1 / 23

Building a Regional Centre

Building a Regional Centre. A few ideas & a personal view CHEP 2000 – Padova 10 February 2000 Les Robertson CERN/IT. Summary. LHC regional computing centre topology Some capacity and performance parameters From components to computing fabrics Remarks about regional centres

moya
Download Presentation

Building a Regional Centre

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Building a Regional Centre A few ideas & a personal view CHEP 2000 – Padova 10 February 2000 Les Robertson CERN/IT

  2. Summary • LHC regional computing centre topology • Some capacity and performance parameters • From components to computing fabrics • Remarks about regional centres • Policies & sociology • Conclusions

  3. Why Regional Centres? • Bring computing facilities closer to home • final analysis on a compact cluster in the physics department • Exploit established computing expertise & infrastructure • Reduce dependence on links to CERN • full ESD available nearby - through a fat, fast, reliable network link • Tap funding sources not otherwise available to HEP • Devolve control over resource allocation • national interests? • regional interests? • at the expense of physics interests?

  4. 2.5 Gbps IN2P3 622 Mbps RAL FNAL Tier 1 155 mbps 155 mbps 622 Mbps Uni n Lab a Tier2 Uni b Lab c   Department  Desktop The MONARC RC Topology CERN – Tier 0 University physics department • Final analysis • Dedicated to local users • Limited data capacity – cached only via the network • Zero administration costs (fully automated) Tier 0 – CERN • Data recording, reconstruction, 20% analysis • Full data sets on permanent mass storage – raw, ESD, simulated data • Hefty WAN capability • Range of export-import media • 24 X 7 availability Tier 1 – established data centreor new facility hosted by a lab • Major subset of data – all/most of the ESD, selected raw data • Mass storage, managed data operation • ESD analysis, AOD generation, major analysis capacity • Fat pipe to CERN • High availability • User consultancy – Library & Collaboration Software support Tier 2 – smaller labs, smaller countries, probably hosted by existing data centre • Mainly AOD analysis • Data cached from Tier 1, Tier 0 centres • No mass storage management • Minimal staffing costs MONARC report: http://home.cern.ch/~barone/monarc/RCArchitecture.html

  5. 2.5 Gbps 622 Mbps 155 mbps 155 mbps 622 Mbps Desktop The MONARC RC Topology CERN – Tier 0 IN2P3 RAL FNAL Tier 1 Uni n Lab a Tier2 Uni b Lab c   Department  MONARC report: http://home.cern.ch/~barone/monarc/RCArchitecture.html

  6. Capacity / Performance all CERN today ~15K SI95 ~25 TB ~100 MB/sec 20% CERN ** 1 SPECint95 = 10 CERNunits = 40 MIPS

  7. Capacity / Performance Approx. Number of farm PCs at CERN today May not find disks as small as that! But we need a high disk count for access, performance, RAID/mirroring, etc. We probably have to buy more disks, larger disks, & use the disks that come with the PCsmuch more disk space Effective throughput of LAN backbone 1.5% of LAN

  8. Building a Regional Centre Commodity components are just fine for HEP • Masses of experience with inexpensive farms • LAN technology is going the right way • Inexpensive high performance PC attachments • Compatible with hefty backbone switches • Good ideas for improving automated operation and management

  9. Evolution of today’s analysis farms Computing & Storage Fabric built up from commodity components • Simple PCs • Inexpensive network-attached disk • Standard network interface (whatever Ethernet happens to be in 2006) with a minimum of high(er)-end components • LAN backbone • WAN connection

  10. Standard components Computing & Storage Fabric built up from commodity components • Simple PCs • Inexpensive network-attached disk • Standard network interface (whatever Ethernet happens to be in 2006) with a minimum of high(er)-end components • LAN backbone • WAN connection

  11. HEP’s not special, just more cost conscious Computing & Storage Fabric built up from commodity components • Simple PCs • Inexpensive network-attached disk • Standard network interface (whatever Ethernet happens to be in 2006) with a minimum of high(er)-end components • LAN backbone • WAN connection

  12. Limit the role of high end equipment Computing & Storage Fabric built up from commodity components • Simple PCs • Inexpensive network-attached disk • Standard network interface (whatever Ethernet happens to be in 2006) with a minimum of high(er)-end components LAN backbone WAN connection

  13. Components  building blocks 36 dual 200 SI95 cpus = 14K SI95s ~ $100K 224 3.5” disks 25-100 TB $50K - $200K 2000 – standard office equipment 36 dual cpus ~900 SI95 120 72GB disks ~9 TB 2005 – standard, cost-optimised, Internet warehouse equipment For capacity & cost estimates see the 1999 Pasta Report: http://nicewww.cern.ch/~les/pasta/welcome.html

  14. The Physics Department System • Two 19” racks & $200K • CPU – 14K SI95 (10% of a Tier1 centre) • Disk – 50TB (50% of a Tier1 centre) • Rather comfortable analysis machine  • Small Regional Centres are not going to be competitive • Need to rethink the storage capacity at the Tier1 centres

  15. Tier 1, Tier 2 RCs, CERN A few general remarks: • A major motivation for the RCs is that we are hard pressed to finance the scale of computing needed for LHC • We need to start now to work together towards minimising costs • Standardisation among experiments, regional centres, CERN so that we can use the same tools and practices to … • Automate everything • Operation & monitoring • Disk & data management • Work scheduling • Data export/import (prefer the network to mail) in order to … • Minimise operation, staffing – • Trade off mass storage for disk + network bandwidth • Acquire contingency capacity rather than fighting bottlenecks • Outsource what you can (at a sensible price) • ……. Keep it simple Work together

  16. The middleware The issues are: • integration of this amorphous collection of Regional Centres • Data • Workload • Network performance • application monitoring • quality of data analysis service Leverage the “Grid” developments • Extending Meta-computing to Mass-computing • Emphasis on data management & caching • … and production reliability & quality – Keep it simple Work together

  17. cpu/disk net tape/DVD 200 m2 A 2-experiment Tier 1 Centre Requirement: 240K SI95 220 TB Basic equipment ~ $3m cpus/disks Processors 20 “standard” racks = 1,440 cpus  280K SI95 Disks 12 “standard” racks = 2,688 disks  300TB (with low capacity disks)

  18. The full costs? • Space • Power, cooling • Software • LAN • Replacement/Expansion 30% per year • Mass storage • People

  19. mass storage ? Do all Tier 1 centres really need a full mass storage operation? • Tapes, robots, storage management software? Need support for export/import media • But think hard before getting into mass storage • Rather • more disks, bigger disks, mirrored disks • cache data across the network from another centre(that is willing to tolerate the stresses of mass storage management) Mass storage is person-power intensive long term costs

  20. Consider outsourcing • Massive growth in co-location centres, ISP warehouses, ASPs, storage renters, etc. • Level 3, Intel, Hot Office, Network Storage Inc, PSI, …. • There will probably be one near you • Check it out – compare costs & prices • Maybe personnel savings can be made

  21. Policies & sociology Access policy? • Collaboration-wide? or restricted access (regional, national, ….) • A rich source of unnecessary complexity Data distribution policies Analysis models • Monarc work will help to plan the centres • But the real analysis models will evolve when the data arrives Keep everything flexible – simple architecture - simple policies - minimal politics

  22. Concluding remarks I • Lots of experience with farms of inexpensive components • We need to scale them up – lots of work but we think we understand it • But we have to learn how to integrate distributed farms into a coherent analysis facility • Leverage other developments • But we need to learn through practice and experience • Retain a healthy scepticism for scalability theories • Check it all out on a realistically sized testbed

  23. Concluding remarks II • Don’t get hung up on optimising component costsDo be very careful with head-count • Personnel costs will probably dominate • Define clear objectives for the centre – • Efficiency, capacity, quality • Think hard if you really need mass storage • Discourage empires & egos • Encourage collaboration & out-sourcing • In fact – maybe we can just buy all this as an Internet service

More Related