Spatial Analysis on the Clouds: DEM Interpolation on the Microsoft - - PowerPoint PPT Presentation

Spatial Analysis on the Clouds: DEM Interpolation on the Microsoft Azure Platform

Abdelmounaam Rezgui

Center of Intelligent Spatial Computing for Water/Energy Science George Mason University

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Spatial Cloud Computing

 Developing and/or Deploying Spatial Applications

• n Cloud Platforms

 Geospatial Applications  90% of all business data has a geographic

component (e.g., address, sales district)

 Geospatial Applications  Data/Compute Intensive  Example:  Digital Elevation Model Interpolation

Motivation

 Large volumes of data

 E.g., satellites collect terabytes of data

daily

 Compute intensive algorithms

 Spatial analysis

 Short Response Time  Unpredictable loads

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Benefits of “Cloudification”

 Simpler Architecture  On-demand Scalability  Reliability  Availability  Maintenance cost

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Digital Elevation Model (DEM) Interpolation

 Shepard's method, IDW (Inverse

Distance Weighting)

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A Previous Work by CISC

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 Grid-based DEM Interpolation

Architecture & Workflow (Grid)

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Result

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Architecture (Cloud)

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Benefits of “Cloudification”

 Simpler Architecture  On-demand Scalability  Reliability  Availability  Maintenance cost

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DEMI on a 200-core rack

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Area size: ~ 80 000 sq. mi.

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Intel Core i3 CPU 540 @ 3.07 Ghz 3.07 Ghz Mem: 4.00 GB RAM 64 bit OS (Windows 7) 2415 seconds Intel Dual Core 2.13 Ghz 2.13 Ghz Mem: 4.00 GB 32 bit OS (Ubuntu) 2958 seconds Intel Dual Core T7100 @ 1.8 Ghz 1.8 Ghz Mem: 2.50 GB 64 bit OS (Windows Server 2008) 4740 seconds Intel Xeon CPU 2.80 Ghz Mem: 2.00 GB 32 bit OS (CentOS) 10328 seconds Azure Small Instance CPU: 1.6 Ghz Mem: 1.75 GB 6103 seconds

Azure Instance sizes

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Amazon Instances

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Large Instance m1.large 7.5 GB memory 4 EC2 Compute Units (2 virtual cores with 2 EC2 Compute Units each) 850 GB instance storage 64-bit platform I/O Performance: High API name: m1.large Extra Large Instance m1.xlarge 15 GB memory 8 EC2 Compute Units (4 virtual cores with 2 EC2 Compute Units each) 1,690 GB instance storage 64-bit platform I/O Performance: High API name: m1.xlarge High-Memory Extra Large Instance m2.xlarge 17.1 GB of memory 6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each) 420 GB of instance storage 64-bit platform I/O Performance: Moderate API name: m2.xlarge

Amazon Instances

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High-Memory Double Extra Large Instance m2.2xlarge 34.2 GB of memory 13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each) 850 GB of instance storage 64-bit platform I/O Performance: High API name: m2.2xlarge High-Memory Quadruple Extra Large Instance m2.4xlarge 68.4 GB of memory 26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each) 1690 GB of instance storage 64-bit platform I/O Performance: High API name: m2.4xlarge High-CPU Extra Large Instance c1.xlarge 7 GB of memory 20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each) 1690 GB of instance storage 64-bit platform I/O Performance: High API name: c1.xlarge

Lessons

 Geospatial cloud computing is cost

effective paradigm

 Computing/storage elasticity will enable

new compute- and data-intensive geospatial applications

 Same development/deployment effort

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Questions

Thank You