论文标题
Phoebe：新兴存储模型的重复使用在线缓存，并进行增强学习
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models
论文作者
论文摘要
随着数据耐用性，高访问速度，低功率效率和字节可寻址性，NVME和SSD已被公认的新兴存储技术代表得到广泛应用。但是，这些技术采用高性能的一个关键问题是如何正确定义智能高速缓存层，以便可以很好地桥接新兴技术和主内存之间的性能差距。为此，我们提出了Phoebe，这是一种适用于广泛的新兴存储模型的最佳在线缓存的重复使用的增强学习框架。通过与缓存环境和数据流进行连续交互，Phoebe能够从单个迹线中提取关键的时间数据依赖关系和相对位置信息，并且随着时间的流逝变得越来越聪明。为了减少在线学习期间的培训开销，我们使用定期培训来摊销成本。 Phoebe在一组Microsoft Cloud Storage Workloads上进行了评估。实验结果表明，Phoebe能够分别缩小LRU的高速缓存失误率和最先进的基于在线学习的高速缓存策略，分别向Belady的最佳政策提高了70.3％和52.6％。
With data durability, high access speed, low power efficiency and byte addressability, NVMe and SSD, which are acknowledged representatives of emerging storage technologies, have been applied broadly in many areas. However, one key issue with high-performance adoption of these technologies is how to properly define intelligent cache layers such that the performance gap between emerging technologies and main memory can be well bridged. To this end, we propose Phoebe, a reuse-aware reinforcement learning framework for the optimal online caching that is applicable for a wide range of emerging storage models. By continuous interacting with the cache environment and the data stream, Phoebe is capable to extract critical temporal data dependency and relative positional information from a single trace, becoming ever smarter over time. To reduce training overhead during online learning, we utilize periodical training to amortize costs. Phoebe is evaluated on a set of Microsoft cloud storage workloads. Experiment results show that Phoebe is able to close the gap of cache miss rate from LRU and a state-of-the-art online learning based cache policy to the Belady's optimal policy by 70.3% and 52.6%, respectively.
