Cornell "case study", using Amazon's Web Service (AWS)

The "case study" cited here contains examples of innovative use of cloud-based infrastructure for provisioning research-based computing power similar to what CCB researchers get by investing in high-performance computers and clusters.

In addition to Amazon, services and providers in this space include Cornell's own CAC’s RedCloud, as well as Google Compute Engine and Microsoft Azure.

Google Compute's calculator and comparing to ChemIT's clusters:

• https://cloud.google.com/products/calculator/

Comparing Google Compute to a ChemIT's systems.

Google system we compared were 4 of their "CP-COMPUTEENGINE-VMIMAGE-N1-STANDARD-8" (8 core, 30GB RAM).

Using that service, expect to pay an Effective Hourly Rate of $0.392. And Monthly total: $1,132.10.

• And that does not include upload/ download or data storage costs.

ChemIT buys a 4-computer, 2U system that costs ~$10,000 (2.5K*4)

• And that does not include ChemIT's services or data storage costs.

Articles

http://www.admin-magazine.com/Archive/2014/21/Building-Big-Iron-in-the-Cloud-with-Google-Compute-Engine/

• (...)Google Compute Engine allows you quickly and easily to create anything from a simple single-node VM to a large-scale compute cluster on Google's world class infrastructure. As of this writing, it supports several stellar open source Linux distributions (and one closed-source option), including Debian and CentOS; CoreOS, FreeBSD, and SELinux [2]; and Red Hat Enterprise Linux, SUSE, and Windows. Instances are available with many options and are completely customizable from a hardware perspective. You can choose the number of cores, RAM, and other machine properties, and you can scale them as you grow [4]. Virtual instances start at a micro instance (f1-micro), with one core and 0.60GB of memory, and go up to 16 cores and 104GB of RAM. For the sake of the demo here, I will be using a shared core micro instance (g1-small; Table 2). Competition from Amazon, Microsoft, Rackspace, and others in the cloud marketplace has put increasing downward pressure on the price of many cloud offerings.(...)

http://www.admin-magazine.com/HPC/Articles/Building-big-iron-in-the-cloud-with-Google-Compute-Engine/ (7/2014)

• (...)Google Compute Engine was opened to the public in June 2012, a bit later than most other players in the cloud marketplace. Arrival time aside, it is a powerful, scalable, and performant IaaS solution.

  Compute Engine allows you quickly and easily to create anything from a simple single-node VM to a large-scale compute cluster on Google's world class infrastructure. As of this writing, it supports several stellar open source Linux distributions (and one closed-source option), including Debian and CentOS; CoreOS, FreeBSD, and SELinux [2]; and Red Hat Enterprise Linux, SUSE, and Windows [3].

  Instances are available with many options and are completely customizable from a hardware perspective. You can choose the number of cores, RAM, and other machine properties, and you can scale them as you grow [4]. Virtual instances start at a micro instance (f1-micro), with one core and 0.60GB of memory, and go up to 16 cores and 104GB of RAM. For the sake of the demo here, I will be using a shared core micro instance (g1-small; Table 2). Competition from Amazon, Microsoft, Rackspace, and others in the cloud marketplace has put increasing downward pressure on the price of many cloud offerings.(...)

http://www.infoworld.com/d/cloud-computing/ultimate-cloud-speed-tests-amazon-vs-google-vs-windows-azure-237169

• February 26, 2014: Ultimate cloud speed tests: Amazon vs. Google vs. Windows Azure

  A diverse set of real-world Java benchmarks shows Google is fastest, Azure is slowest, and Amazon is priciest.

Other

Roger's thoughts, from 7/30/14:

• I think cloud clustering has lots of potential for getting computations done quickly, or without permanent systems. (surge)
• I believe it will take fair amount of development & learning time (which we currently don't have much of). A working example would be useful.
• The above cited Cornell researcher's "case study" is somewhat informative to IT-type folks such as us, but on the technical side. It is non-specific regarding costs, effort, results, or comparing to physical system costs.
• Suggestions for making this "case study" more useful to our managers and researchers:
  
  1. Create a summary of the case study.
  2. Put together some numbers to allow comparison of this example and Chemistry's research computing needs and current practices.