With the growing amount of information and data, object-oriented storage systems have been widely used in many applications, including the Google File System, Amazon S3, Hadoop Distributed File System, and Ceph, in which load balancing of metadata plays an important role in improving the input/output performance of the entire system. Unbalanced load on the metadata server leads to a serious bottleneck problem for system performance. However, most existing metadata load balancing strategies, which are based on subtree segmentation or hashing, lack good dynamics and adaptability. In this study, we propose a metadata dynamic load balancing (MDLB) mechanism based on reinforcement learning (RL). We learn that the Q_learning algorithm and our RL-based strategy consist of three modules, i.e., the policy selection network, load balancing network, and parameter update network. Experimental results show that the proposed MDLB algorithm can adjust the load dynamically according to the performance of the metadata servers, and that it has good adaptability in the case of sudden change of data volume.

随着信息和数据量的增长，面向对象的存储系统已广泛用于许多应用程序中，包括Google File System，Amazon S3，Hadoop Distributed File System和Ceph，在这些应用程序中，元数据的负载平衡在其中起着重要的作用。改善整个系统的输入/输出性能。元数据服务器上的不平衡负载导致严重的系统性能瓶颈问题。但是，大多数现有的基于子树分段或哈希的元数据负载平衡策略都缺乏良好的动态性和适应性。在这项研究中，我们提出了一种基于强化学习（RL）的元数据动态负载平衡（MDLB）机制。我们了解到Q_learning算法和基于RL的策略由三个模块组成，即策略选择网络，负载平衡网络，和参数更新网络。实验结果表明，所提出的MDLB算法可以根据元数据服务器的性能动态调整负载，并且在数据量突然变化的情况下具有良好的适应性。