1. Update on Computing/Cloud Marco DestefanisUniversità degli Studi di Torino Stefano Bagnasco, Flavio Astorino, Dario Berzano, Stefano Lusso, Marco Maggiora, Sara Vallero, Laura Zotti IHEP Beijing BESIII Ferrara, Italy October 21, 2014

2. Motivation • The amount of resources and the variety of applications is steadily increasing, manpower unfortunately is not • It is becoming almost mandatory to consolidate such resources to achieve scalability and economies-of-scale • Separate application management from infrastructure management • Our Data Centers need to become providers of computing and storage resources, not (only) of high level services • The Cloud approach (IaaS) might help to better provision resources to the different scientific computing applications • Grid Sites, small or medium computing farms, single users,… • Admit dynamic resource relocation to increase CPU power for a Grid and reduce some other that are not using resources or having less priority • Several cloud computing projects are starting at national and European level • From definition of best practices and reference configurations to deployment of large-scale distributed infrastructures • A local working cloud infrastructure will also allow to take immediately part in such activities

3. Cloud Computing • On-demand self-service. • A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider. • Broad network access. • Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client. • Resource pooling. • Computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. • Rapid elasticity. • Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. • Measured service. • Cloud systems automatically control and optimize resource use by leveraging a metering capability at a level of abstraction appropriate to the type of service.

4. Service Models Service layer SaaS • Software-as-a-Service • Platform-as-a-Service • Infrastructure-as-a-Service PaaS IaaS Abstraction layer Hardware & Infrastructure

5. Service Models IaaS PaaS SaaS Applications Applications Applications You manage Data Data Data Runtime Runtime Runtime Middleware Middleware Middleware O.S. O.S. O.S. We manage Virtualization Virtualization Virtualization Servers Servers Servers Storage Storage Storage Networking Networking Networking

6. Compute Nodes • 7 Dual Twin Olidata (4 hosts) • Host: 2 x AMD 6320 MHz 64 GB RAM 175 HS06 • +1 DG1 fundings, in test • 3 twins dedicated to BESIII • 2KHS06 dedicated to BESIII + the partial use of 0.7KHS06 from DG1 Storage • 400 TB gross DELL MD 3660f + expansion 20 TB net for BESIII

7. CloudInfrastructure BOSS running Not all random trigger data Reconstruction for phase data Analysis if we have the dst Now: 1) All the main services are installed 2) Direct submission to CE 3) CVMFS tested 4) Job submission in Dirac included 5) Submission from IHEP tested 6) Cloud monitoring: Zabbix

8. Test Cloud – Cpu and Mem cpu load mem load

9. Test Cloud – Cpu and Mem cpu load mem load

10. Cloud – OpenNebula Interface

11. Cloud – ZabbixMonitoring mem load cpu load

12. Cloud – Running Jobs

13. Cloud – IHEP Job Submission

14. BESIII Cloud Lab

15. Cloud – Future Plans • Dirac server and client configuration • Storage elements if new storage is funded • Multi-purpose cloud monitoring • Integration with other clouds • Jobs user friendly • Multiple or multipurpose OS • Priorities jobs in the same experiment jobs in shared resources • Elastic instantiation of VMs