pipsCloud: High performance cloud computing for remote sensing big data management and processing
Massive, large-region coverage, multi-temporal, multi-spectral remote sensing (RS) datasets are employed widely due to the increasing requirements for accurate and up-to-date information about resources and the environment for regional and global monitoring. In general, RS data processing involves a...
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| Vydáno v: | Future generation computer systems Ročník 78; s. 353 - 368 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2018
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| Témata: | |
| ISSN: | 0167-739X, 1872-7115 |
| On-line přístup: | Získat plný text |
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| Abstract | Massive, large-region coverage, multi-temporal, multi-spectral remote sensing (RS) datasets are employed widely due to the increasing requirements for accurate and up-to-date information about resources and the environment for regional and global monitoring. In general, RS data processing involves a complex multi-stage processing sequence, which comprises several independent processing steps according to the type of RS application. RS data processing for regional environmental and disaster monitoring is recognized as being computationally intensive and data intensive.
We propose pipsCloud to address these issues in an efficient manner, which combines recent cloud computing and HPC techniques to obtain a large-scale RS data processing system that is suitable for on-demand real-time services. Due to the ubiquity, elasticity, and high-level transparency of the cloud computing model, massive RS data management and data processing for dynamic environmental monitoring can all be performed on the cloud via Web interfaces. A Hilbert-R+-based data indexing method is employed for the optimal querying and access of RS images, RS data products, and interim data. In the core platform beneath the cloud services, we provide a parallel file system for massive high-dimensional RS data, as well as interfaces for accessing irregular RS data to improve data locality and optimize the I/O performance. Moreover, we use an adaptive RS data analysis workflow management system for on-demand workflow construction and the collaborative processing of a distributed complex chain of RS data, e.g., for forest fire detection, mineral resources detection, and coastline monitoring. Our experimental analysis demonstrated the efficiency of the pipsCloud platform.
•A Cloud-enabled HPC platform for large-scale RS applications.•Hilbert-R+ Tree based data indexing for optimal RS big data indexing.•Collaborative large-scale RS workflow processing across data centers.•Cloud-enabled virtual HPC environment with VMs and bare-metal provisioning. |
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| AbstractList | Massive, large-region coverage, multi-temporal, multi-spectral remote sensing (RS) datasets are employed widely due to the increasing requirements for accurate and up-to-date information about resources and the environment for regional and global monitoring. In general, RS data processing involves a complex multi-stage processing sequence, which comprises several independent processing steps according to the type of RS application. RS data processing for regional environmental and disaster monitoring is recognized as being computationally intensive and data intensive.
We propose pipsCloud to address these issues in an efficient manner, which combines recent cloud computing and HPC techniques to obtain a large-scale RS data processing system that is suitable for on-demand real-time services. Due to the ubiquity, elasticity, and high-level transparency of the cloud computing model, massive RS data management and data processing for dynamic environmental monitoring can all be performed on the cloud via Web interfaces. A Hilbert-R+-based data indexing method is employed for the optimal querying and access of RS images, RS data products, and interim data. In the core platform beneath the cloud services, we provide a parallel file system for massive high-dimensional RS data, as well as interfaces for accessing irregular RS data to improve data locality and optimize the I/O performance. Moreover, we use an adaptive RS data analysis workflow management system for on-demand workflow construction and the collaborative processing of a distributed complex chain of RS data, e.g., for forest fire detection, mineral resources detection, and coastline monitoring. Our experimental analysis demonstrated the efficiency of the pipsCloud platform.
•A Cloud-enabled HPC platform for large-scale RS applications.•Hilbert-R+ Tree based data indexing for optimal RS big data indexing.•Collaborative large-scale RS workflow processing across data centers.•Cloud-enabled virtual HPC environment with VMs and bare-metal provisioning. |
| Author | Wang, Lizhe Zomaya, Albert Y. Yan, Jining Chang, Victor Ma, Yan |
| Author_xml | – sequence: 1 givenname: Lizhe orcidid: 0000-0003-2766-0845 surname: Wang fullname: Wang, Lizhe organization: School of Computer Science, China University of Geoscience, Wuhan 430074, PR China – sequence: 2 givenname: Yan surname: Ma fullname: Ma, Yan email: mayan@radi.ac.cn organization: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, PR China – sequence: 3 givenname: Jining surname: Yan fullname: Yan, Jining organization: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, PR China – sequence: 4 givenname: Victor surname: Chang fullname: Chang, Victor organization: Xi’an Jiaotong-Liverpool University, Suzhou, PR China – sequence: 5 givenname: Albert Y. surname: Zomaya fullname: Zomaya, Albert Y. organization: School of Information Technologies, University of Sydney, Australia |
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| Cites_doi | 10.1109/AINA.2013.95 10.1109/SBAC-PAD.2013.18 10.1080/014311600210227 10.1145/1327452.1327492 10.1109/JPROC.2012.2196250 10.1109/IGARSS.2013.6723840 10.1109/CloudCom.2012.6427589 10.1109/TSC.2009.4 10.1109/CISP.2011.6100438 10.1109/LGRS.2011.2136317 10.1016/j.future.2015.09.031 10.1109/IPDPSW.2012.64 10.1109/ICCSNT.2011.6182030 10.1109/PACT.2005.13 10.1109/CyberC.2010.75 10.1109/IGARSS.2012.6351392 10.1109/GCC.2010.103 10.1109/ESIAT.2010.5568475 10.1002/cpe.994 10.1109/AINA.2010.30 10.1109/CLUSTER.2012.51 10.1109/JIOT.2014.2325071 10.1109/JSTARS.2011.2162643 10.1109/IPDPS.2011.103 10.1109/PDP.2009.43 10.1007/s10766-013-0272-7 10.1007/s11554-009-0126-0 10.1109/COMPSACW.2014.18 10.1109/ICMULT.2010.5631433 10.1109/ICIEV.2014.6850733 10.1109/ICECC.2011.6067845 10.1109/GeoInformatics.2011.5980671 10.1109/ICPADS.2011.95 10.1109/CCGRID.2009.93 10.1109/JSTARS.2010.2095495 10.1109/36.868880 10.1109/JSTARS.2011.2106332 10.1109/MCC.2014.9 10.1109/TGRS.2006.888103 10.1109/RSETE.2011.5964523 |
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| References | Th. Udelhoven, Big data in environmental remote sensing: Challenges and chances, 12 2013. Yi-Man Ma, Che-Rung Lee, Yeh-Ching Chung, Infiniband virtualization on KVM, in: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science, CloudCom, Dec 2012, pp. 777–781. De Grandi, Mayaux, Rauste, Rosenqvist, Simard, Saatchi (br000070) 2000; 38 Zawodny (br000265) 2009; 79 Mehul Nalin Vora, Hadoop-Hbase for large-scale data, in: 2011 International Conference on Computer Science and Network Technology, ICCSNT, vol. 1, Dec 2011, pp. 601–605. J. Milthorpe, V. Ganesh, A.P. Rendell, D. Grove, X10 as a parallel language for scientific computation: Practice and experience, in: Parallel Distributed Processing Symposium, IPDPS, 2011 IEEE International, May 2011, pp. 1080–1088. Abdelwahab, Hamdaoui, Guizani, Rayes (br000250) 2014; 1 Cao, Shi (br000100) 2006; 10 (br000275) 2016; 57 Lee, Gasster, Plaza, Chang, Huang (br000025) 2011; 4 Xie, Su, Lin, Ma, Liang (br000120) 2013; 8 Barham, Dragovic, Fraser, Hand, Harris, Ho, Neugebauer, Pratt, Warfield (br000225) 2003 Dean, Ghemawat (br000195) 2008; 51 S. Pandey, A. Barker, K.K. Gupta, R. Buyya, Minimizing execution costs when using globally distributed cloud services, in: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, AINA, April 2010, pp. 222–229. Remon, Sanchez, Paz, Quintana-Orti, Plaza (br000125) 2011; 8 Taeyoung Kim, Myungjin Choi, Tae-Byeong Chae, Parallel processing with MPI for inter-band registration in remote sensing, in: 2011 IEEE 17th International Conference on Parallel and Distributed Systems, ICPADS, Dec 2011, pp. 1021–1025. Nan Lu, Chengqi Cheng, An Jin, Haijian Ma, An index and retrieval method of spatial data based on GeoSOT global discrete grid system, in: Geoscience and Remote Sensing Symposium, IGARSS, 2013 IEEE International, July 2013, pp. 4519–4522. Dobre, Xhafa (br000185) 2014; 42 Plaza (br000040) 2009; 4 Bingxin Liu, Ying Li, Peng Chen, Yongyi Guan, Junsong Han, Large oil spill surveillance with the use of MODIS and AVHRR images, in: 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE, June 2011, pp. 1317–1320. Ma, Wang, Zomaya, Chen, Ranjan (br000080) 2013; 99 Plaza, Chang (br000075) 2007 Plaza, Du, Chang, King (br000135) 2011; 4 Daniel Mandl, Matsu: An elastic cloud connected to a sensorweb for disaster response, 2011, pp. 1–22. Solberg (br000055) 2012; 100 OGC-OpenGIS Consortium et al. The OpenGIS abstract specification-topic 7: The earth imagery case, 1999. N. Skytland, Big data: What is NASA doing with big data today. Open. Gov open access article, 2012. Keahey, Parashar (br000115) 2014; 1 Meixia Deng, Liping Di, Genong Yu, A. Yagci, Chunming Peng, Bei Zhang, Dayong Shen, Building an on-demand web service system for global agricultural drought monitoring and forecasting, in: Geoscience and Remote Sensing Symposium, IGARSS, 2012 IEEE International, July 2012, pp. 958–961. Deelman, Singh, Su, Blythe, Gil, Kesselman, Mehta, Vahi, Bruce Berriman, Good, Laity, Jacob, Katz (br000295) 2005; 13 Dongjian Xue, Zhengwei He, Zhiheng Wang, Zhouqu county 8.8 extra-large-scale debris flow characters of remote sensing image analysis, in: 2011 International Conference on Electronics, Communications and Control, ICECC, Sept 2011, pp. 597–600. A. Rosenqvist, M. Shimada, B. Chapman, K. McDonald, G. De Grandi, H. Jonsson, C. Williams, Y. Rauste, M. Nilsson, D. Sango, M. Matsumoto, An overview of the JERS-1 SAR global boreal forest mapping (GBFM) project, in: Geoscience and Remote Sensing Symposium, 2004. IGARSS ’04. Proceedings. 2004 IEEE International, vol. 2, Sept 2004, pp. 1033–1036. Jianghui, Feng, Zhenhong, Renyi (br000270) 2015; 13 R. Rabenseifner, G. Hager, G. Jost, Hybrid MPI/OPENMP parallel programming on clusters of multi-core SMP nodes, in: 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Feb 2009, pp. 427–436. Nan Dun, K. Taura, An empirical performance study of chapel programming language, in: Parallel and Distributed Processing Symposium Workshops Ph.D. Forum, IPDPSW, 2012 IEEE 26th International, May 2012, pp. 497–506. Yan Ma, Lizhe Wang, Dingsheng Liu, Peng Liu, Jun Wang, Jie Tao, Generic parallel programming for massive remote sensing data processing, in: 2012 IEEE International Conference on Cluster Computing, CLUSTER, Sept 2012, pp. 420–428. Xue Xiaorong, Guo Lei, Wang Hongfu, Xiang Fang, A parallel fusion method of remote sensing image based on IHS transformation, in: 2011 4th International Congress on Image and Signal Processing, CISP, vol. 3, Oct 2011, pp. 1600–1603. Schumann, Hostache, Puech, Hoffmann, Matgen, Pappenberger, Pfister (br000050) 2007; 45 Lin, Lu, Fei, Chebotko, Pai, Lai, Fotouhi, Hua (br000285) 2009; 2 Bo Li, Hui Zhao, Zhenhua Lv, Parallel isodata clustering of remote sensing images based on MapReduce, in: 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, Oct 2010, pp. 380–383. R. Nasim, A.J. Kassler, Deploying OpenStack: Virtual infrastructure or dedicated hardware, in: Computer Software and Applications Conference Workshops, COMPSACW, 2014 IEEE 38th International, July 2014, pp. 84–89. A.B.M. Moniruzzaman, K.W. Nafi, S.A. Hossain, An experimental study of load balancing of OpenNebula open-source cloud computing platform, in: 2014 International Conference on Informatics, Electronics Vision, ICIEV, May 2014, pp. 1–6. Ali, Kiriansky, Simons, Zaroo (br000235) 2012; vol. 7155 Wei-Yu Chen, C. Iancu, K. Yelick, Communication optimizations for fine-grained UPC applications, in: 14th International Conference on Parallel Architectures and Compilation Techniques, 2005. PACT 2005. Sept 2005, pp. 267–278. Yanying Wang, Yan Ma, Peng Liu, Dingsheng Liu, Jibo Xie, An optimized image mosaic algorithm with parallel I/O and dynamic grouped parallel strategy based on minimal spanning tree, in: 2010 9th International Conference on Grid and Cooperative Computing, GCC, Nov 2010, pp. 501–506. Fauvel, Benediktsson, Boardman, Brazile, Bruzzone, Camps-Valls, Chanussot, Gamba, Gualtieri, Marconcini (br000035) 2007 Blagojevi, Hargrove, Iancu, Yelick (br000165) 2010 Feng-Cheng Lin, Lan-Kun Chung, Wen-Yuan Ku, Lin-Ru Chu, Tien-Yin Chou, Service component architecture for geographic information system in cloud computing infrastructure, in: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications, AINA, March 2013, pp. 368–373. Ludscher, Bertram, Berkley, Higgins, Jaeger, Jones, Lee, Tao, Zhao (br000290) 2006; 18 Yinghui Zhao, Remote sensing based soil moisture estimation on high performance PC server, in: 2010 International Conference on Environmental Science and Information Application Technology, ESIAT, vol. 1, July 2010, pp. 64–69. Xinyuan Qu, Jiacun Li, Wenji Zhao, Xiaoli Zhao, Cheng Yan, Research on critical techniques of disaster-oriented remote sensing quick mapping, in: 2010 International Conference on Multimedia Technology, ICMT, Oct 2010, pp. 1–4. D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, D. Zagorodnov, The Eucalyptus open-source cloud-computing system, in: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009. CCGRID’09. May 2009, pp. 124–131. Rosenqvist, Shimada, Chapman, Freeman, De Grandi, Saatchi, Rauste (br000030) 2000; 21 A. Kivity, Y. Kamay, D. Laor, U. Lublin, A. Liguori, KVM: The Linux virtual machine monitor, in: OLS’09: Ottawa Linux Symposium 2009, Jul 2007, pp. 225–230. Gamba, Du, Juergens, Maktav (br000020) 2011; 4 S. Varrette, M. Guzek, V. Plugaru, X. Besseron, P. Bouvry, HPC performance and energy-efficiency of XEN, KVM and VMWare hypervisors, in: 2013 25th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD, Oct 2013, pp. 89–96. Ma, Zhao, Liu (br000095) 2009; vol. 5545 Almeer (br000210) 2012; 3 Yuehu Liu, Bin Chen, Hao Yu, Yong Zhao, Zhou Huang, Yu Fang, Applying GPU and POSIX thread technologies in massive remote sensing image data processing, in: 2011 19th International Conference on Geoinformatics, June 2011, pp. 1–6. Dobre (10.1016/j.future.2016.06.009_br000185) 2014; 42 Keahey (10.1016/j.future.2016.06.009_br000115) 2014; 1 Barham (10.1016/j.future.2016.06.009_br000225) 2003 10.1016/j.future.2016.06.009_br000150 10.1016/j.future.2016.06.009_br000230 10.1016/j.future.2016.06.009_br000155 10.1016/j.future.2016.06.009_br000110 Almeer (10.1016/j.future.2016.06.009_br000210) 2012; 3 Deelman (10.1016/j.future.2016.06.009_br000295) 2005; 13 Jianghui (10.1016/j.future.2016.06.009_br000270) 2015; 13 10.1016/j.future.2016.06.009_br000190 10.1016/j.future.2016.06.009_br000145 10.1016/j.future.2016.06.009_br000105 10.1016/j.future.2016.06.009_br000060 10.1016/j.future.2016.06.009_br000140 Ludscher (10.1016/j.future.2016.06.009_br000290) 2006; 18 10.1016/j.future.2016.06.009_br000260 10.1016/j.future.2016.06.009_br000065 10.1016/j.future.2016.06.009_br000220 Fauvel (10.1016/j.future.2016.06.009_br000035) 2007 Remon (10.1016/j.future.2016.06.009_br000125) 2011; 8 10.1016/j.future.2016.06.009_br000180 Abdelwahab (10.1016/j.future.2016.06.009_br000250) 2014; 1 Dean (10.1016/j.future.2016.06.009_br000195) 2008; 51 10.1016/j.future.2016.06.009_br000255 10.1016/j.future.2016.06.009_br000015 10.1016/j.future.2016.06.009_br000215 Plaza (10.1016/j.future.2016.06.009_br000075) 2007 10.1016/j.future.2016.06.009_br000170 10.1016/j.future.2016.06.009_br000010 Blagojevi (10.1016/j.future.2016.06.009_br000165) 2010 10.1016/j.future.2016.06.009_br000175 Lin (10.1016/j.future.2016.06.009_br000285) 2009; 2 10.1016/j.future.2016.06.009_br000130 (10.1016/j.future.2016.06.009_br000275) 2016; 57 Schumann (10.1016/j.future.2016.06.009_br000050) 2007; 45 10.1016/j.future.2016.06.009_br000090 Lee (10.1016/j.future.2016.06.009_br000025) 2011; 4 Cao (10.1016/j.future.2016.06.009_br000100) 2006; 10 Xie (10.1016/j.future.2016.06.009_br000120) 2013; 8 Ali (10.1016/j.future.2016.06.009_br000235) 2012; vol. 7155 10.1016/j.future.2016.06.009_br000245 10.1016/j.future.2016.06.009_br000200 10.1016/j.future.2016.06.009_br000005 10.1016/j.future.2016.06.009_br000205 10.1016/j.future.2016.06.009_br000160 10.1016/j.future.2016.06.009_br000280 10.1016/j.future.2016.06.009_br000085 Solberg (10.1016/j.future.2016.06.009_br000055) 2012; 100 10.1016/j.future.2016.06.009_br000240 10.1016/j.future.2016.06.009_br000045 Ma (10.1016/j.future.2016.06.009_br000095) 2009; vol. 5545 Gamba (10.1016/j.future.2016.06.009_br000020) 2011; 4 Zawodny (10.1016/j.future.2016.06.009_br000265) 2009; 79 Rosenqvist (10.1016/j.future.2016.06.009_br000030) 2000; 21 Plaza (10.1016/j.future.2016.06.009_br000135) 2011; 4 Plaza (10.1016/j.future.2016.06.009_br000040) 2009; 4 Ma (10.1016/j.future.2016.06.009_br000080) 2013; 99 De Grandi (10.1016/j.future.2016.06.009_br000070) 2000; 38 |
| References_xml | – volume: 51 start-page: 107 year: 2008 end-page: 113 ident: br000195 article-title: MapReduce: Simplified data processing on large clusters publication-title: Commun. ACM – reference: Mehul Nalin Vora, Hadoop-Hbase for large-scale data, in: 2011 International Conference on Computer Science and Network Technology, ICCSNT, vol. 1, Dec 2011, pp. 601–605. – volume: 4 start-page: 5 year: 2011 end-page: 7 ident: br000020 article-title: Foreword to the special issue on “human settlements: A global remote sensing challenge” publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – reference: A.B.M. Moniruzzaman, K.W. Nafi, S.A. Hossain, An experimental study of load balancing of OpenNebula open-source cloud computing platform, in: 2014 International Conference on Informatics, Electronics Vision, ICIEV, May 2014, pp. 1–6. – volume: 13 start-page: 365 year: 2015 end-page: 370 ident: br000270 article-title: Research of the landuse vector data storage and spatial index based on the main memory database publication-title: J. Zhejiang Univ. (Sci. Ed.) – reference: Th. Udelhoven, Big data in environmental remote sensing: Challenges and chances, 12 2013. – volume: 2 start-page: 79 year: 2009 end-page: 92 ident: br000285 article-title: A reference architecture for scientific workflow management systems and the view SOA solution publication-title: IEEE Trans. Serv. Comput. – volume: 1 start-page: 21 year: 2014 end-page: 27 ident: br000115 article-title: Enabling on-demand science via cloud computing publication-title: IEEE Cloud Comput. – volume: 3 start-page: 637 year: 2012 end-page: 644 ident: br000210 article-title: Cloud Hadoop MapReduce for remote sensing image analysis publication-title: J. Emerg. Trends Comput. Inf. Sci. – reference: Nan Lu, Chengqi Cheng, An Jin, Haijian Ma, An index and retrieval method of spatial data based on GeoSOT global discrete grid system, in: Geoscience and Remote Sensing Symposium, IGARSS, 2013 IEEE International, July 2013, pp. 4519–4522. – reference: J. Milthorpe, V. Ganesh, A.P. Rendell, D. Grove, X10 as a parallel language for scientific computation: Practice and experience, in: Parallel Distributed Processing Symposium, IPDPS, 2011 IEEE International, May 2011, pp. 1080–1088. – reference: Dongjian Xue, Zhengwei He, Zhiheng Wang, Zhouqu county 8.8 extra-large-scale debris flow characters of remote sensing image analysis, in: 2011 International Conference on Electronics, Communications and Control, ICECC, Sept 2011, pp. 597–600. – volume: vol. 7155 start-page: 213 year: 2012 end-page: 222 ident: br000235 article-title: Performance evaluation of HPC benchmarks on VMWares ESXI server publication-title: Euro-Par 2011: Parallel Processing Workshops – reference: Yi-Man Ma, Che-Rung Lee, Yeh-Ching Chung, Infiniband virtualization on KVM, in: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science, CloudCom, Dec 2012, pp. 777–781. – volume: 10 start-page: 55 year: 2006 end-page: 58 ident: br000100 article-title: Primary study of massive imaging auto-processing system pixel factory publication-title: Bull. Surv. Mapp. – volume: 21 start-page: 1375 year: 2000 end-page: 1387 ident: br000030 article-title: The global rain forest mapping project - a review publication-title: Int. J. Remote Sens. – volume: 4 start-page: 528 year: 2011 end-page: 544 ident: br000135 article-title: High performance computing for hyperspectral remote sensing publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 1 start-page: 276 year: 2014 end-page: 288 ident: br000250 article-title: Enabling smart cloud services through remote sensing: An internet of everything enabler publication-title: IEEE Internet Things J. – reference: A. Kivity, Y. Kamay, D. Laor, U. Lublin, A. Liguori, KVM: The Linux virtual machine monitor, in: OLS’09: Ottawa Linux Symposium 2009, Jul 2007, pp. 225–230. – volume: 4 start-page: 191 year: 2009 end-page: 193 ident: br000040 article-title: Special issue on architectures and techniques for real-time processing of remotely sensed images publication-title: J. Real-Time Image Process. – volume: 42 start-page: 710 year: 2014 end-page: 738 ident: br000185 article-title: Parallel programming paradigms and frameworks in big data era publication-title: Int. J. Parallel Program. – reference: Bingxin Liu, Ying Li, Peng Chen, Yongyi Guan, Junsong Han, Large oil spill surveillance with the use of MODIS and AVHRR images, in: 2011 International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE, June 2011, pp. 1317–1320. – reference: Yuehu Liu, Bin Chen, Hao Yu, Yong Zhao, Zhou Huang, Yu Fang, Applying GPU and POSIX thread technologies in massive remote sensing image data processing, in: 2011 19th International Conference on Geoinformatics, June 2011, pp. 1–6. – volume: 99 start-page: 1 year: 2013 ident: br000080 article-title: Task-tree based large-scale mosaicking for remote sensed imageries with dynamic DAG scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. – volume: 57 start-page: 24 year: 2016 end-page: 41 ident: br000275 article-title: Cloud computing adoption framework: A security framework for business clouds publication-title: Future Gener. Comput. Syst. – start-page: 164 year: 2003 end-page: 177 ident: br000225 article-title: Xen and the art of virtualization publication-title: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles – year: 2007 ident: br000075 article-title: High Performance Computing in Remote Sensing – volume: vol. 5545 start-page: 357 year: 2009 end-page: 366 ident: br000095 article-title: An asynchronous parallelized and scalable image resampling algorithm with parallel I/O publication-title: Computational Science-ICCS 2009 – reference: Meixia Deng, Liping Di, Genong Yu, A. Yagci, Chunming Peng, Bei Zhang, Dayong Shen, Building an on-demand web service system for global agricultural drought monitoring and forecasting, in: Geoscience and Remote Sensing Symposium, IGARSS, 2012 IEEE International, July 2012, pp. 958–961. – reference: Yinghui Zhao, Remote sensing based soil moisture estimation on high performance PC server, in: 2010 International Conference on Environmental Science and Information Application Technology, ESIAT, vol. 1, July 2010, pp. 64–69. – volume: 8 start-page: 1691 year: 2013 end-page: 1695 ident: br000120 article-title: Bare metal provisioning to OpenStack using xCAT publication-title: J. Comput. Phys. – start-page: 1 year: 2007 end-page: 45 ident: br000035 article-title: Recent advances in techniques for hyperspectral image processing publication-title: Remote Sens. Environ. – reference: Feng-Cheng Lin, Lan-Kun Chung, Wen-Yuan Ku, Lin-Ru Chu, Tien-Yin Chou, Service component architecture for geographic information system in cloud computing infrastructure, in: 2013 IEEE 27th International Conference on Advanced Information Networking and Applications, AINA, March 2013, pp. 368–373. – volume: 100 start-page: 2931 year: 2012 end-page: 2945 ident: br000055 article-title: Remote sensing of ocean oil-spill pollution publication-title: Proc. IEEE – reference: S. Pandey, A. Barker, K.K. Gupta, R. Buyya, Minimizing execution costs when using globally distributed cloud services, in: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, AINA, April 2010, pp. 222–229. – reference: OGC-OpenGIS Consortium et al. The OpenGIS abstract specification-topic 7: The earth imagery case, 1999. – reference: S. Varrette, M. Guzek, V. Plugaru, X. Besseron, P. Bouvry, HPC performance and energy-efficiency of XEN, KVM and VMWare hypervisors, in: 2013 25th International Symposium on Computer Architecture and High Performance Computing, SBAC-PAD, Oct 2013, pp. 89–96. – volume: 79 year: 2009 ident: br000265 article-title: Redis: Lightweight key/value store that goes the extra mile publication-title: Linux Mag. – reference: Wei-Yu Chen, C. Iancu, K. Yelick, Communication optimizations for fine-grained UPC applications, in: 14th International Conference on Parallel Architectures and Compilation Techniques, 2005. PACT 2005. Sept 2005, pp. 267–278. – reference: R. Nasim, A.J. Kassler, Deploying OpenStack: Virtual infrastructure or dedicated hardware, in: Computer Software and Applications Conference Workshops, COMPSACW, 2014 IEEE 38th International, July 2014, pp. 84–89. – volume: 4 start-page: 508 year: 2011 end-page: 527 ident: br000025 article-title: Recent developments in high performance computing for remote sensing: A review publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. – volume: 8 start-page: 924 year: 2011 end-page: 928 ident: br000125 article-title: Real-time endmember extraction on multicore processors publication-title: IEEE Geosci. Remote Sens. Lett. – reference: Nan Dun, K. Taura, An empirical performance study of chapel programming language, in: Parallel and Distributed Processing Symposium Workshops Ph.D. Forum, IPDPSW, 2012 IEEE 26th International, May 2012, pp. 497–506. – reference: Daniel Mandl, Matsu: An elastic cloud connected to a sensorweb for disaster response, 2011, pp. 1–22. – reference: Yan Ma, Lizhe Wang, Dingsheng Liu, Peng Liu, Jun Wang, Jie Tao, Generic parallel programming for massive remote sensing data processing, in: 2012 IEEE International Conference on Cluster Computing, CLUSTER, Sept 2012, pp. 420–428. – volume: 38 start-page: 2218 year: 2000 end-page: 2233 ident: br000070 article-title: The global rain forest mapping project JERS-1 radar mosaic of tropical Africa: development and product characterization aspects publication-title: IEEE Trans. Geosci. Remote Sens. – volume: 18 start-page: 1039 year: 2006 end-page: 1065 ident: br000290 article-title: Scientific workflow management and the Kepler system publication-title: Concurr. Comput.: Pract. Exper. – start-page: 3:1 year: 2010 end-page: 3:10 ident: br000165 article-title: Hybrid PGAS runtime support for multicore nodes publication-title: Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model – reference: N. Skytland, Big data: What is NASA doing with big data today. Open. Gov open access article, 2012. – reference: A. Rosenqvist, M. Shimada, B. Chapman, K. McDonald, G. De Grandi, H. Jonsson, C. Williams, Y. Rauste, M. Nilsson, D. Sango, M. Matsumoto, An overview of the JERS-1 SAR global boreal forest mapping (GBFM) project, in: Geoscience and Remote Sensing Symposium, 2004. IGARSS ’04. Proceedings. 2004 IEEE International, vol. 2, Sept 2004, pp. 1033–1036. – reference: Taeyoung Kim, Myungjin Choi, Tae-Byeong Chae, Parallel processing with MPI for inter-band registration in remote sensing, in: 2011 IEEE 17th International Conference on Parallel and Distributed Systems, ICPADS, Dec 2011, pp. 1021–1025. – reference: D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L. Youseff, D. Zagorodnov, The Eucalyptus open-source cloud-computing system, in: 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009. CCGRID’09. May 2009, pp. 124–131. – reference: R. Rabenseifner, G. Hager, G. Jost, Hybrid MPI/OPENMP parallel programming on clusters of multi-core SMP nodes, in: 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, Feb 2009, pp. 427–436. – reference: Xinyuan Qu, Jiacun Li, Wenji Zhao, Xiaoli Zhao, Cheng Yan, Research on critical techniques of disaster-oriented remote sensing quick mapping, in: 2010 International Conference on Multimedia Technology, ICMT, Oct 2010, pp. 1–4. – volume: 45 start-page: 1715 year: 2007 end-page: 1725 ident: br000050 article-title: High-resolution 3-D flood information from radar imagery for flood hazard management publication-title: IEEE Trans. Geosci. Remote Sens. – reference: Bo Li, Hui Zhao, Zhenhua Lv, Parallel isodata clustering of remote sensing images based on MapReduce, in: 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC, Oct 2010, pp. 380–383. – reference: Yanying Wang, Yan Ma, Peng Liu, Dingsheng Liu, Jibo Xie, An optimized image mosaic algorithm with parallel I/O and dynamic grouped parallel strategy based on minimal spanning tree, in: 2010 9th International Conference on Grid and Cooperative Computing, GCC, Nov 2010, pp. 501–506. – reference: Xue Xiaorong, Guo Lei, Wang Hongfu, Xiang Fang, A parallel fusion method of remote sensing image based on IHS transformation, in: 2011 4th International Congress on Image and Signal Processing, CISP, vol. 3, Oct 2011, pp. 1600–1603. – volume: 13 start-page: 219 year: 2005 end-page: 237 ident: br000295 article-title: Pegasus: a framework for mapping complex scientific workflows onto distributed systems publication-title: Sci. Program. J. – ident: 10.1016/j.future.2016.06.009_br000110 – ident: 10.1016/j.future.2016.06.009_br000200 doi: 10.1109/AINA.2013.95 – volume: 3 start-page: 637 issue: 4 year: 2012 ident: 10.1016/j.future.2016.06.009_br000210 article-title: Cloud Hadoop MapReduce for remote sensing image analysis publication-title: J. Emerg. Trends Comput. Inf. Sci. – ident: 10.1016/j.future.2016.06.009_br000245 doi: 10.1109/SBAC-PAD.2013.18 – ident: 10.1016/j.future.2016.06.009_br000005 – volume: 21 start-page: 1375 issue: 6–7 year: 2000 ident: 10.1016/j.future.2016.06.009_br000030 article-title: The global rain forest mapping project - a review publication-title: Int. J. Remote Sens. doi: 10.1080/014311600210227 – volume: 51 start-page: 107 issue: 1 year: 2008 ident: 10.1016/j.future.2016.06.009_br000195 article-title: MapReduce: Simplified data processing on large clusters publication-title: Commun. ACM doi: 10.1145/1327452.1327492 – volume: 100 start-page: 2931 issue: 10 year: 2012 ident: 10.1016/j.future.2016.06.009_br000055 article-title: Remote sensing of ocean oil-spill pollution publication-title: Proc. IEEE doi: 10.1109/JPROC.2012.2196250 – ident: 10.1016/j.future.2016.06.009_br000260 doi: 10.1109/IGARSS.2013.6723840 – volume: 79 year: 2009 ident: 10.1016/j.future.2016.06.009_br000265 article-title: Redis: Lightweight key/value store that goes the extra mile publication-title: Linux Mag. – ident: 10.1016/j.future.2016.06.009_br000010 – ident: 10.1016/j.future.2016.06.009_br000240 doi: 10.1109/CloudCom.2012.6427589 – volume: 2 start-page: 79 issue: 1 year: 2009 ident: 10.1016/j.future.2016.06.009_br000285 article-title: A reference architecture for scientific workflow management systems and the view SOA solution publication-title: IEEE Trans. Serv. Comput. doi: 10.1109/TSC.2009.4 – ident: 10.1016/j.future.2016.06.009_br000150 doi: 10.1109/CISP.2011.6100438 – volume: 8 start-page: 924 issue: 5 year: 2011 ident: 10.1016/j.future.2016.06.009_br000125 article-title: Real-time endmember extraction on multicore processors publication-title: IEEE Geosci. Remote Sens. Lett. doi: 10.1109/LGRS.2011.2136317 – volume: 57 start-page: 24 year: 2016 ident: 10.1016/j.future.2016.06.009_br000275 article-title: Cloud computing adoption framework: A security framework for business clouds publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2015.09.031 – ident: 10.1016/j.future.2016.06.009_br000175 doi: 10.1109/IPDPSW.2012.64 – ident: 10.1016/j.future.2016.06.009_br000255 doi: 10.1109/ICCSNT.2011.6182030 – ident: 10.1016/j.future.2016.06.009_br000170 doi: 10.1109/PACT.2005.13 – ident: 10.1016/j.future.2016.06.009_br000205 doi: 10.1109/CyberC.2010.75 – volume: vol. 5545 start-page: 357 year: 2009 ident: 10.1016/j.future.2016.06.009_br000095 article-title: An asynchronous parallelized and scalable image resampling algorithm with parallel I/O – volume: 8 start-page: 1691 issue: 7 year: 2013 ident: 10.1016/j.future.2016.06.009_br000120 article-title: Bare metal provisioning to OpenStack using xCAT publication-title: J. Comput. Phys. – ident: 10.1016/j.future.2016.06.009_br000280 doi: 10.1109/IGARSS.2012.6351392 – ident: 10.1016/j.future.2016.06.009_br000145 doi: 10.1109/GCC.2010.103 – ident: 10.1016/j.future.2016.06.009_br000065 – ident: 10.1016/j.future.2016.06.009_br000140 doi: 10.1109/ESIAT.2010.5568475 – start-page: 164 year: 2003 ident: 10.1016/j.future.2016.06.009_br000225 article-title: Xen and the art of virtualization – start-page: 3:1 year: 2010 ident: 10.1016/j.future.2016.06.009_br000165 article-title: Hybrid PGAS runtime support for multicore nodes – volume: 13 start-page: 365 issue: 3 year: 2015 ident: 10.1016/j.future.2016.06.009_br000270 article-title: Research of the landuse vector data storage and spatial index based on the main memory database publication-title: J. Zhejiang Univ. (Sci. Ed.) – volume: 18 start-page: 1039 issue: 10 year: 2006 ident: 10.1016/j.future.2016.06.009_br000290 article-title: Scientific workflow management and the Kepler system publication-title: Concurr. Comput.: Pract. Exper. doi: 10.1002/cpe.994 – ident: 10.1016/j.future.2016.06.009_br000105 doi: 10.1109/AINA.2010.30 – ident: 10.1016/j.future.2016.06.009_br000160 doi: 10.1109/CLUSTER.2012.51 – volume: 1 start-page: 276 issue: 3 year: 2014 ident: 10.1016/j.future.2016.06.009_br000250 article-title: Enabling smart cloud services through remote sensing: An internet of everything enabler publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2014.2325071 – volume: 4 start-page: 508 issue: 3 year: 2011 ident: 10.1016/j.future.2016.06.009_br000025 article-title: Recent developments in high performance computing for remote sensing: A review publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2011.2162643 – ident: 10.1016/j.future.2016.06.009_br000180 doi: 10.1109/IPDPS.2011.103 – ident: 10.1016/j.future.2016.06.009_br000130 doi: 10.1109/PDP.2009.43 – volume: 42 start-page: 710 issue: 5 year: 2014 ident: 10.1016/j.future.2016.06.009_br000185 article-title: Parallel programming paradigms and frameworks in big data era publication-title: Int. J. Parallel Program. doi: 10.1007/s10766-013-0272-7 – volume: 4 start-page: 191 issue: 3 year: 2009 ident: 10.1016/j.future.2016.06.009_br000040 article-title: Special issue on architectures and techniques for real-time processing of remotely sensed images publication-title: J. Real-Time Image Process. doi: 10.1007/s11554-009-0126-0 – ident: 10.1016/j.future.2016.06.009_br000230 – year: 2007 ident: 10.1016/j.future.2016.06.009_br000075 – ident: 10.1016/j.future.2016.06.009_br000215 doi: 10.1109/COMPSACW.2014.18 – ident: 10.1016/j.future.2016.06.009_br000085 doi: 10.1109/ICMULT.2010.5631433 – volume: 99 start-page: 1 year: 2013 ident: 10.1016/j.future.2016.06.009_br000080 article-title: Task-tree based large-scale mosaicking for remote sensed imageries with dynamic DAG scheduling publication-title: IEEE Trans. Parallel Distrib. Syst. – ident: 10.1016/j.future.2016.06.009_br000220 doi: 10.1109/ICIEV.2014.6850733 – ident: 10.1016/j.future.2016.06.009_br000045 doi: 10.1109/ICECC.2011.6067845 – ident: 10.1016/j.future.2016.06.009_br000090 doi: 10.1109/GeoInformatics.2011.5980671 – ident: 10.1016/j.future.2016.06.009_br000155 doi: 10.1109/ICPADS.2011.95 – volume: 13 start-page: 219 year: 2005 ident: 10.1016/j.future.2016.06.009_br000295 article-title: Pegasus: a framework for mapping complex scientific workflows onto distributed systems publication-title: Sci. Program. J. – start-page: 1 year: 2007 ident: 10.1016/j.future.2016.06.009_br000035 article-title: Recent advances in techniques for hyperspectral image processing publication-title: Remote Sens. Environ. – volume: 10 start-page: 55 year: 2006 ident: 10.1016/j.future.2016.06.009_br000100 article-title: Primary study of massive imaging auto-processing system pixel factory publication-title: Bull. Surv. Mapp. – ident: 10.1016/j.future.2016.06.009_br000190 doi: 10.1109/CCGRID.2009.93 – volume: 4 start-page: 528 issue: 3 year: 2011 ident: 10.1016/j.future.2016.06.009_br000135 article-title: High performance computing for hyperspectral remote sensing publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2010.2095495 – volume: 38 start-page: 2218 issue: 5 year: 2000 ident: 10.1016/j.future.2016.06.009_br000070 article-title: The global rain forest mapping project JERS-1 radar mosaic of tropical Africa: development and product characterization aspects publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/36.868880 – volume: vol. 7155 start-page: 213 year: 2012 ident: 10.1016/j.future.2016.06.009_br000235 article-title: Performance evaluation of HPC benchmarks on VMWares ESXI server – volume: 4 start-page: 5 issue: 1 year: 2011 ident: 10.1016/j.future.2016.06.009_br000020 article-title: Foreword to the special issue on “human settlements: A global remote sensing challenge” publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2011.2106332 – ident: 10.1016/j.future.2016.06.009_br000015 – volume: 1 start-page: 21 issue: 1 year: 2014 ident: 10.1016/j.future.2016.06.009_br000115 article-title: Enabling on-demand science via cloud computing publication-title: IEEE Cloud Comput. doi: 10.1109/MCC.2014.9 – volume: 45 start-page: 1715 issue: 6 year: 2007 ident: 10.1016/j.future.2016.06.009_br000050 article-title: High-resolution 3-D flood information from radar imagery for flood hazard management publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2006.888103 – ident: 10.1016/j.future.2016.06.009_br000060 doi: 10.1109/RSETE.2011.5964523 |
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| Score | 2.5744677 |
| Snippet | Massive, large-region coverage, multi-temporal, multi-spectral remote sensing (RS) datasets are employed widely due to the increasing requirements for accurate... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 353 |
| SubjectTerms | Big data Cloud computing Data-intensive computing High performance computing Remote sensing |
| Title | pipsCloud: High performance cloud computing for remote sensing big data management and processing |
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