1. More than IaaS Academic Cloud Services for Researchers
More than IaaS
Academic Cloud Services for Researchers
Patrik Schnellmann
Linz, 31. May 2017

2. Infrastructure as a Service
Infrastructure as a Service
How we make the difference with IaaS

3.
Is there a difference?

4.
obstgrossmarkt.de

5.
swissfruit.ch

6.
Differentiation

7. Strategic considerations
Strategic considerations
If you compete on price, you are lost.
With our cloud services we address the market niche of higher education.
Therefore we have to carefully position our cloud services!

8. Swiss community cloud Based on OpenStack and Ceph
Swiss community cloud
Based on OpenStack and Ceph
3’464 CPU cores 20 TB RAM
3 PB Disk storage 10 Gbps network
Zurich, University of the Arts University of Lausanne

9. Use Cases Research Teaching Central IT services Network Simulations
Use Cases
Research
Network Simulations
Big Data Analysis
Physics simulations
Biochemistry
Teaching
Student VM for IT courses
Learning environment for big data
Academic software (iPython, R, SPSS)
Central IT services
Off Site Secondary Servers
External Monitoring
Storage / Backup

10.
SCALE-UP project
Goal
Create academic services on the cloud infrastructure
User group in focus are researchers and lecturers
Duration
August 2015 – December 2017
Project partners
8 project partners from universities
Funding
Co-funded by the program “Scientific Information” of swissuniversities with matching funds of the institutions

11. Virtual Private Cloud Goal
Integrate SWITCHengines virtual machines and storage into the campus network
Access easily to systems behind the firewall
Profit from scalability and redundancy
Results
Layer 2 network between campus and SWITCHengines
Examples
Enhance redundancy for authentication with an additional directory service on SWITCHengines

12. Big Data Analytics Goal Learn how to use Big Data analysis tools
Provision a ready to use Hadoop and Apache Spark cluster.
Results
Teaching: Sandbox with Zeppelin, Spark, jupyter.
Researchers: Orchestration software to deploy cluster
Lessons learned
Lots of potential in community to learn about Big Data tools
Many researchers are happy with just a powerful VM.

13. Scientific Data Pools Goal Store large quantities of data
Manage data sets and share them with other researchers
Examples
1000 genomes, Common crawl, Google ngrams
Private data sets for projects
Lessons Learned
No clear definition of “large” dataset: can be a few TBytes
Safely storing and sharing the data creates a lot of value

14. Community Service Hub Goal
Create a place for SWITCHengines based & other services
Make local services available for the whole community
Lower hurdles for offering and obtaining services (procurement, legal framework, SLA, authentication)
Example services
SWITCHdrive, SWITCHengines
IaaS / SaaS via GÉANT Cloud Procurement
Geodata service, Lab IMS (openBIS)

15. Lessons Learned Even small solutions can make researchers happy:
Lessons Learned
Even small solutions can make researchers happy:
A powerful VM with a few GBytes of storage covers already many needs.
Many researchers still spend more money for coffee than for cloud computing.
Many times, local solutions at the university are chosen as alternatives.
Know your users and their needs. Then you know how to provide them the best service!

17. How we differentiate What makes our services unique:
How we differentiate
What makes our services unique:
Flexibility to respond to specific needs
Direct contact to SWITCH and engineers
Integration to university processes (authentication, billing)
Swiss location and governing law
The biggest asset is the trust our customers have in us!