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