Tiziana Ferrari End-to-End Performance with Traffic Aggregation 1

End-to-End Performancewith Traffic Aggregation

Tiziana Ferrari

Tiziana.Ferrari@cnaf.infn.it

TF-TANT Task ForceTNC 2000, Lisbon 23 May 2000

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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

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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

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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: max arrival rate < min departure rate

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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

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Test network

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Test scenario

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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

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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 a real distribution

• Scheduling: priority queuing

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Best-effort traffic pack size distribution

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Tail drop

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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

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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)

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Number of EF streams-Constant packet size (40 by of payload) and EF load (32%)-Variable number of EF streams: [1, 100] asymptotic convergence

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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

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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

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EF packet size (IPDV)

-IPDV inversely proportional to the burst size

-Tradeoff between one-way delay and IPDV

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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)

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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)

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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

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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