With the rapid advancement of information technology, the digital space has become humanity's “third living space.” However,this new space has also created security risks, exhibiting novel national security and ...With the rapid advancement of information technology, the digital space has become humanity's “third living space.” However,this new space has also created security risks, exhibiting novel national security and development-related characteristics. In such a context,digital sovereignty encompasses dominance over digital technologies,rule-making authority in the digital realm, a voice in the digital space,and the right to develop the digital economy. Many countries and regions worldwide, such as the United States, the European Union, and China,have placed extreme importance on digital sovereignty, as evidenced by their adoption of relevant strategies. China faces four barriers that hinder its efforts to enhance digital sovereignty: path dependence,technology maturity, generalized functional security, and energy consumption. China urgently needs to promote information technology innovation at the paradigm level, facilitate much-needed breakthroughs in ground-breaking technologies, and build an independent knowledge system with Chinese characteristics regarding the digital space. Only in this manner can China forge a new path of digitalization to consolidate its digital sovereignty and advance its modernization.展开更多
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
文摘With the rapid advancement of information technology, the digital space has become humanity's “third living space.” However,this new space has also created security risks, exhibiting novel national security and development-related characteristics. In such a context,digital sovereignty encompasses dominance over digital technologies,rule-making authority in the digital realm, a voice in the digital space,and the right to develop the digital economy. Many countries and regions worldwide, such as the United States, the European Union, and China,have placed extreme importance on digital sovereignty, as evidenced by their adoption of relevant strategies. China faces four barriers that hinder its efforts to enhance digital sovereignty: path dependence,technology maturity, generalized functional security, and energy consumption. China urgently needs to promote information technology innovation at the paradigm level, facilitate much-needed breakthroughs in ground-breaking technologies, and build an independent knowledge system with Chinese characteristics regarding the digital space. Only in this manner can China forge a new path of digitalization to consolidate its digital sovereignty and advance its modernization.
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.
