Geodetic imaging data from Interferometric Synthetic Aperture Radar (InSAR) are used to measure crustal deformation related to tectonic motions and displacements on earthquake faults. NASA's UAVSAR project and related efforts are creating large catalogs of data products. The user base of these data products is also growing, introducing the need for downstream tools to support computationally expensive individual research as well as access to voluminous and heterogeneous data products. Bundling data inside a virtual machine becomes impractical for load balancing and on-demand auto scaling. A possible solution is to separate the application services from the data service. The Amazon public cloud would be utilized for computation and analysis and private data would be served from a private cloud through Open Geospatial Consortium (OGC) cascading services. We are using Amazon's Elastic Compute Cloud (EC2), which is a basic virtual machine service, for cloud deployment. Elastic Load Balancing (ELB) is used to seamlessly distribute incoming traffic among multiple instances. Auto scaling with CloudWatch results in on-demand scalability. ELB detects unhealthy instances and automatically reroutes traffic, while auto scaling replaces the unhealthy instances to maintain high availability. Content is distributed globally through CloudFront where distribution is via a global network of edge locations, which optimizes performance by routing content to the nearest edge location. Amazon web services (AWS) cloud infrastructure provides easy deployment of highly available and on-demand scalable applications. However, applications requiring instant access of relatively large datasets face certain limitations from Elastic Block Store (EBS). A single EBS volume is limited to 1 TB, and as well as the high costs, and EBS volumes cannot be shared among multiple instances. The current processed UAVSAR Repeat Pass Interferometry data products are about 2.5 TB and the volume continues to expand.


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    Titel :

    Cloud computing for geodetic imaging data processing, analysis, and modeling


    Beteiligte:
    Donnellan, Andrea (Autor:in) / Parker, Jay W. (Autor:in) / Wang, Jun (Autor:in) / Ma, Yu (Autor:in) / Pierce, Marlon (Autor:in)


    Erscheinungsdatum :

    01.03.2014


    Format / Umfang :

    1391155 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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