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

Spatial Cloud Computing  Developing and/or Deploying Spatial Applications on 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 2

Motivation  Large volumes of data  E.g., satellites collect terabytes of data daily  Compute intensive algorithms  Spatial analysis  Short Response Time  Unpredictable loads 3

Benefits of “Cloudification”  Simpler Architecture  On-demand Scalability  Reliability  Availability  Maintenance cost 4

Digital Elevation Model (DEM) Interpolation  Shepard's method, IDW (Inverse Distance Weighting) 5

A Previous Work by CISC  Grid-based DEM Interpolation 6

Architecture & Workflow (Grid) 7

Result 8

Architecture (Cloud) 9

Benefits of “Cloudification”  Simpler Architecture  On-demand Scalability  Reliability  Availability  Maintenance cost 10

DEMI on a 200-core rack Area size: ~ 80 000 sq. mi. 11

Azure Small Instance CPU: 1.6 Ghz Mem: 1.75 GB 6103 seconds Intel Core i3 CPU 540 @ 3.07 Ghz 3.07 Ghz Mem: 4.00 GB RAM 64 bit OS (Windows 7) Intel Dual Core 2.13 Ghz 2415 seconds 2.13 Ghz Mem: 4.00 GB Intel Dual Core T7100 @ 1.8 Ghz 1.8 32 bit OS (Ubuntu) Ghz 2958 seconds Mem: 2.50 GB Intel Xeon CPU 2.80 Ghz 64 bit OS (Windows Server 2008) Mem: 2.00 GB 4740 seconds 32 bit OS (CentOS) 10328 seconds 12

Azure Instance sizes 13

Amazon Instances Large Instance 7.5 GB memory m1.large 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 15 GB memory m1.xlarge 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 17.1 GB of memory Large Instance 6.5 EC2 Compute Units (2 virtual cores with 3.25 EC2 Compute Units each) m2.xlarge 420 GB of instance storage 64-bit platform I/O Performance: Moderate API name: m2.xlarge 14

Amazon Instances High-Memory Double 34.2 GB of memory Extra Large Instance 13 EC2 Compute Units (4 virtual cores with 3.25 EC2 Compute Units each) m2.2xlarge 850 GB of instance storage 64-bit platform I/O Performance: High API name: m2.2xlarge High-Memory 68.4 GB of memory Quadruple Extra Large 26 EC2 Compute Units (8 virtual cores with 3.25 EC2 Compute Units each) Instance 1690 GB of instance storage m2.4xlarge 64-bit platform I/O Performance: High API name: m2.4xlarge High-CPU Extra Large 7 GB of memory Instance 20 EC2 Compute Units (8 virtual cores with 2.5 EC2 Compute Units each) c1.xlarge 1690 GB of instance storage 64-bit platform I/O Performance: High API name: c1.xlarge 15

Lessons  Geospatial cloud computing is cost effective paradigm  Computing/storage elasticity will enable new compute- and data-intensive geospatial applications  Same development/deployment effort 16

Questions Thank You 17 17