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 wh...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.展开更多
以一款插电式燃料电池电动汽车(plug-in fuel cell electric vehicle,PFCEV)为研究对象,为改善燃料电池氢气消耗和电池电量消耗之间的均衡,实现插电式燃料电池电动汽车的燃料电池与动力电池之间的最优能量分配,考虑燃料电池汽车实时能...以一款插电式燃料电池电动汽车(plug-in fuel cell electric vehicle,PFCEV)为研究对象,为改善燃料电池氢气消耗和电池电量消耗之间的均衡,实现插电式燃料电池电动汽车的燃料电池与动力电池之间的最优能量分配,考虑燃料电池汽车实时能量分配的即时回报及未来累积折扣回报,以整车作为环境,整车控制作为智能体,提出了一种基于增强学习算法的插电式燃料电池电动汽车能量管理控制策略.通过Matlab/Simulink建立整车仿真模型对所提出的策略进行仿真验证,相比于基于规则的策略,在不同行驶里程下,电池均可保持一定的电量,整车的综合能耗得到明显降低,在100、200和300 km行驶里程下整车百公里能耗分别降低8.84%、29.5%和38.6%;基于快速原型开发平台进行硬件在环试验验证,城市行驶工况工况下整车综合能耗降低20.8%,硬件在环试验结果与仿真结果基本一致,表明了所制定能量管理策略的有效性和可行性.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.61572520 and 61521003)。
文摘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.
