Cloud Telecons 3

From Earth Science Information Partners (ESIP)

Dec 19, 2011 ESIP cloud computing cluster Telecon

  1. Participants: Carol Meyer, Ken Keiser, Rich Martin, Thomas Huang, Phil Yang, Doug Nebert, Qunying Huang, Aigun Heo, Aijun Chen, Brand Niemann, Liping Di, Long Phan, Jason Simmons, Chris Webber, Peter Cornillon, Upendra Dadi, Thomson Nguy(Amazon)
  1. Co-Chair: Thomas Huang, Rick Martin, Phil Yang
  2. Supportor: Qunying Huang
  3. Call-in toll-free number (US/Canada): 1-877-668-4493
  4. Attendee access code: 23133897#


  1. ESIP Winter Meeting Cloud Computing Schedule (Thomas Huang)
  2. ESIP Cloud Computing Testbed (Phil Yang)
  3. Amazon cloud computing presentation(Rick Martin)


ESIP Winter Meeting Cloud Schedules (Thomas Huang)

  1. Link for agenda:
  2. Mike Little will talk about SMD plan for cloud computing and relevant activities
  3. Ames/Nebula: Talks from Nebula on capabilities, architecture, open discussions for NASA cloud computing initiatives
  4. JPL will introduce their activities of using cloud computing

ESIP Cloud Computing Testbed (Phil Yang)

  1. Requirements for cloud comptuing testbed is evolving at (please feel free to update):

  1. Discussed in the 2011 summer meeting: objectives, activities
  2. More topics will be discussed in the winter meeting
    1. The goal is to define the testbed, possible vendors either to buy a testbed or contribute a testbed environment
    2. Carol and Phil talked with Microsoft for Azure
    3. Thomas is looking for NASA cloud resources for NASA users
    4. Rick is checking with other commercial providers

Amazon cloud computing presentation (Rick Martin, Thomson Nguy )

  1. Prensenter: Thomson Nguy (Amazon)
  2. Topic: Overall of what is Amazon doing, AWS clients, security strategies
  3. Presentation Notes:
    1. Infrasturcture as a service
    2. Driving: on-demand easy to use, launch, turn off
    3. Customer choice: a variety of flexible choices for user, OS, programming tools, applications
    4. Flexible Pricing models: on-demand, elastic scale up and down, reserved instance or reserved fixed price, combination of price model , 50% saving price
    5. More than 100 governments and institution client
    6. Use cases: Mars exploration rovers, deep space network,carbon in the articic reservoir vuluneratiblity experiment, lunar mapper
    7. Mars Science Lab: fast motion field test- image processing in the cloud, massively parallel computations
    8. European space agency: ESA centre for earth observations; data collected by satellites in s3; 50.000 users at peak 30TB at a time; scale up storage
    9. Research and collaboration: Observable mdeical outcomes program(OMOP); Needed platform for cross-industry collaboration ; solution: OMOP use AWS for their research lab for providing a scientific computational platform for researchers; Benefits: avoided capital expense; flexibility to scale up and down based on computations demand and need; research tools can be shared with the extended community
    10. Top 5 Pharma: dynamic molecular modeling; large parallel problem; set up 30.000 cores –centos, 26.7 TB RAM, 2 PB disk space, HTTPs, ssh , 256-bit AES; supported software: condor(job management), chef (configuration management, Grill (cycle plugin for chef); speed and agility, $1.279 per hour, 3 hours batch job
    11. Security: AWS certifications, sarbanes-oxley(SOX), SAS70 type II audit, pci data security standard company (Credit card) etc;
    12. Security is a shared responsibility
    13. AWS VPC Architecture


  1. Licence
    1. Thomson:Buy licensed virtual machines charged by hours
    2. Doug: If you buy the agreement in advance, e.g, ESRI, you can use and install the vm without extra charge
  2. How to select optimal options for VMs
    1. Thomson:AWS provide the calculation model, but basically requires user to test and make decision
  3. Charge: any tools to monitor usage and costs
    1. Create several accounts by IAM service
    2. Credit card/GSA
    3. CloudWatch service to monitor the usage
  4. Computing units for cloud computing
    1. Thomson: EC unit , ~ 1.0 -1.2 GHz
  5. Storage indexing for massive data
    1. S3,users bulid index;
    2. EBS
    3. Amazon has DB services, simple DB, could help mange massive data;
  6. Customer back-up storage
    1. Thomson: HSM, partners provide the tools for customizing the storage

Meeting Adjourned at 2:30 pm