![Example Runs
Serial – Pumping Test
WTAQ model (USGS)
Parameter Initial Estimated
Horizontal Hydraulic Conductivity [ft/day] 37 10 37
Vertical Hydraulic Conductivity [ft/day] 0.81 0.1 0.81
45.7 100 45.7
Specific Storage [ft-1] 0 0 0
Original
Model
Vertical Hydraulic Conductivity anisotropy
[Kh/Kv]](https://image.slidesharecdn.com/pestcloudpresentation-160504181810/85/Unlimited-Virtual-Computing-Capacity-using-the-Cloud-for-Automated-Parameter-Estimation-22-320.jpg)
Uploaded byJoseph Luchette
Unlimited Virtual Computing Capacity using the Cloud for Automated Parameter Estimation
This document discusses using cloud computing infrastructure for automated parameter estimation. It outlines some limitations of traditional computing like limited processors, computing power, and availability. Cloud computing allows leveraging third-party computing capabilities over the internet as a service. Examples are provided of using cloud infrastructure for serial and parallel parameter estimation modeling runs. The conclusions state that parameter estimation using PEST can be performed on cloud infrastructure, eliminating limitations of small to mid-size businesses and providing scalable and low-cost computational resources.
More Related Content
Similar to Unlimited Virtual Computing Capacity using the Cloud for Automated Parameter Estimation
Unlimited Virtual Computing Capacity using the Cloud for Automated Parameter Estimation
- 1. Joseph A. Luchette, GregoryK. Nelson, Charles F. McLane III, Liliana I. Cecan. McLane Environmental, LLC., Princeton, NJ Unlimited Virtual Computing Capacity using the Cloud for Automated Parameter Estimation
- 2. Why? Where? How? Outline Problem Description Cloud Computing Introduction Setupand Use Flexibility/Limitations/Reliability Costs Example Runs Conclusions
- 3. Problem Description Limited numberof processors
- 4. Problem Description Limited numberof processors Inadequate computing power
- 5. Problem Description Limited timesof availability
- 6. Problem Description Limited timesof availability Cost of hardware
- 7. Cloud Computing Introduction Leveraging 3rdparty computing capability through the internet as a service.
- 8. Cloud Computing Types ofCloud Services Auto scaling applications.
- 9.
- 10. Infrastructure-as-a-Service Setup and Use Pointand Click Interfaces or APIs
- 11. Setup and Use Pointand Click Interfaces or APIs Root Access Infrastructure-as-a-Service
- 12. Infrastructure-as-a-Service Your Server Setup andUse Point and Click Interfaces or APIs Root Access Server “Images” – Save and Deploy
- 13. Infrastructure-as-a-Service Setup and Use Pointand Click Interfaces or APIs Root Access Server “Images” – Save and Deploy Networking Network Interfaces (NICs) Public/Private IP addresses
- 14. Infrastructure-as-a-Service Flexibility/Limitations/Reliability Operating System • Emulation(Qemu) • Server 2003/XP • Server 2008/Vista
- 15. Infrastructure-as-a-Service Flexibility/Limitations/Reliability Operating System CPU Equivalencies –i.e. One EC2 ”Compute Unit” provides the equivalent CPU capacity of a 1.0-1.2 GHz 2007 Opteron or 2007 Xeon processor. Up to 20 units in a High-CPU instance on EC2. (8 virtual cores with 2.5 Compute units each)
- 16. Infrastructure-as-a-Service Flexibility/Limitations/Reliability Operating System CPU RAM &Disk More RAM and Disk?
- 17. Infrastructure-as-a-Service Flexibility/Limitations/Reliability Operating System CPU RAM &Disk Data transfer and SLA
- 18. Infrastructure-as-a-Service Costs Metered pricing RAM orCPU capacity by the hour $.08/hr to $1.20/hr Data transfer in/out $0.10 to $0.50 per GB BandwidthRAM CPU
- 19. Infrastructure-as-a-Service Costs Metered pricing RAM orCPU capacity by the hour $.08/hr to $1.20/hr Data transfer in/out $0.10 to $0.50 per GB Extras? Operating system and software BandwidthRAM CPU
- 20. Example Runs Serial –Pumping Test One virtual machine on GoGrid
- 21. Example Runs Serial –Pumping Test WTAQ model (USGS)
- 22. Example Runs Serial –Pumping Test WTAQ model (USGS) Parameter Initial Estimated Horizontal Hydraulic Conductivity [ft/day] 37 10 37 Vertical Hydraulic Conductivity [ft/day] 0.81 0.1 0.81 45.7 100 45.7 Specific Storage [ft-1] 0 0 0 Original Model Vertical Hydraulic Conductivity anisotropy [Kh/Kv]
- 23. Example Runs Serial –Pumping Test 10-5 10-4 10-3 10-2 10-1 100 101 Time 10-3 10 -2 10-1 100 10 1 102 103 Drawdown 10-5 10-4 10-3 10-2 10-1 100 101 Time Initial Estimated 0 100 200 300 400 500 Initial Estimated 0 100 200 300 400 500 OriginalWTAQModel Model Vs Initial Real Vs Initial 45 degree line 0 4 8 12 16 Optimized Estimated 0 4 8 12 16 Model Vs Estimated Real Vs Optimized 45 degree line
- 24. Example Runs Parallel –Regional Model 4 virtual machines on GoGrid Master Slaves
- 25. Example Runs Parallel –Regional Model MODFLOW 2000 Layer 1 Layer 2 Layer 3 Layer 4 Layer 5
- 26. Example Runs Parallel –Regional Model MODFLOW 2000 700 710 720 730 740 750 760 770 780 790 800 700 710 720 730 740 750 760 770 780 790 800 Modelled Water Level Elevation OriginalModelWaterLevelElevation 700 710 720 730 740 750 760 770 780 790 800 700 710 720 730 740 750 760 770 780 790 800 Estimated Water Level Elevation OriginalWaterLevelElevation Original Model vs Estimate 45° Line
- 27. Conclusions PEST parameter estimationcan be performed on a cloud infrastructure.
- 28. Conclusions PEST parameter estimationcan be performed on a cloud infrastructure. Eliminate the limiting factors of small-mid size business settings for running computationally intensive modeling. Utilize scalable, low cost, on demand computational resources
- 29.