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End-to-End Performance with Traffic Aggregation

End-to-End Performance with Traffic Aggregation. Tiziana Ferrari Tiziana.Ferrari@cnaf.infn.it TF-TANT Task Force TNC 2000, Lisbon 23 May 2000. Overview. Diffserv and aggregation EF: Arrival and departure rate configuration Test scenario Metrics End-to-end performance (PQ): EF load

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End-to-End Performance with Traffic Aggregation

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  1. End-to-End Performancewith Traffic Aggregation Tiziana Ferrari Tiziana.Ferrari@cnaf.infn.it TF-TANT Task Force TNC 2000, Lisbon 23 May 2000 End-to-End Performance with Traffic Aggregation

  2. Overview • Diffserv and aggregation • EF: Arrival and departure rate configuration • Test scenario • Metrics • End-to-end performance (PQ): • EF load • Number of EF streams • EF packet size • WFQ and PQ • Conclusions End-to-End Performance with Traffic Aggregation

  3. Problem statement • Support of end-to-end Quality of Service (QoS) for mission-critical applications in IP networks • Solutions: • Per-flow  the Integrated Services architecture • Signalling (RSVP) • Per-class  the Differentiated Services • Classification and marking (QoS policies) • Scheduling • Traffic conditioning (policing and shaping) • DSCP • Aggregation • Expedited Forwarding and Assured Forwarding End-to-End Performance with Traffic Aggregation

  4. Aggregation • Benefit: greater scalability, no protocol overhead • Problem: interaction between flows multiplexed in the same class • Jitter: distortion of per-flow inter-packet gap • One-way delay: queuing delay due to non-empty queues • Requirement: maxarrival rate < mindeparture rate End-to-End Performance with Traffic Aggregation

  5. Arrival and departure rate configuration • Maximum arrival rate is proportional to the number of input traffic bundles • One-way delay: maximum queuing delay depends on the number of EF streams and can be arbitrarily large: Del = txMTU + n with priority queuing where n is the number of input streams  Experiments of aggregation without shaping and policing MTU Dep_rate End-to-End Performance with Traffic Aggregation

  6. Test network End-to-End Performance with Traffic Aggregation

  7. Test scenario End-to-End Performance with Traffic Aggregation

  8. Metrics • One-way delay (RFC 2679): difference between the wire time at which the last byte of a packet arrives at destination and the wire time at which the first byte is sent out (absolute value) • Jitter (Instantaneous Packet Delay Variation): for two consequent packets i and i-1 IPDV = | Di – Di-1 | • Max Burstiness: minimum queue length at which no tail drop occurs • Packet loss percentage End-to-End Performance with Traffic Aggregation

  9. Traffic profile • Expedited Forwarding: • SmartBits 200, UDP, CBR • UDP CBR streams injected from each site • Background traffic: • UDP, CBR • Permanent congestion in each hop • Packet size according to areal distribution • Scheduling: priority queuing End-to-End Performance with Traffic Aggregation

  10. Best-effort traffic pack size distribution End-to-End Performance with Traffic Aggregation

  11. Tail drop End-to-End Performance with Traffic Aggregation

  12. EF load -Constant packet size (40 by of payload) and number of streams (40) -Variable EF load: [10, 50]% -delay unit: 108.14 msec  burstiness is a linear function of the number of pack/sec End-to-End Performance with Traffic Aggregation

  13. EF load (2) One-way delay: both average and distribution almost independent of the EF rate IPDV distribution: moderate improvement with load (tx unit: transmission time of 1 EF packet, 0.424 msec) End-to-End Performance with Traffic Aggregation

  14. Number of EF streams -Constant packet size (40 by of payload) and EF load (32%) -Variable number of EF streams: [1, 100]  asymptotic convergence End-to-End Performance with Traffic Aggregation

  15. EF packet size -Constant number of streams (40) and EF load (32%) -Variable EF frame size: 40, 80, 120, 240 bytes (variable pack/sec rate) -delay unit: 113.89 msec  moderate increase in burstiness [1632, 1876] bytes delay increase, IPDV decrease End-to-End Performance with Traffic Aggregation

  16. EF packet size (delay) • -large packet size  smaller packet rate, different composition of • the TX queue and the corresponding time needed to • empty the queue increases • e.g. • 240 bytes: 240 pack/sec  TX queue = BEBEB • queuing time = 16.2 msec 40 bytes: 720 pack/sec  TX queue = BEEEB  queueing time = 11.747 msec The longer the transmission queue, the larger the effect of the pack/sec rate End-to-End Performance with Traffic Aggregation

  17. EF packet size (IPDV) • IPDV inversely proportional to the burst size • Tradeoff between one-way delay and IPDV End-to-End Performance with Traffic Aggregation

  18. WFQ and PQ: comparison • Constant number of streams (40) • Variable EF frame size: 40, 512 bytes and variable rate: [10, 50]% • WFQ is less burstiness prone (interelaving of BE and EF) End-to-End Performance with Traffic Aggregation

  19. Conclusions and future work • Aggregation produces packet loss due to packet clustering and consequent tail drop • Load: • primary factor, great burstiness, minor effect on one-way delay • Rate (pack/sec): great effect on one-way delay • number of EF streams: small dependency • Tradeoff: shaping (in few key aggregation points) and queue size tuning • EF-based services: viable, validation needed (future work) End-to-End Performance with Traffic Aggregation

  20. References • http://www.cnaf.infn.it/˜ferrari/tfng/ds/ • http://www.cnaf.infn.it/˜ferrari/tfng/qosmon/ • Report of activities (phase 2) http://www.cnaf.infn.it/˜ferrari/tfng/ds/rep2-del.doc • Priority Queuing Applied to Expedited Forwarding: a Measurement-Based Analysis, T. Ferrari, G. Pau, C. Raffaelli, Mar 2000 http://www.cnaf.infn.it/˜ferrari/tfng/ds/pqEFperf.pdf • A Measurement-based Analysis of Expedited Forwarding PHB Mechanisms, T. Ferrari, P. Chimento, Feb 2000, IWQoS 2000 , in print http://www.cnaf.infn.it/˜ferrari/tfng/ds/iwqos2ktftant.doc End-to-End Performance with Traffic Aggregation

  21. Overview of diffserv experiments • Policing: Single- and multi-parameter token buckets with TCP traffic • traffic metering and packet marking (PHB class selectors) • scheduling: WFQ, SCFQ, PQ • capacity allocation between queues, class isolation • queue dimensioning (buffer depth and TCP burst tolerance, tx queue) • per-class service rate configuration • one-way delay and instantaneous packet delay variation • Assured Forwarding: PHB differentiation through WRED • throughput performance : • packet drop probability, number of TCP streams per AF PHB, minimum threshold • Expedited Forwarding: • multiple congestion points • multiple EF aggregation points • variable load, number of streams and packet size End-to-End Performance with Traffic Aggregation

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