支持大规模地震探测数据快速可视化的云端数据缓存技术
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摘要
首先,基于云计算应用模式,提出一种能有效利用云存储架构的双层缓存技术.通过在客户端和服务器端建立分布式缓存,能有效避免用户频繁访问远端数据,为用户构建轻量级的客户端,解决了目前地学数据可视化软件大量占用用户本地存储容量的问题.同时服务器端也避免了多次访问云存储文件系统,减少了大量的数据检索与加载时间.其次,提出一种ARLS(association rule last successor)访问预测算法,根据用户的历史访问记录,利用关联规则挖掘用户的访问模式,对其访问行为进行预测,进而提前加载数据,提高缓存命中率,解决了用户在可视化过程中不断移动兴趣区域,频繁更换渲染数据的问题,能有效应对用户具有多种访问模式的情况,提高了预测准确率.实验结果表明,该云存储架构显著减少了本地资源消耗,访问预测算法的准确率在最差情形下可达47.59%,平均准确率达91.3%,分布式缓存的平均缓存命中率达95.61%,可有效支持云端大规模地震数据的快速可视化.
Firstly,based on the cloud computing application model,we proposed a double-layer cache technology which could efficiently utilize the cloud storage architecture.By establishing the distributed cache between the client and the server,it could effectively avoid users frequent access to remote data and build lightweight clients for users,which solved the problem that current geoscience data visualization software occupied a large number of user's local storage capacity,and adapt to the rapid development of mobile devices.In the mean time,the server side also avoided multiple access to the cloud storage file system,reducing a lot of data retrieval and loading time.Secondly,we proposed an association rule last successor access prediction algorithm,according to user's historical accessrecords,the association rules were used to mine the user's access mode,and predict their access behavior.Then the data was loaded in advance,the cache hit rate was improved,we solved the problem of constantly moving region of interest and changing the rendering data frequently in the process of visualization,our system could effectively deal with the user's multiple access patterns case and improve the accuracy of the prediction.Experimental results show that the cloud storage architecture significantly reduces the local resource consumption.The accuracy rate of the access prediction algorithm is 47.59% in the worst case,the average accuracy rate is 91.3%,and the average cache hit rate of distributed cache is 95.61%,which can effectively support the rapid visualization of large-scale seismic data in the cloud.
Firstly,based on the cloud computing application model,we proposed a double-layer cache technology which could efficiently utilize the cloud storage architecture.By establishing the distributed cache between the client and the server,it could effectively avoid users frequent access to remote data and build lightweight clients for users,which solved the problem that current geoscience data visualization software occupied a large number of user's local storage capacity,and adapt to the rapid development of mobile devices.In the mean time,the server side also avoided multiple access to the cloud storage file system,reducing a lot of data retrieval and loading time.Secondly,we proposed an association rule last successor access prediction algorithm,according to user's historical accessrecords,the association rules were used to mine the user's access mode,and predict their access behavior.Then the data was loaded in advance,the cache hit rate was improved,we solved the problem of constantly moving region of interest and changing the rendering data frequently in the process of visualization,our system could effectively deal with the user's multiple access patterns case and improve the accuracy of the prediction.Experimental results show that the cloud storage architecture significantly reduces the local resource consumption.The accuracy rate of the access prediction algorithm is 47.59% in the worst case,the average accuracy rate is 91.3%,and the average cache hit rate of distributed cache is 95.61%,which can effectively support the rapid visualization of large-scale seismic data in the cloud.
引文
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[13]李浩松,朱欣焰,李京伟,等.WebGIS空间数据分布式缓存技术研究[J].武汉大学学报(信息科学版),2005,30(12):1092-1095.(LI Haosong,ZHU Xinyan,LI Jingwei,et al.Research on WebGIS Spatial Data Distributed Caching Technology[J].Geomatics and Information Science of Wuhan University,2005,30(12):1092-1095.)
[14]吴峰光,奚宏生,徐陈锋.一种支持并发访问流的文件预取算法[J].软件学报,2010,21(8):1820-1833.(WU Fengguang,XI Hongsheng,XU Chenfeng.File Prefetching Algorithm for Concurrent Streams[J].Journal of Software,2010,21(8):1820-1833.)
[2]洪振刚.三维地质体建模可视计算及并行化的研究与应用[D].成都:成都理工大学,2010.(HONG Zhengang.Research and Application of 3D Geological Modeling Visual Computing and It’s Parallelism[D].Chengdu:Chengdu University of Technology,2010.)
[3]许国,李敦仁,王长海,等.GOCAD地质三维建模技术及其在水电工程中的应用[J].红水河,2007,26(增刊):113-116.(XU Guo,LI Dunren,WANG Changhai,et al.GOCAD Geological 3D Modeling Technology and Its Application in Hydropower Engineering[J].Hongshui River,2007,26(Suppl):113-116.)
[4]蒋锐.GOCAD与CATIA在三维地质建模生产中的应用分析[J].地下水,2013,35(2):97-98.(JIANG Rui.Application of GOCAD and CATIA in 3DGeological Modeling[J].Ground Water,2013,35(2):97-98.)
[5]白鑫.基于八叉树结构的大规模地震数据的快速加载机制[D].长春:吉林大学,2013.(BAI Xin.Fast Loading Mechanism of Large Data in Deep Exploration Based on Octree Structure[D].Changchun:Jilin University,2013.)
[6]赵波,詹毅,祝宽海.GeoEast地震数据处理解释一体化软件系统V2.0[J].石油科技论坛,2015,34(增刊):4-7.(ZHAO Bo,ZHAN Yi,ZHU Kuanhai.GeoEast Integrated Seismic Data Processing and Interpretation Systems(V2.0)[J].Oil Forum,2015,34(Suppl):4-7.)
[7]侯忠民.三维体数据兴趣区域提取技术及其应用的研究[D].长春:吉林大学,2014.(HOU Zhongmin.The Research on ROI Extraction of 3D Volume Data and Its Application[D].Changchun:Jilin University,2014.)
[8]WEI Xiaohui,BAI Xin,BAI Sen,et al.On-Demand Tile Preload for Large-Scale Seismic Data 3D-Visualization[J].Journal of Computational Information Systems,2015,11(4):1513-1520.
[9]刘爱贵,陈刚.一种基于用户的LNS文件预测模型[J].计算机工程与应用,2007,43(29):14-16.(LIU Aigui,CHEN Gang.User-Based LASTN Successors File Prediction Model[J].Computer Engineering and Applications,2007,43(29):14-16.)
[10]张胜利,陈莉君.一种基于文件预测的分布式缓存模型[J].微型机与应用,2014,33(12):69-72.(ZHANG Shengli,CHEN Lijun.A Distributed Caching Model Based on File Prediction[J].Microcomputers&Its Applications,2014,33(12):69-72.)
[11]Battle L,Chang R,Stonebraker M.Dynamic Prefetching of Data Tiles for Interactive Visualization[C]//Proceedings of the 2016International Conference on Management of Data.New York:ACM,2016:1363-1375.
[12]杨帆.OpenProbe地震体数据并行渲染机制及实现[D].长春:吉林大学,2015.(YANG Fan.The Mechanism and Realization of Parallel Rendering of Open Probe Seismic Volume Data[D].Changchun:Jilin University,2015.)
[13]李浩松,朱欣焰,李京伟,等.WebGIS空间数据分布式缓存技术研究[J].武汉大学学报(信息科学版),2005,30(12):1092-1095.(LI Haosong,ZHU Xinyan,LI Jingwei,et al.Research on WebGIS Spatial Data Distributed Caching Technology[J].Geomatics and Information Science of Wuhan University,2005,30(12):1092-1095.)
[14]吴峰光,奚宏生,徐陈锋.一种支持并发访问流的文件预取算法[J].软件学报,2010,21(8):1820-1833.(WU Fengguang,XI Hongsheng,XU Chenfeng.File Prefetching Algorithm for Concurrent Streams[J].Journal of Software,2010,21(8):1820-1833.)