Academic Compute Cloud Provisioning and Usage AAA Project

1. Academic Compute Cloud Provisioning and UsageAAA Project Peter Kunszt ETH/SystemsX.ch 2012, October 23

2. Project Goals • How to extend current cluster services using cloud technology? • Support new application models (MapReduce, specialized servers). • Test real applications. • Understand performance implications. • Define Service Models: How to move to cloud-like service orientation models. • Define Business Models: How to accommodate pay-per-use, OpEx vs. CapEx, how to plan an academic private cloud, and how to use and offer public clouds • Run real applications: Run a regular, a compute-intensive and a data-intensive application on the cloud.

3. Project Goals Provide input to the mid- and long-term strategy for cluster and cloud infrastructure at ETH and UZH. • How to extend current cluster services using cloud technology? • Support new application models (MapReduce, specialized servers). • Test real applications. • Understand performance implications. • Define Service Models: How to move to cloud-like service orientation models. • Define Business Models: How to accommodate pay-per-use, OpEx vs. CapEx, how to plan an academic private cloud, and how to use and offer public clouds • Run real applications: Run a regular, a compute-intensive and a data-intensive application on the cloud. Disseminate results in Switzerland broadly in academia and to interested parties (Workshop at project end)

4. Project Organization SWITCH AAA Project Lead: Peter Kunszt Software Business Model UZH ETHZ Peter Kunszt SyBIT team, FGCZ Malmström group Guido Capitani others as needed Dean Flanders Markus Eurich Consultants Sergio Maffioletti Riccardo Murri Christian Panse Tyanko Alekseyev Antonio Messina Olivier Byrde +Brutus team members Sandro Mathys

5. Project Organization ETH Project Steering Board SWITCH AAA Reto Gutmann Olivier Byrde Dordaneh Arangeh Peter Kunszt Dean Flanders Michelle Binswanger Project Lead: Peter Kunszt Software Business Model UZH ETHZ Peter Kunszt SyBIT team, FGCZ Malmström group Guido Capitani others as needed Dean Flanders Markus Eurich Consultants Sergio Maffioletti Riccardo Murri Christian Panse Tyanko Alekseyev Antonio Messina Olivier Byrde +Brutus team members Sandro Mathys

6. Motivation • Today : World of Products • Hardware, Software to be bought as products • Users buy, set up, install, configure and use • Evolving into: World of Services • Software and Services bought directly as Apps • Users make use what they need immediately Users will buy more services in the future, not just products. These services will be often times in the cloud. We too want to offer services, not just products.

7. DEFINITION Cloud Attributes: When do we talk about a cloud • Self-service, On-demand, Cost transparency • Access to immediately available resources, paying for usage only. No long-term commitments. No up-front investments needed. Operational expenses only. • Elasticity, Multi-tenancy, Scalability • Grow and shrink size of resource on request. Sharing with other users without impacting each other. Economies of scale.

8. Definitions • Self-service: A consumer can unilaterally provision computing capabilities, such as server time and network storage, without requiring human interaction. • On-demand: As needed, at the time when needed, automatic provisioning. • Cost Transparency: Accounting of actual usage transparent to user and service provider both, measured in corresponding terms (Hours CPU time, GB per Month, MB Transfer, etc)

9. Definitions • Elastic: Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. • Multi-tenant: The provider’s computing resources are pooled to serve multiple consumers, with resources dynamically assigned and reassigned according to consumer demand. • Scalable: To the consumer, the capabilities available for provisioning often appear to be unlimited and can be appropriated in any quantity at any time. http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf

10. HPC Pyramid CSCS Computing needs Number of users

11. Relation to Cloud: As User (extension) Cloud CSCS Computing needs Use Burst Number of users

12. Today, University Clusters do not make use of the Cloud: • Technical details to be investigated: • Bursting the cluster into the cloud • Networking? • User Management? • File System? • Cloud-compatible licenses for commercial products are often not available • No billing mechanism to bill users of cluster for pay-per-use services

13. Relation to Cloud: As Provider Cloud CSCS Account / charge usage Computing needs Expose to Number of users

14. Not clear how to be a Cloud Provider with a University Cluster • Univ. cluster is not self-service • Capital expenses, not just pay-per-use • Long-term commitment • Not extensible on-demand, not elastic • Sharing with others only according to policies • More stringent terms of use, needs account • We have examples to look at: • SDSC, Cornell, Oslo

15. Infrastructure and Platform as a Service Classic Approach Today IaaS . PaaS From www.cloudadoption.org SaaS 95% time savings Infrastructure Platform Software FINISH START

16. Software & Apps run on platforms, NOT infrastructure www.cloudadoption.org

17. DEFINITION Cloud Stack Users or Portals. Can directly use each stack. CLIENTS Software User Interface MachineInterface Platform Components Services Infrastructure Compute Storage Network HARDWARE Any kind of infrastructure for any of the stacks.

18. Who can makes use of what • Users may use any service • Portals may use any service • SaaS may or may not be built on top of PaaS or IaaS • PaaS may or may not be built on top of IaaS User Portal SaaS PaaS IaaS Hardware

19. DEFINITION Public, Private, Hybrid Clouds Private Cloud Public Cloud Hybrid Cloud Connect • Own infrastructure only • In-house or hosted • Internal use or for sale • Full control on cloud stack, accounting, etc • Offered by partner organizations or cloud providers • Only operational expenses • No control on cloud stack, dependency on external partner • Private Cloud connected to Public Cloud • Remote cloud resources on-demand • Constraints on own cloud stack: needs to interoperate with public cloud Institutional boundary

20. Goal: Understand the relationships.. • ..in terms of virtual Servers • ..in terms of Storage • ..in terms of Networking

21. Goal: How to evolve the HPC Service.. • ..to be able to offer a Platform as a Service. • ..to be able to make use of public clouds seamlessly (Hybrid model, CloudBursting)

22. Goal: New Software Services

23. Goal: New Business Models • Cannot charge at full cost if we want to be the service provider (competitive advantage) • Internal and external views • Efficient, fair, feasible and generally accepted funding and charging model • New opportunities should not require to change existing business procedures for existing infrastructure (evolution not revolution) • Transparent Financial Accounting mechanism

24. Status: Information and Survey • We collected a lot of information and conducted a survey on existing solutions • Choices (we need to limit ourselves): • OpenStack • VMWare • HP Matrix

25. Cloud Stack Comparison Matrix

26. OpenStack Distribution comparison

27. Public IaaS Comparison

28. Status: Infrastructure 1 • ETH: HP CloudSystem Matrix Testbed • Operational as of THIS WEEK • 8 Intel, 8 AMD blades • 128GB memory per blade • 10TB storage 3PAR • HP Matrix cloud software is fixed • This is on RENT we have to give it back

29. Status: Infrastructure 2 • ETH: Build our own from new components. • Standard cluster nodes x16, diskless • 128GB RAM on each node • Very fast storage (SSD based) for VM images • Attach standard storage NAS from ETH • Cloud Stack: • OpenStack • VMWare • Will be here in 2 weeks • This remains at ETH after the project

30. Status: Infrastructure 3 • University of Zurich: Recycle existing components. • Set of old cluster nodes, heterogeneous • Cloud filesystem using local node storage (technologies will be evaluated) • GlusterFS • Ceph

31. Status: Software • Use Cases are defined and chosen • MapReduce (Hadoop) • Existing sw deployment: Crossbow (genomics) • New development (proteomics) • Compute intensive • GAMESS • Rosetta • Data intensive • HCS (High Content Screening) – image analysis • Servers • Matlab, R, CLC Bio, etc servers

32. Status: Business Model • Several models are being worked out • Shareholder model – one-time fee for TFLOPS or TB • Subscription model – yearly fee • Pay-per-use model • Self service options • Very detailed like Amazon • High-level ‘virtual cluster’ or PaaS • Top-level SaaS user gateways

33. Lots of Interactions • With Cloud providers • IBM, Amazon, CloudSigma, HP, Google • Software providers • VMWare, HP, Dell, OpenStack flavors (Piston, ..) • Universities • SWITCH, ZHAW, SDSC, Cornell, Imperial College, U Oslo, Zaragoza

34. Next Steps • Cloud Bursting of Cluster into our own cloud and into Amazon (reproducing VM-MAD) • Startup and teardown times • Management tests • Performance • Use Cases are set up on the infrastructure

35. SDCD 2012: Supporting Science with Cloud Computing • November 19 2012, University of Bern, http://www.swing-grid.ch/sdcd2012/ • The EcoCloud Project [EPFL: Anne Wiggins] • Academic Compute Cloud Project at ETH [ETH/SystemsX: Peter Kunszt] • From Bare-Metal to Cloud [ZHAW/ICClab: Andrew Edmonds] • Review of CERN Data Center Infrastructure [CERN: Gavin McCance] • Big Science in the Public Clouds: Watching ATLAS proton collisions at CloudSigma [CloudSigma: Michael Higgins] • Supporting Research with Flexible Computational Resources [University Oxford: David Wallom] • The iPlant Collaborative: Science in the Cloud for Plant Biology [University of Arizona/iPlant: Edwin Skidmore] • Tiny Particle within Huge Data [ETH: Christoph Grab] • Roundtable discussion: Cloud Strategies and thoughts for Researchers in Switzerland