Hybrid storage systems (HSS) use multiple different storage devices to
provide high and scalable storage capacity at high performance. Recent research
proposes various techniques that aim to accurately identify
performance-critical data to place it in a "best-fit" storage device.
Unfortunately, most of these techniques are rigid, which (1) limits their
adaptivity to perform well for a wide range of workloads and storage device
configurations, and (2) makes it difficult for designers to extend these
techniques to different storage system configurations (e.g., with a different
number or different types of storage devices) than the configuration they are
designed for. We introduce Sibyl, the first technique that uses reinforcement
learning for data placement in hybrid storage systems. Sibyl observes different
features of the running workload as well as the storage devices to make
system-aware data placement decisions. For every decision it makes, Sibyl
receives a reward from the system that it uses to evaluate the long-term
performance impact of its decision and continuously optimizes its data
placement policy online. We implement Sibyl on real systems with various HSS
configurations. Our results show that Sibyl provides 21.6%/19.9% performance
improvement in a performance-oriented/cost-oriented HSS configuration compared
to the best previous data placement technique. Our evaluation using an HSS
configuration with three different storage devices shows that Sibyl outperforms
the state-of-the-art data placement policy by 23.9%-48.2%, while significantly
reducing the system architect's burden in designing a data placement mechanism
that can simultaneously incorporate three storage devices. We show that Sibyl
achieves 80% of the performance of an oracle policy that has complete knowledge
of future access patterns while incurring a very modest storage overhead of
only 124.4 KiB.

Sibyl 是一种用于混合存储系统的数据放置策略技术，使用基于强化学习的方法通过对运行负载的不同特征以及存储设备的观测使系统更加自适应，进而优化性能，并在实际系统中的不同配置上得到了验证。

Sibyl: 使用在线强化学习在混合存储系统中实现自适应和可扩展的数据放置

Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning

Distributed computing excels at processing large scale data, but the
communication cost for synchronizing the shared parameters may slow down the
overall performance. Fortunately, the interactions between parameter and data
in many problems are sparse, which admits efficient partition in order to
reduce the communication overhead.
In this paper, we formulate data placement as a graph partitioning problem.
We propose a distributed partitioning algorithm. We give both theoretical
guarantees and a highly efficient implementation. We also provide a highly
efficient implementation of the algorithm and demonstrate its promising results
on both text datasets and social networks. We show that the proposed algorithm
leads to 1.6x speedup of a state-of-the-start distributed machine learning
system by eliminating 90\% of the network communication.

该论文提出了一种基于图分割的分布式计算算法，其可以有效降低数据分布式处理过程中的通信成本，实验结果表明该算法能够在机器学习系统中实现 1.6 倍的加速，并且消除 90％的网络通信。