Cloud vs on-prem TREs: costs, constraints, pros & cons#
Overview#
Summary#
The main decision drivers are security and cost. Cloud is more flexible for projects with different funding sources and does not require an expensive data centre for research institutions but does not offer the highest levels of security.
A potential solution is a hybrid model where you get a cloud-like infrastructure on an on-prem compute.
Cloud provision via Jisc (as oppose to direct with the cloud provider) can be cheaper and it also handles SSO: https://www.jisc.ac.uk/forms/uk-access-management-federation-sign-up# Resources: Google RADLab: https://cloud.google.com/blog/topics/public-sector/googles-new-rad-lab-solution-helps-spin-cloud-projects-quickly-and-compliantly
Next steps#
Develop a roadmap plan for a hybrid, cloud-agnostic model
Raw Notes#
Compute capacity/ data centres for advanced ML projects is expensive for research institutions
Credits make it easier to use cloud for projects with different funding sources
Could a good solution be a hybrid model where you get a cloud-like infrastructure on an on-prem compute
So could be completely disconnected from internet for high security
Google have set something like this up at Sanger
Factors determining on-prem vs cloud
security
cost
Cloud provision via Jisc (as oppose to direct with the cloud provider) can be cheaper and it also handles SSO: https://www.jisc.ac.uk/forms/uk-access-management-federation-sign-up#
Resources: Google RADLab: https://cloud.google.com/blog/topics/public-sector/googles-new-rad-lab-solution-helps-spin-cloud-projects-quickly-and-compliantly
Roadmap plan#
Questions#
What would a solution to this problem look like?
What resources would be needed (people, time, funds, infrastructure etc.)?
How can this community support you in getting them?
What working groups/orgs are already working on this, if any? How can we collaborate with them effectively?
Notes#
hybrid model (see above)
Solution that is cloud-agnostic and could also run on on-prem hardware