Academic Compute Cloud Provisioning and Usage Project

1. Academic Compute Cloud Provisioning and UsageProject Peter Kunszt ETH/SystemsX.ch 2012, November 19 Bern

2. Motivation Researchers often only want services, notproducts. Services rely on • Infrastructure • Middleware • Application Software • Research Informatics ‘Glue’ We ‘the supporters’ want to offer ‘Apps’ • Maintainable services • Published, usable tools and software • Browsable published research data SDCD Bern

3. Motivation: SystemsX.ch Largest Swiss national research effort to date SCHWEIZERISCHE EIDGENOSSENSCHAFTCONFÉDÉRATION SUISSECONFEDERAZIONE SVIZZERACONFEDERAZIUN SVIZRA SDCD Bern

4. Somenumbers.. • Funded by the Swiss government with CHF 25Million/year for 2008-2011, 2012, 2013-2016 • 12 Swiss Universities and Research Institutions invest a matching 25 Million/y • Projects approved by the SNSF • 14 large research projects (4-7MCHF) until 2012, 10 new starting 2013 (3MCHF) • 50+ PhD projects • 20+ interdisciplinary pilot projects • 1 strategic support project: SyBIT 2MCHF/y average SDCD Bern

5. SyBIT Project Motivation • SystemsX.ch will produce and analyze a large amount of data • Strong need for coordinationamong data providers • Strong need for commonsemantics and compatibleservice offerings • Increased need for professionally supportedtools and services SDCD Bern

6. SyBIT provides support IPP Service Providers Platforms PhosphoNetX LipidX MetaNetX PlantGrowth CellPlasticity LiverX CycliX Neurochoice WingX YeastX DynamiX CINA BattleX InfectX IT Infrastructure SyBIT Bioinformatics IPHD SDCD Bern

7. SyBIT gives feedback IPP Service Providers Platforms PhosphoNetX LipidX MetaNetX PlantGrowth CellPlasticity LiverX CycliX Neurochoice WingX YeastX DynamiX CINA BattleX InfectX IT Infrastructure SyBIT Bioinformatics IPHD SDCD Bern

8. Motivation Researchers often only want services, notproducts. Services rely on • Infrastructure • Middleware • Application Software • Research Informatics ‘Glue’ We ‘the supporters’ want to offer ‘Apps’ • Maintainable services • Published, usable tools and software • Browsable published research data Enabling Research IT as a Service SDCD Bern

9. 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. SDCD Bern

10. 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) SDCD Bern

11. 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. SDCD Bern

12. 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) SDCD Bern

13. 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

14. HPC Pyramid CSCS Computing needs Number of users SDCD Bern

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

16. 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 SDCD Bern

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

18. 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 SDCD Bern

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

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

21. 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.

22. 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 SDCD Bern

23. 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 SDCD Bern

24. 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) SDCD Bern

25. Information Gathering • We collected a lot of information and conducted a survey on existing solutions (mandate to CloudBroker) SDCD Bern

26. 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 SDCD Bern

27. Choices • Commercial Cloud Appliance • Evaluate HP CloudSystem Matrix • Integrated hardware: HP blades and 3PAR storage • Runs with VMWare or Hyper-V • Complete management and end-user interfaces • Build our own • 2 different systems (Dell based) • OpenStack: Several distributions to test • Special software: ScaleMP, cloud FS SDCD Bern

28. Cloud Stack Comparison Matrix

29. OpenStack Distribution comparison

30. Public IaaS Comparison

31. 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

32. 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 • Being installed next monday • This remains at ETH after the project

33. 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 SDCD Bern

34. HPC + Cloud: On the same HW Compute Nodes ……. Storage HPC CLUSTER CLOUD HW SDCD Bern

35. HPC + Cloud: On the same HW • Classic CLUSTER – Not Virtualized • Can be heterogeneous HW • OS controlled by Admins • Scheduler for job submission • Applications compiled and installed • Shared FS • CLOUD – Virtualized • Hypervisor and Cloud Stack controlled by Admins • Template ‘Apps’ • Users can create new • Different kinds of storage • Different setups possible • Virtual SMP Compute Nodes ……. Storage HPC CLUSTER CLOUD HW SDCD Bern

36. Storage • Ceph, Gluster • Mount REAL=non-virtual cluster FS (Lustre, GPFS) • Mount NFS • Object stores, e.g. SWIFT • Different HW • Local Disks • iSCSI • Very fast SSD-based appliance over 10Gb or FC or IB (deduplication, compression) – for VMs and fast disk SDCD Bern

37. Cloud HPC Use Cases to Test 1 • Extending the regular cluster into the cloud • Just run cluster node instances • Register back with cluster scheduler • Jobs can request these nodes explicitly • ALREADY tested using Amazon • Building a full virtualized cluster in our Cloud • Everything virtual: Cluster nodes, headnodes • Cluster FS : several options (see storage) • What do we learn? Reality check: HPC performance SDCD Bern

38. Test Case 1 Software • Use regular cluster workloads, NOT data intensive • Rosetta: structural biology • GAMESS: molecular chemistry simulation • SMSCG workloads (if we get there) SDCD Bern

39. Cloud HPC Use Cases to Test 2 • Hadoop cluster • Build the virtual cluster dedicated to Hadoop • HFS or Swift • Commercial tool cluster: Matlab • Matlab ‘cluster’: allocate a few ‘fat’ VMs to Matlab • Let it run its internal clustering, expose to user SDCD Bern

40. Test Case 2 Software • A bit more data intensive • Hadoop use cases • Proteomics: analysis of selected reaction monitoring data • Genomics: bowtie over hadoop (Crossbow) • Matlab and R • Set up cluster matlab on regular cluster • On SMP’d nodes SDCD Bern

41. Cloud HPC Use Cases to Test 3 • Data intensive workflow • InfectX pipeline: Image analysis – several TB of small files • Many kinds of scripts, mostly Matlab • Same workflow can be submitted many times • Error prone! • OpenBIS on-demand workflow • Extend metadata catalog with some basic processing capabilities using remote resources • Streaming of data to perform some processing in the cloud

42. 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 SDCD Bern

43. Business Models • 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 SDCD Bern

44. Timeline today Jul‘12 Apr‘12 Oct‘12 Jan‘13 Apr‘13 ETHProject Start SWITCH AAAProject Start SWITCH AAAProject End Information Gathering Business Model Refinement of Targets Application Definition HP CloudSystem on lease delivered ready return to HP ETH Self-built system ready call decision delivery UZH Self-built system assemblyfrom existing stuff ready Application testing

45. Output • Workshop in April’13 to show results of project • To all Swiss research community – See you there! • Input to ETH, UZH strategies for research infrastructure • Drive next procurement processes • Drive strategies for cooperation/outsourcing models • Drive new policy models for funding and sustainability SDCD Bern