MONARC : results and open issues

1. MONARC : results and open issues Laura Perini Milano

2. Layout of the talk • Most material from Irwin Gaines talk at Chep2000 • The basic goals and structure of the project • The Regional Centers • Motivation • Characteristics • Functions • Same Results from the simulations • The need for more realistic “implementation oriented” Models: Phase-3 • Relations with GRID • Status of the project: Phase-3 LOI presented in January, Phase-2 Final Report to be published next week, Milestones and basic goals met

3. MONARC • A joint project (LHC experiments and CERN/IT) to understand issues associated with distributed data access and analysis for the LHC • Examine distributed data plans of current and near future experiments • Determine characteristics and requirements for LHC regional centers • Understand details of analysis process and data access needs for LHC data • Measure critical parameters characterizing distributed architectures, especially database and network issues • Create modeling and simulation tools • Simulate a variety of models to understand constraints on architectures

4. MONARC • Models Of Networked Analysis At Regional Centers • Caltech, CERN, FNAL, Heidelberg, INFN, • Helsinki, KEK, Lyon, Marseilles, Munich, Orsay, Oxford, RAL,Tufts, ... • GOALS • Specify the main parameters characterizing the Model’s performance: throughputs, latencies • Determine classes of Computing Models feasible for LHC (matched to network capacity and data handling resources) • Develop “Baseline Models” in the “feasible” category • Verify resource requirement baselines: (computing, data handling, networks) COROLLARIES: • Define the Analysis Process • Define Regional Center Architectures • Provide Guidelines for the final Models 622 Mbits/s FNAL 4.107 MIPS 110 Tbyte Robot Desk tops 622 Mbits/s Desk tops University n.106MIPS m Tbyte Robot N x 622 Mbits/s Optional Air Freight CERN n.107 MIPS m Pbyte Robot Desk tops 622Mbits/s 622 Mbits/s 622 Mbits/s

5. Working Groups • Architecture WG • Baseline architecture for regional centres, Technology tracking, Survey of computing model of current HENP experiments • Analysis Model WG • Evaluation of LHC data analysis model and use cases • Simulation WG • Develop a simulation tool set for performance evaluation of the computing models • Testbed WG • Evaluate the performance of ODBMS, network in the distributed environment

6. General Need for distributed data access and analysis: • Potential problems of a single centralized computing center include: • - scale of LHC experiments: difficulty of accumulating and managing all resources at one location • - geographic spread of LHC experiments: providing equivalent location independent access to data for physicists • - help desk, support and consulting in same time zone • - cost of LHC experiments: optimizing use of resources located world wide

7. Motivations for Regional Centers • A distributed computing architecture based on regional centers offers: • A way of utilizing the expertise and resources residing in computing centers all over the world • Provide local consulting and support • To maximize the intellectual contribution of physicists all over the world without requiring their physical presence at CERN • Acknowledgement of possible limitations of network bandwidth • Allows people to make choices on how they analyze data based on availability or proximity of various resources such as CPU, data, or network bandwidth.

8. Future Experiment Survey • Analysis/Results • From the previous survey, we saw many sites contributed to Monte Carlo generation • This is now the norm • New experiments trying to use the Regional Center concept • BaBar has Regional Centers at IN2P3 and RAL, a smaller one in Rome • STAR has Regional Center at LBL/NERSC • CDF and D0 offsite institutions paying more attention as run gets closer.

9. Future Experiment Survey • Other observations/ requirements • In the last survey, we pointed out the following requirements for RC’s: • 24X7 support • software development team • diverse body of users • good, clear documentation of all s/w and s/w tools • The following are requirements for the central site (I.e. CERN) • Central code repository easy to use and easily accessible for remote sites • be “sensitive” to remote sites in database handling, raw data handling and machine flavors • provide good, clear documentation of all s/w and s/w tools • The experiments in this survey achieving the most in distributed computing are following these guidelines

10. Tier0: CERN • Tier1: National “Regional” Center • Tier2: Regional Center • Tier3: Institute Workgroup Server • Tier4: Individual Desktop • Total 5 Levels

11. CERN storage network 12 Gbps processors ………… 1400 boxes 160 clusters 40 sub-farms tapes 1.5 Gbps 0.8 Gbps 3 Gbps* 8 Gbps 12 Gbps* farm network 480 Gbps* 0.8 Gbps (daq) LAN-SAN routers 100 drives LAN-WAN routers CMS Offline Farm at CERN circa 2006 250 Gbps storage network 5 Gbps 0.8 Gbps 5400 disks 340 arrays ……... disks * assumes all disk & tape traffic on storage network double these numbers if all disk & tape traffic through LAN-SAN router lmr for Monarc study- april 1999

13. storage network configured as I/O servers 1.5 Gbps 3 Gbps* farm network Processor cluster sub-farm: 36 boxes, 144 cpus, 5 m2 basic box four 100 SI95 processors standard network connection (~2 Gbps) 15% of systems configured as I/O servers (disk server, disk-tape mover, Objy AMS, ..) with additional connection to the storage network cluster 9 basic boxes with a network switch (<10 Gbps) sub-farm 4 clusters - with a second-level network switch (<50 Gbps) one sub-farm fits in one rack cluster and sub-farm sizing adjusted to fit conveniently the capabilities of network switch, racking, power distribution components lmr for Monarc study- april 1999

14. Regional Centers • Regional Centers will • Provide all technical services and data services required to do the analysis • Maintain all (or a large fraction of) the processed analysis data. Possibly may only have large subsets based on physics channels. Maintain a fixed fraction of fully reconstructed and raw data • Cache or mirror the calibration constants • Maintain excellent network connectivity to CERN and excellent connectivity to users in the region. Data transfer over the network is preferred for all transactions but transfer of very large datasets on removable data volumes is not ruled out. • Share/develop common maintenance, validation, and production software with CERN and the collaboration • Provide services to physicists in the region, contribute a fair share to post-reconstruction processing and data analysis, collaborate with other RCs and CERN on common projects, and provide services to members of other regions on a best effort basis to further the science of the experiment • Provide support services, training, documentation, trouble shooting to RC and remote users in the region

15. Mass Storage & Disk Servers Database Servers Data Import Data Export Tier 2 Network from CERN Local institutes Network from Tier 2 and simulation centers Production Reconstruction Raw/Sim-->ESD Scheduled, predictable experiment/ physics groups Production Analysis ESD-->AOD AOD-->DPD Scheduled Physics groups Individual Analysis AOD-->DPD and plots Chaotic Physicists CERN Tapes Tapes Desktops Support Services Physics Software Development R&D Systems and Testbeds Info servers Code servers Web Servers Telepresence Servers Training Consulting Help Desk

16. Mass Storage & Disk Servers Database Servers Data Import Data Export Total Storage: Robotic Mass Storage - 300TB Raw Data: 50TB 5*10**7 events (5% of 1 year) Raw (Simulated) Data: 100TB 10**8 events EDS (Reconstructed Data): 100TB - 10**9 events (50% of 2 years) AOD (Physics Object) Data: 20TB 2*10**9 events (100% of 2 years) Tag Data: 2TB (all) Calibration/Conditions data base: 10TB (only latest version of most data types kept here) Central Disk Cache - 100TB (per user demand) CPU Required for AMS database servers: ??*10**3 SI95 power Tier 2 Network from CERN Local institutes Network from Tier 2 and simulation centers Production Reconstruction Raw/Sim-->ESD Scheduled, predictable experiment/ physics groups Production Analysis ESD-->AOD AOD-->DPD Scheduled Physics groups Individual Analysis AOD-->DPD and plots Chaotic Physicists CERN Tapes Tapes Data Input Rate from CERN: Raw Data - 5% 50TB/yr ESD Data - 50% 50TB/yr AOD Data - All 10TB/yr Revised ESD - 20TB/yr Data Input from Tier 2: Revised ESD and AOD - 10TB/yr Data Input from Simulation Centers: Raw Data - 100TB/yr Data Output Rate to CERN: AOD Data - 8 TB/yr Recalculated ESD - 10 TB/yr Simulation ESD data - 10 TB/yr Data Output to Tier 2: Revised ESD and AOD - 15 TB/yr Data Output to local institutes: ESD, AOD, DPD data - 20TB/yr Desktops Physics Software Development R&D Systems and Testbeds Info servers Code servers Web Servers Telepresence Servers Training Consulting Help Desk

17. Physics Sftware Development Desktops Mass Storage & Disk Servers Database Servers Data Import Data Export Tier 2 Network from CERN Local institutes Web Servers Telepresence Servers Network from Tier 2 and simulation centers Production Reconstruction Raw/Sim-->ESD Scheduled experiment/ physics groups Production Analysis ESD-->AOD AOD-->DPD Scheduled Physics groups Individual Analysis AOD-->DPD and plots Chaotic Physicists CERN Tapes Info servers Code servers Tapes Event Selection Jobs: 10 physics groups * 10**8 events (10%samples) * 3 times/yr based on ESD and latest AOD data 50 SI95/evt ==> 5000 SI95 power Physics Object creation Jobs: 10 physics groups * 10**7 events (1% samples) * 8 times/yr based on selected event sample ESD data 200 SI95/event ==> 5000 SI95 power Derived Physics data creation Jobs: 10 physics groups * 10**7 events * 20 times/yr based on selected AOD samples, generates “canonical” derived physics data 50 SI95/evt ==> 3000 SI95 power Total 110 nodes of 100 SI95 power Training Consulting Help Desk Farms of low cost commodity computers, limited I/O rate, modest local disk cache ----------------------------------------------------- Reconstruction Jobs: Reprocessing of raw data: 10**8 events/year (10%) Initial processing of simulated data: 10**8/year 1000 SI95-sec/event ==> 10**4 SI95 capacity: 100 processing nodes of 100 SI95 power Derived Physics data creation Jobs: 200 physicists * 10**7 events * 20 times/yr based on selected AOD and DPD samples 20 SI95/evt ==> 30,000 SI95 power Total 300 nodes of 100 SI95 power R&D Systems and Testbeds

18. MONARC Analysis Process Example

19. Model and Simulation parameters • Have a new set of parameters common to all simulating groups. • More realistic values, but still to be discussed/agreed on the basis of Experiment’s information. 1000 Proc_time_RAW SI95sec/event (350) 25 Proc_Time_ESD “ (2.5) 5 Proc_Time_AOD “ (0.5) 3 Analyze_Time_TAG “ 3 Analyze_Time_AOD “ 15 Analyze_Time_ESD “ (3) 600 Analyze_Time_RAW “ (350) 100 Memory of Jobs MB 5000 Proc_Time_Create_RAW SI95sec/event (35) 1000 Proc_Time_Create_ESD “ (1) 25 Proc_Time_Create_AOD “ (1)

20. Base Model used • Basic Jobs • Reconstruction of 107 events : RAW--> ESD --> AOD --> TAG at CERNIt’s the production while the data are coming from the DAQ (100 days of running collecting a billion of events per year) • Analysis of 5 Working Groups each of 25 analyzers on TAG only (no request to higher level data samples). Every analyzer submit 4 sequential jobs on 106 events.Each analyzer work start-time is a flat random choice in the range of 3000 seconds.Each analyzer data sample of 106 events is a random choice in the complete data sample of TAG DataBase consisting of 107 events. • Transfer (FTP) of a 107 events ESD, AOD and TAG from CERN to RC • CERN Activities : Reconstruction, 5 WG Analysis, FTP transfer • RC Activities : 5 (uncorrelated) WG Analysis, receive FTP transfer • Job’s “paper estimate”: • Single Analysis Job : 1.67 CPU hours at CERN = 6000 sec at CERN (same at RC) • Reconstruction at CERN for 1/500 RAW to ESD : 3.89 CPU hours = 14000 sec • Reconstruction at CERN for 1/500 ESD to AOD : 0.03 CPU hours = 100 sec

21. Resources: LAN speeds ?! • In our Models the DB Servers are uncorrelated and thus one activity uses a single Server. The bottlenecks are the “read” and “write” speed to and from the Server. In order to use the CPU power at reasonable percentage we need a read speed of at least 300 MB/s and a write speed of 100 MB/s (milestone already met today) • We use 100 MB/s in current simulations (10 Gbits/sec switched LANs in 2005 may be possible). • Processing node link speed is negligible in our simulations. • Of course the “real” implementation of the Farms can be different, but the results of the simulation do not depend on “real” implementation: they are based on usable resources. See following slides

22. More realistic values for CERN and RC • Data Link speeds at 100 MB/sec (all values) except : • Node_Link_Speed at 10 MB/sec • WAN Link speeds at 40 MB/sec • CERN • 1000 Processing nodes each of 500 SI95 • RC • 200 Processing nodes each of 500 SI95 1000 Processing nodes times 500SI95 = 500kSI95 about the CPU power of CERN Tier0 disk space as for the number of DBs 100kSI95 processing Power = 20% CERN disk space as for the number of DBs

23. Overall Conclusions • MONARC simulation tools are: • sophisticated enough to allow modeling of complex distributed analysis scenarios • simple enough to be used by non experts • Initial modeling runs are alkready showing interesting results • Future work will help identify bottlenecks and understand constraints on architectures

24. MONARC Phase 3 More Realistic Computing Model Development • Confrontation of Models with Realistic Prototypes; • At Every Stage: Assess Use Cases Based on Actual Simulation, Reconstruction and Physics Analyses; • Participate in the setup of the prototyopes • We will further validate and develop MONARC simulation system using the results of these use cases (positive feedback) • Continue to Review Key Inputs to the Model • CPU Times at Various Phases • Data Rate to Storage • Tape Storage: Speed and I/O • Employ MONARC simulation and testbeds to study CM variations, and suggest strategy improvements

25. MONARC Phase 3 • Technology Studies • Data Model • Data structures • Reclustering, Restructuring; transport operations • Replication • Caching, migration (HMSM), etc. • Network • QoS Mechanisms: Identify Which are important • Distributed System Resource Management and Query Estimators • (Queue management and Load Balancing) • Development of MONARC Simulation Visualization Tools for interactive Computing Model analysis

26. Relation to GRID • The GRID project is great! • Development of s/w tools needed for implementing realistic LHC Computing Models • farm management, WAN resource and data management, etc…. • Help in getting funds for real life testbed systems (RC prototypes) • Complementarity GRID-MONARC hierarchical RC Model • Hierarchy of RC is a safe option. If GRID will bring big advancements, less hierarchical models should alo become possible • Timings well matched • MONARC Phase-3 to last ~1 year: bridge to GRID project starting early in 2001 • Afterwards common work by LHC experiments for developping the computing models will surely be still needed: in which project framework and for how long we will see then...