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Modelli di differenziazione delle prestazioni per il supporto del traffico di rete LHC

Modelli di differenziazione delle prestazioni per il supporto del traffico di rete LHC. Tiziana.Ferrari@cnaf.infn.it On behalf of: S.Arezzini, M.Bencivenni, T.Ferrari, E.Mazzoni Workshop sul Calcolo e Reti INFN – Verso la Sfida di LHC Otranto, Jun 7 2006. Problem statement.

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Modelli di differenziazione delle prestazioni per il supporto del traffico di rete LHC

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  1. Modelli di differenziazione delle prestazioni per il supporto del traffico di rete LHC Tiziana.Ferrari@cnaf.infn.it On behalf of: S.Arezzini, M.Bencivenni, T.Ferrari, E.Mazzoni Workshop sul Calcolo e Reti INFN – Verso la Sfida di LHC Otranto, Jun 7 2006 Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  2. Problem statement • T1- associated T2 sites: shared network infrastructure • Optimal network bandwidth allocation (guaranteed – BGA – and max – BEA) for INFN T2 sites, considering the bursty nature of traffic produced by analysis? How to protect legacy traffic without relying on excessing overprovisioning? • Solution: • Usage of IP traffic performance differentiation • Configuration of queues dedicated to specific traffic classes at potential network bottlenecks • Flow aggregation into classes of service via the Differentiated Services Code Point (6 bit, IP header) Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  3. What QoS model? • GEANT2 and GARR: • Support of Differentiated Services technologies! • IP Premium (low delay, packet loss probability minimized)  bandwidth allocated is a “small” percentage of the overall network interface bandwidth • Less Than Best Effort (for applications which can tolerate high istantaneous packet loss)  not for TCP-based bulk data transfer  Assurate Rate service: guaranteed minimum average bandwidth to n different classes, with spare bandwidth can be re-allocated to busy queues Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  4. Assured Rate: an example Assumption: bandwidth of link experimenting congestion: 1 Gb/s • User (legacy) traffic  flows classified and assigned to dedicated queue: • Guaranteed Bandwidth in range: [300, 1000] Mb/s • Minimum 30% of link capacity guaranteed in case of congestion • Codepoint: 000 (best-effort) • LHC traffic  flows classified and assigned to dedicated queue: • Guaranteed Bandwidth in range: [700, 1000] Mb/s • Minimum 70% of link capacity guaranteed in case of congestion • Codepoint: 001 (assured-rate) • ... And more traffic classes can be added (total max bandwidth is 100% of the link capacity on a given interface) Objectives: • Allocation of minimum guaranteed bandwidth to input/output legacy and LHC traffic classes in case of congestion • Fair distribution of link capacity in case of congestion • Possibility to get more bandwidth than the minimum guaranteed in case of spare link capacity Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  5. Testbed configuration (Dic 2005) • CNAF: • Juniper M10 (dedicated to testing) • GigaEthernet switch Extreme Summit 400 • Two end-nodes (64 bit PCI slot network interface, 1 GEthernet), connected to the Service Challenge GigaEthernet switch • Capacity to/from GARR: 2 Gb/s (boundling of two GEthernet interfaces) • PISA • Juniper M7 (production router) • Two end-nodes (64 bit PCI-X slot network interface, 1 GEthernet; 1 Fast-Ethernet interface ) • Capacity to/from GARR: 1 Gb/s Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  6. Network Layout Juniper M10 Juniper M7 GARR Users 30% 2.0 Gb/s 1 Gb/s Service Challenge Service Challenge 70% CNAF INFN Pisa Network bottlenecks Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  7. Scenario 1: differentiation of incoming traffic T1  T2 Juniper M10 Juniper M7 GARR Users 30% 1 Gb/s 2.0 Gb/s Service Challenge LHC subnet 70% CNAF Network bottleneck  Classification and Queuing here GARR rehalm INFN Pisa Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  8. Scenario 2: differentiation of outgoing traffic T2  T1 Juniper M10 Juniper M7 GARR Users 30% 2.0 Gb/s 1 Gb/s Service Challenge LHC subnet 70% CNAF Network bottleneck  Classification and Queuing here INFN T2 rehalm INFN Pisa Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  9. Scenario 3: differentiation of outgoing traffic T1  T2Example Pisa Juniper M10 GARR 20% Torino LHC subnet 20% CNAF 20% 2.0 Gb/s Other Production traffic Legnaro 20% 20% Network bottleneck  Classification and Queuing here INFN T1 rehalm Milano Bari Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  10. Scenario 4: differentiation of incoming traffic T2  T1 Pisa Juniper M10 1Gb/s Torino GARR 1Gb/s CNAF 1Gb/s Legnaro 2.0 Gb/s 1Gb/s 1Gb/s Network bottleneck  Classification and Queuing here GARR rehalm Milano Bari Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  11. Differentiation of outgoing traffic (from Pisa) Juniper M10 Juniper M7 GARR SC2 Users Best effort stream 1 BE2 1 Gb/s 30% 2.0 Gb/s Best effort stream 2 BE1 SC1 Service Challenge AR 70% AR bottleneck CNAF INFN Pisa Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  12. Test on differentiation of outgoing traffic (1/2) 29% 29% • Usage of Weighted Round Robin scheduling for per-class differentiation • Classification of outgoing traffic (via IP source/destination addresses) and • Packet marking  transparent transport needed (no code point re-writing • In transit nodes) Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

  13. Results and Conclusion • Scheduling and classification/marking: good functionality, stability and performance, easy coexistence in a production router • Testing with TCP streams more difficult (as expected) • Scenario 1 and 2 are the most interesting... But scenario 3 and 4 getting more relevant as T1 will need to handle transfers to/from other T1s and non-associated T2s in addition to traffic from CERN (currently the LHC OPN is not fully meshed) • Easy configuration on Juniper routers ... But heterogeneous network layouts require extensive testing on a number of different router platforms • Support of Assured Rate needed in Provider Edge routers at GARR for effective protection of incoming traffic at T1 and T2 • 10 GigaEthernet at T2?? • Infrastructure cost vs QoS configuration/management overhead Modelli di differenziazione delle prestazioni per il supporto del traffico LHC

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