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使用强化学习调度时变工作负载 |AAAI人工智能会议论文集
Source: ojs.aaai.org
Article summary:
1. This article proposes a deep reinforcement learning (DRL) based approach to utilize the various temporal resource usage patterns of time-varying workloads running on shared computing clusters.
2. The authors also present a technique for creating equivalence classes in large production workloads to improve the scalability of their method.
3. Validation on actual production traces from Google and Alibaba shows that their technique can significantly improve cluster performance metrics such as utilization, fragmentation, and resource exhaustion compared to baselines.
Article analysis:
The article is generally trustworthy and reliable, as it provides evidence for its claims in the form of validation on actual production traces from Google and Alibaba. The authors have also provided clear definitions for key terms used throughout the paper, such as “time-varying workloads” and “shared computing clusters”, which helps readers understand the context of the paper more easily. Furthermore, the authors have presented both sides of an argument fairly by providing counterarguments to their own claims and exploring unexplored counterarguments.
However, there are some potential biases in the article that should be noted. For example, while the authors provide evidence for their claims in terms of validation on actual production traces from Google and Alibaba, they do not provide any evidence or data from other sources or companies that could help support their claims further. Additionally, while they explore unexplored counterarguments to their own claims, they do not provide any evidence or data to back up these counterarguments either. Finally, there is no mention of possible risks associated with using deep reinforcement learning (DRL) based approaches for scheduling time-varying workloads on shared computing clusters; this should be addressed in future research papers on this topic.