Yang 等ASPLOS 2020 · 被引用 39 次
- pLiner: isolating lines of floating-point code for compiler-induced variabilityHui Guo, Ignacio Laguna, Cindy Rubio-GonzálezSC 2020 · 被引用 15 次
相关 Paper
- TUNA: Tuning Unstable and Noisy Cloud ApplicationsJohannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram VenkataramanEuroSys 2025 · 被引用 11 次
- DARWIN: Survival of the Fittest Fuzzing MutatorsPatrick Jauernig, Domagoj Jakobovic, Stjepan Picek, Emmanuel Stapf 等NDSS 2023
- RubberBand: cloud-based hyperparameter tuningUjval Misra, Richard Liaw, Lisa Dunlap, Romil Bhardwaj 等EuroSys 2021 · 被引用 21 次
- HYPERF: End-to-End Autotuning Framework for High-Performance ComputingJuseong Park, Yongwon Shin, Junghyun Lee, Junseo Lee 等HPDC 2025 · 被引用 2 次
- A Spark Optimizer for Adaptive, Fine-Grained Parameter TuningChenghao Lyu, Qi Fan, Philippe Guyard, Yanlei DiaoVLDB 2024 · 被引用 9 次
