Low-cost cloud computing solution for geo-information processing

Abstract

Cloud computing has emerged as a leading computing paradigm, with an increasing number of geographic information (geo-information) processing tasks now running on clouds. For this reason, geographic information system/remote sensing (GIS/RS) researchers rent more public clouds or establish more private clouds. However, a large proportion of these clouds are found to be underutilized, since users do not deal with big data every day. The low usage of cloud resources violates the original intention of cloud computing, which is to save resources by improving usage. In this work, a low-cost cloud computing solution was proposed for geo-information processing, especially for temporary processing tasks. The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory. The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study. Compared to commercial public clouds and dedicated private clouds, the proposed solution is more low-cost and resource-saving, and is more suitable for GIS/RS applications.

Keywords

cloud computing / geo-information processing / geo-processing

References

[1] ZhouZ, HuZ-g, SongT, YuJ-yang. A novel virtual machine deployment algorithm with energy efficiency in cloud computing [J]. Journal of Central South University, 2015, 22(3): 974-983

[2] MaH, HuZ-gang. User preferences-aware recommendation for trustworthy cloud services based on fuzzy clustering [J]. Journal of Central South University, 2015, 22(9): 3495-3505

[3] GaoP-c, LiuZ, XieM-h, TianKun. CRG-index: A more sensitive Ht-index for enabling dynamic views of geographic features. The Professional Geographer, 2016, 68(4): 533-545

[4] MellP, GranceT. The NIST definition of cloud computing (draft) [J]. NIST Special Publication, 2011, 800(145): 1-7

[5] GaoP-c, LiuZ, HanF, TangL, XieM-hui. Accelerating the computation of multi-scale visual curvature for simplifying a large set of polylines with Hadoop [J]. GIScience & Remote Sensing, 2015, 52(3): 315-331

[6] LiJ-y, MengL-k, WangF Z, ZhangW, CaiYang. A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation [J]. Computers & Geosciences, 2014, 70: 110-119

[7] AddairT G, DodgeD A, WalterW R, RuppertS D. Large-scale seismic signal analysis with Hadoop [J]. Computers & Geosciences, 2014, 66: 145-154

[8] WangP-y, WangJ-q, ChenY, NiG-yuan. Rapid processing of remote sensing images based on cloud computing [J]. Future Generation Computer Systems, 2013, 29(8): 1963-1968

[9] LeeD W, LiangS L. Geopot: A cloud-based geolocation data service for mobile applications [J]. International Journal of Geographical Information Science, 2011, 25(8): 1283-1301

[10] LiQ-q, ZhangT, YuYang. Using cloud computing to process intensive floating car data for urban traffic surveillance [J]. International Journal of Geographical Information Science, 2011, 25(8): 1303-1322

[11] FujiokaE, BergheE V, DonnellyB, CastilloJ, ClearyJ, HolmesC, McknightS, HalpinP. Advancing global marine biogeography research with open-source GIS software and cloud computing [J]. Transactions in GIS, 2012, 16(2): 143-160

[12] LinF-c, ChungL-k, WangC-j, KuW-y, ChouT-yin. Storage and processing of massive remote sensing images using a novel cloud computing platform [J]. GIScience & Remote Sensing, 2013, 50(3): 322-336

[13] TangW-w, FengW-peng. Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units [J]. Computers, Environment and Urban Systems, 2017, 61: 187-197

[14] PuriS, AgarwalD, HeX, PrasadS K. MapReduce algorithms for GIS polygonal overlay processing [C]. The 27th IEEE International Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2013Cambridge, MAIEEE1009-1016

[15] LeeK, KangS. Mobile cloud service of geo-based image processing functions: A test ipad implementation [J]. Remote Sensing Letters, 2013, 4(9): 910-919

[16] KimK, KangS, LeeK. Geo-based image blending in a mobile cloud environment [J]. Remote Sensing Letters, 2013, 4(11): 1117-1126

[17] GaoS, LiL-n, LiW-w, JanowiczK, ZhangYue. Constructing gazetteers from volunteered big geo-data based on Hadoop [J]. Computers, Environment and Urban Systems, 2017, 61: 172-186

[18] ExpósitoR R, TaboadaG L, RamosS, TouriñoJ, DoalloR. Evaluation of messaging middleware for highperformance cloud computing [J]. Personal and Ubiquitous Computing, 2013, 17(8): 1709-1719

[19] DoelitzscherF, SulistioA, ReichC, KuijsH, WolfD. Private cloud for collaboration and e-learning services: From IAAS to SAAS [J]. Computing, 2011, 91(1): 23-42

[20] GaoP-c, LiuZ, XieM-h, TianKun. The development of and prospects for private cloud GIS in China [J]. Asian Journal of Geoinformatics, 2014, 14(4): 30-38

[21] NoumanD M, ShamsiJ A. Volunteer computing: Requirements, challenges, and solutions [J]. Journal of Network and Computer Applications, 2014, 39: 369-380

[22] AndersonD P. BOINC: A system for public-resource computing and storage [C]. The 5th IEEE/ACM International Workshop on Grid Computing, 20044-10

[23] AndersonD P, CobbJ, KorpelaE, LebofskyM, WerthimerD. SETI@home: An experiment in public-resource computing [J]. Communications of the ACM, 2002, 45(11): 56-61

[24] ChangB-r, TsaiH-f, ChenC-ming. Evaluation of virtual machine performance and virtualized consolidation ratio in cloud computing system [J]. Journal of Information Hiding and Multimedia Signal Processing, 2013, 4(3): 192-200

[25] YuanJ, ZhengY, ZhangC-y, XieW-l, XieX, SunG-z, HuangYan. T-Drive: Driving directions based on taxi trajectories [C]. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010New YorkACM99-108

[26] LiuH-r, LateckiL, LiuW-yu. A unified curvature definition for regular, polygonal, and digital planar curves [J]. International Journal of Computer Vision, 2008, 80(1): 104-124

[27] SchindlerF, FörstnerW. Dijkstrafps: Graph partitioning in geometry and image processing [J]. Photogrammetrie Fernerkundung Geoinformation, 2013, 2013(4): 285-296

[28] WenzelS, FörstnerW. Finding poly-curves of straight line and ellipse segments in images [J]. Photogrammetrie Fernerkundung Geoinformation, 2013, 2013(4): 297-308

[29] LiaoX-k, YungC-q, TangT, YiH-z, WangF, WuQ, XueJ-ling. Openmc: Towards simplifying programming for tianhe supercomputers [J]. Journal of Computer Science and Technology, 2014, 29(3): 532-546