用强化学习自动调参，实现云系统数据复制与扩展。
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems
- 用强化学习替代人工设定阈值，实现自动数据复制。
- 可适应不同负载趋势，减少人工干预需求。
- 适合需要自适应资源管理的云服务场景。
鉴于其直观性，许多云服务商采用基于阈值的数据复制策略来实现自动资源扩展。然而，有效设置阈值需要人工干预以校准每个指标的阈值，并需深入理解当前工作负载趋势，这往往难以实现。强化学习在云计算相关领域已有广泛应用，是实现自动数据复制策略的有前景方向。本文综述了基于强化学习（RL）的数据复制与数据扩展策略。
原文摘要 · Abstract (English)
Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL).
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