NENYA: Cascade Reinforcement Learning for Cost-Aware Failure Mitigation at Microsoft 365
Author:
Affiliation:
1. Microsoft Research, Beijing, UNK, China
2. Microsoft 365, Suzhou, UNK, China
3. Microsoft 365, Suzhou, China
4. Microsoft Azure, Seattle, WA, USA
5. The University of Newcastle, Newcastle, UNK, Australia
6. Microsoft 365, Seattle, WA, USA
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3534678.3539127
Reference19 articles.
1. Predicting Disk Replacement towards Reliable Data Centers
2. Unreliable failure detectors for reliable distributed systems
3. On the quality of service of failure detectors
4. Failures in large scale systems
5. Tuomas Haarnoja , Aurick Zhou , Pieter Abbeel , and Sergey Levine . 2018 . Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor . In Proceedings of ICML 2018 . PMLR, 1861--1870. Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine. 2018. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In Proceedings of ICML 2018. PMLR, 1861--1870.
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