蓝鲸元数据服务器集群的细粒度负载迁移

大数据应用对信息系统的底层存储提出了极大挑战,其首选方案为元数据服务器(metadata server,MDS)集群架构.MDS集群系统为了实现负载均衡,采用的基本机制为元数据服务的负载迁移.当前主流方案存在迁移时间长,迁移中写相关元数据访问需要阻塞的问题.提出细粒度的元数据服务负载迁移方案,由迁出端恢复迁移机制和细粒度控制迁移机制2部分组成.在迁移过程中,以文件为粒度在迁入端重建迁出端的元数据相关状态结构,并根据被访问状态的类型选择在迁出端或迁入端进行立即响应,而非阻塞操作直到迁移完成.在蓝鲸MDS集群系统上实现了此方案,测试和分析表明,在10 000规模的迁移场景中,迁移时间控制在800ms以下,元数据或状态写入延迟最高为215ms.与系统原方案中90s的固有延时相比,此方案有效解决了MDS集群中负载迁移时间过长的问题,并且极大地降低了迁移过程对正常元数据访问的影响.     

基于知识增强的企业实体关系预测模型

随着知识图谱的不断发展,大量应用于工业界的产业知识图谱应运而生。然而,这些产业知识图谱经常缺乏充足的企业关联关系,如上下游关系、供应关系、合作关系、竞争关系等,导致其应用范围受到极大限制。现有企业关系预测研究大多仅关注知识图谱中三元组本身的结构信息,未能充分利用企业文本描述和企业关联实体的描述等多视角信息。为解决该问题,提出了一种基于知识增强的企业实体关系预测模型KERP。模型首先通过多视角实体特征三元组学习,完善企业实体特征表示;其次,利用图注意力网络获取实体的高阶语义表示,并与TransR模型学习的实体关系低阶语义表示进行融合,进一步增强企业实体及其关系的特征表示;最后,通过二维卷积解码器ConvE实现对企业实体关系的预测。在新能源汽车产业知识图谱数据上的实验分析表明,与现有主流实体关系预测模型相比,KERP在预测企业关系上具有更好的效果,在F1值上有6.7%的提升。此外,在多个公开实体关系预测数据集上的实验结果表明,KERP模型在一般化的实体关系预测任务上也具有较好的通用性。     

A Non-Forced-Write Atomic Commit Protocol for Cluster File Systems

Distributed metadata consistency is one of the critical issues of metadata clusters in distributed file systems. Existing methods to maintain metadata consistency generally need several log forced write operations. Since synchronous disk IO is very inefficient, the average response time of metadata operations is greatly increased. In this paper, an asynchronous atomic commit protocol （ACP） named Dual-Log （DL） is presented. It does not need any log forced write operations. Optimizing for distributed metadata operations involving only two metadata servers, DL mutually records the redo log in counterpart metadata servers by transferring through the low latency network. A crashed metadata server can redo the metadata operation with the redundant redo log. Since the latency of the network is much lower than the latency of disk IO, DL can improve the performance of distributed metadata service significantly. The prototype of DL is implemented based on local journal. The performance is tested by comparing with two widely used protocols, EP and S2PC-MP, and the results show that the average response time of distributed metadata operations is reduced by about 40%-60%, and the recovery time is only I second under 10 thousands uncompleted distributed metadata operations.