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See also
- Restricted: Two CU researchers' Cloud case-studies (Cornell NetID required; cited below, too)
- Ramping up and down cloud services to save money
- Cornell's own CAC’s RedCloud:
- http://cloudcomputing.cornell.edu/Research.php (added 3/20/15)
- This is sponsored by the Computer Science department. (Not tied to CIT or CAC.)
- http://itnews.cornell.edu/cloudification-services-will-offer-ramp-cloud-infrastructure (3/13/15 CIT article)
- A Cornell IT manager peer shared this nice digestible interview/talk with one of his favorite experts talking about cloud and IT for life sciences:
- Note: Cross-posted in Yammer, <http://www.it.cornell.edu/services/yammer/>.
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N.B. Other academic departments in A&S which could benefit from cloud computing, per Frank Strickland (3/20/15), include Linguistics, Anthropology, Psychology, and Econ. Some of these are listed in the DNSDB entry for Cornell's cloud computing research hub.
- Restricted: Two CU researchers' Cloud case-studies (Cornell NetID required)
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| Offering | Core count compared (performance, though?) | RAM (FWIW) | Cost | Cost comparison |
|---|---|---|---|---|
| ChemIT | 48 cores (6 cores/ proc. * | 32-64GB, usually | $10,000 total hardware (~$2,600/computer * | $2,500/ yr (Assumes last 4 years, 3 of which are under warranty) Lots of local IT labor costs. (Maybe $10K/ yr, at least for first set of 4?) |
| Google Compute | 32 cores (8 cores/ computer * | 30GB | $1,132.10 per month. (Used Google Compute's calculator, | $13,585.20/ yr No local IT labor costs. |
Google Compute: More cores and RAM | 64 cores (16 cores/ computer * | 60GB | $2,264.19 per month. (Used Google Compute's calculator, | $27,170.28/ yr No local IT labor costs. |
Google Compute: More cores, less RAM | 64 cores (16 cores/ computer * | 14.4GB | $1,423.21 per month. (Used Google Compute's calculator, | $17,078.52/ yr No local IT labor costs. |
Articles
http://www.theregister.co.uk/2016/02/15/nice_catch_amazon_bezos_buys_hpc_toolkit_from_italy/
- AWS already offers HPC-clusters-as-a-service. Once it beds in NICE, it will also have a fine way to make those clusters attack certain workloads. And once it is in that position, it will be in a position to take more of the HPC market and give yet another corner of on-premises kit market a kicking.
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