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Distributed IaaS Clouds and 100G Networking for HEP applications

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(1)   Distributed IaaS Clouds and 100G Networking for HEP applications. HPCS 2013 June 3. Ian  Gable    A.Agarwal,  A.Charbonneau,  C.Leavett-­‐Brown,  K  Lewall.  R.  Impey  ,  M.Paterson,     W.  Podiama,  R.J.  Sobie,    R.Taylor  . University  of  Victoria,  NRC  . M.  Hay,  D.  McWilliam    Y.  Savard,  T.  Tam  . BCNet  and  CANARIE   A.  Barczyk,  R.  Hockett,  D.  Kcira,    I.  Legrand,  S.  McKee,  A.  Mughal,   H.  Newman,    S.  Rosza,  S.  Timoteo,  R.  Voicu  . Caltech  and  University  of    Michigan  . 1  .

(2) Typically, computing tasks have been moved to the data. . With Infrastructure-as-a-Service clouds we want to move the data to computing on demand as resources become available.. Networks are the key.. Network Data. VM Node. IaaS Clouds. 2  .

(3) Outline. Running ATLAS jobs on distributed IaaS cloud. – Motivation. – Software required. – Results from 14 months of production operation. 100G network demonstrations at Super Computing 2012. – HEP networks today. – Physical setup. – Results. 3  .

(4) ATLAS Experiment in One Slide. • A particle detector at CERN which gathers a vast amount of data (100+ PB in last 2 years). • Analysis and simulation computational tasks (i.e. jobs) are embarrassingly parallel. • Most of the computation is completed today on the WLCG Grid.   See  Reda  Tafirout’s  talk:   “ATLAS  Experiment:  Big  Data  +  Big  Compute  =  Big  Discovery”   Immediately  following  this  talk       4  .

(5) Many facilities are evolving into clouds. Can we use these sites in a federated way?. And integrate them into our existing systems?. What are some of the challenges?. Distributed cloud computing system for ATLAS. Production system for ATLAS experiment. Integrated into the WLCG. 5  .

(6) HTCondor     JobQueue  . Cloud   Scheduler     Discover  User-­‐Job  . UserJob   User   VM  . IaaS   Clouds   Randall  Sobie        IPP/UVictoria  . 6  .

(7) HTCondor     JobQueue  . Cloud   Scheduler    . Boot  User-­‐VM   on  a  cloud   UserJob   User   VM  . IaaS   Clouds   Randall  Sobie        IPP/UVictoria  . 7  .

(8) HTCondor     JobQueue  . Cloud   Scheduler    . UserJob  . VM  registers     with  HTCondor  . User   VM  . IaaS   Clouds   Randall  Sobie        IPP/UVictoria  . 8  .

(9) HTCondor     JobQueue  . Cloud   Scheduler    . UserJob   Dispatches  UserJob   to  VM  . Remote  VM  image  repository  (Nimbus  clouds)   Upload  VMs  to  OpenStack  clouds  . User   VM  . IaaS   Clouds   9  .

(10) Clouds  . Nimbus    . Victoria(3)   Ottawa   FutureGrid  Chicago   FutureGrid  SanDiego   FutureGrid  Florida  . Current clouds. OpenStack  .   Melbourne-­‐NECTAR   CERN-­‐Ibex   CANARIE-­‐West   CANARIE-­‐East   Imperial  College-­‐GridPP  .  . 10  .

(11) Cloud evaporation and condensation. WestGrid     outage   FutureGrid   Monthly   Maintenance    . Number  of  8-­‐core  VMs  in  May  2013  . 11  .

(12) VMs in 2013. OpenStack  Clouds   Nimbus  Clouds  (and  NECTAR)  . CANARIE   CERN    IBex  . Imperial     College  . 8-­‐core  VMs  at  each  site  . 12  .

(13) 400,000.  . Fully  integrated  as  an  ATLAS  Grid  site   (grid  operations,  monitoring,  …)   April  2012  . 15,000.     Integrated  number  of  jobs        (380,000)       Weekly  jobs  (12,500  in  2013)       Peak  over  1000  simultaneous  jobs     April  2013  .  . 13  .

(14) Moving  on  to  Networking  . 14  .

(15) HEP networks today. 15  .

(16) Next Generation 100G Networks. At Super Computing 2012 in November 2012 in Salt Lake City a we worked together to set new records for Wide Area Network transfer..  . CANARIE, BCNet, Internet 2. 3x 100G circuits into a single conference booth. 16  .

(17) SC12 WAN. Caltech Ě Victoria Ě Michigan Efficient LHC Data Distribution across 100GE Networks 4  x  40G   QSFP  SR. 100G Seattle to BCNet. Univ of Victoria. 40GE  Gen3   Servers 710 STARLIGHT. 100G. Univ of Michigan. SDN. 100G. Internet2 SLC. Internet2, Caltech 818W LA. SDN. SDN. SCinet 40GE  Gen3   Server 100G. Caltech Booth.  100G. Cisco ONS 15454 Optical Network System. Cisco  M6 100GE  DWDM. 9  x  40G   QSFP  SR. Cisco ONS 15454 Optical Network System. 3  x  40G   QSFP  SR. Alcatel  SR-­‐12 100/40G Switch-­‐Router. Alcatel  SR-­‐12 100/40G Switch-­‐Router. 40GE  Gen3   Servers. Juniper MX480 100/40G Switch-Router. 40GE  Gen3   Servers. DDN SFA-­‐12K. 17  .

(18) Victoria to Salt Lake City. 100G. Seattle to BCNet SDN. 4 x 40G QSFP SR. Univ of Victoria. SCinet. Internet2 SLC. UVic Ě SC12 WAN Diagram. 100G  100G. Caltech Booth At SC12. 6  x  40G   QSFP  SR. 100/40G Switch-­‐router. 40GE  Gen3   Servers. 18  .

(19) IBM x3650 M4. 19  .

(20) Juniper MX480. 20  .

(21) Ciena OME 6500. 21  .

(22) 22  .

(23) 23  .

(24) SC Show Floor 40G and 100G equipment. 4x SuperMicro Servers SSD Storage. 2x SuperMicro Lustre clients. Data Direct Networks Storage 120 TB. 2x Dell R720 Lustre clients Alcatel-Lucent 3x100GE 15x40GE Juniper MX480 1x100GE 4x40GE 2x SuperMicro Servers FusionIO Storage. 2x Dell R520 Lustre clients Force10/Dell Z9000 (40GE x 32) 33 Mellanox ConnectX-3 VPI cards. 24  . 2.

(25) Alcatel 3 x LR4 100G CFP. 25  .

(26) Force 10 z9000 32x40G Ethernet . 26  .

(27) Day One: Memory to Memory.    100G . Memory to Memory Transfer UVic to SC. 3 Machine at UVic to Three Machines at SC. At SC11 this was hard to achieve.    0G . 27  .

(28) Disk to Disk. 4 IBM x3650 with OCZ SSDs at UVic. to. SSD servers and Lustre clients mounting DDN SFA12K. 1500 km. Peak 96 Gbps sustained 85 Gbps for 10 hours. 28  .

(29) Remote Direct Memory Access over Ethernet. 5% CPU Utilization. 1 Server in the Booth and two Servers at Caltech. 29  .

(30) 337 Gbps peak. Memory to Memory. Rate equivalent to 3PB per day. 30  .

(31) Successes. IaaS Production system. – ATLAS IaaS distributed cloud in production for 14 month. – CANFAR (astronomy) using the same technology. – Federated system 10+ clouds over 3 continents, HEP and non-HEP sites. – 1000 simultaneous jobs. – Dynamic system that handles changing availability. 100G networking. – 96 Gbps disk to disk using ‘modest’ set of hardware over 1500 km. – 337 Gbps memory to memory using 3 sites. – Promising demonstration of RDMA over Ethernet.. – Possible to efficiently use 100G networks at the end site. 31  .

(32) Summary. Clouds are increasingly available to the research community.. Distributed cloud computing is a viable solution for scientific computing. No observed limits to the system scalability in low I/O cases.. We expect 100G networks to remove barriers to high I/O applications on clouds.. 32  .

(33) Industrial Partners. 33  .

(34) http://heprc.phys.uvic.ca   [email protected]     @igable   Cloud  Scheduler:   https://github.com/hep-­‐gc/cloud-­‐scheduler    . 34  .

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