Differential Congestion Notification: Taming the Elephants

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Differential Congestion NotificationTaming the
Elephants
Long Le, Jay Kikat, Kevin Jeffay, and Don
Smith Department of Computer science University
of North Carolina at Chapel Hill http//www.cs.unc
.edu/Research/dirt Published on ICNP 2004

• Presented by
• Feng Li (lif_at_cs.wpi.edu)

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Outline

• Background Router-based congestion control
• Active Queue Management (AQM)
• Explicit Congestion Notification (ECN)
• Do AQM schemes works?
• The case for differential congestion notification
  (DCN).
• A DCN prototype and its empirical evaluation.

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Router-Based Congestion ControlThe Case against
drop-tail queuing (FIFO)

• Large (full) queues in routers are bad things.
• End to end latency is dominated by the length of
  queues at switches in network.
• Allowing Queues to overflow is a bad thing
• Connections that transmit at high rates can
  starve connections that transmit at low rates.
• Causes connections to synchronize their response
  to congestion and become unnecessarily busty.

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Router-Based Congestion ControlActive Queue
Management (AQM)

• Key concept Drop packets before a queue
  overflows to signal incipient congestion to
  end-system.
• Basic mechanism When the queue length exceeds
  threshold, packets are probabilistically dropped
• Random Early Detection (RED) AQM
• Always en-queue if queue length less than a
  low-water mark
• Always drop if queue length is greater than a
  high-water mark
• probabilistically drop/en-queue if queue length
  is in between these two marks.

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The Proportional Integral (PI)Controller

• PI attempts to maintain an explicit target queue
  length.
• PI Samples instantaneous queue length at fixed
  intervals and computes a mark/drop probability at
  Kth sample
• p(KT)a x (q(kT) qref ) b x (q ((k-1) T) q
  ref) p ((k-1) T)
• a, b, and T depends on link capacity, maximum RTT
  and the number of flows at a router.

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Explicit congestion Notification Overview

• Set a bit in a packets header and forward
  towards the ultimate destination
• A receiver recognizes the marked packet and sets
  a corresponding bit in the next outgoing ACK
• When a sender receives an ACK with ECN it
  invokes a response similar to that for packet loss

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Put the piece together AQMECN

• If a RED Router detects congestion it will mark
  arriving packets.
• The router will then forward marked packets from
  ECN-Capable senders.
• and drop marked packets from all other senders.

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Do AQM Schemes work? Evaluation of AFER, PI and
REM

• The effects of Active Queue Management on Web
  Performance SIGCOMM 2003. When user response
  times are important performance metrics
• Without ECN, PI results in a modest performance
  improvement over drop tail and other AQM schemes.
• With ECN, both PI and REM provide significant
  performance improvement over drop-tail.

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Evaluation of AQM, PI and REMExperimental
results 98 Load. From SIGCOMM 2003
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Outline

• Background Router-based congestion control
• Active Queue Management (AQM)
• Explicit Congestion Notification (ECN)
• Do AQM schemes works?
• The case for differential congestion notification
  (DCN).
• A DCN prototype and its empirical evaluation.

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Discussion of ECNDisadvantages

• Claim
• ECN deployment requires the participation of both
  router and end-systems. That raises cost and
  complexity
• Firewalls and network address translators
  intentionally or unintentionally drop all ECN
  packets or clear ECN bits. Only 1.1 websites
  correctly deployed ECN in 2003.
• Conclusion
• AQM would be more appealing without ECN.

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The Structure of Web TrafficDistribution of
Response sizes (figure 1)
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The Structure of Web TrafficPercent of Bytes
transferred by response sizes (figure 2)
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DiscussionDo AQM designs inherently require ECN?

• Claim Differentiating between flows at the
  flow-level is important.
• ECN is required for good AQM performance because
  it eliminates the need for short flows (a
  significant fraction of their) data
• With ECN, short flows (mostly) no longer
  retransmit data
• But their performance is still hurt by AQM
• Why signal short flows at all?
• They have no real transmission rate to adapt
• Hence signaling these flows provides no benefit
  to the network and only hurts end-system
  performance

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Outline

• Background Router-based congestion control
• Active Queue Management (AQM)
• Explicit Congestion Notification (ECN)
• Do AQM schemes works?
• The case for differential congestion notification
  (DCN).
• A DCN prototype and its empirical evaluation.

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Realizing Differential NotificationIssues and
approach

• How to identify packets belonging to long-lived,
  high bandwidth flows with minimal state?
• Adopt the Estan Varghese flow filtering scheme
  developed for traffic accounting SIGCOMM 2002
• How to determine when to signal congestion (by
  Dropping packets)
• Use a PI-Like scheme INFOCOM 2001
• Differential treatment of Flows an old idea.
• FRED, CHOKe, AFD, RIO-PS
• SRED, SFB, RED-PD

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Classifying FlowsA score-boarding Approach

• Use two hash tables (Hash keys are formed by IP
  addressing 4-tuple plus protocol number.
• A suspect flow table HB (High Band Width) and
• A per-flow packet count table SB (score board)
• Arriving packets from flows in HB are subject to
  dropping
• Arriving packets from other flows are inserted
  into SB and tested to determine if the flow
  should be considered high bandwidth.
• Using a simple packet count threshold for this
  determination.

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Classifying FlowsA score-boarding
approach(figure3)
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An Alternate ApproachAFD Pan et al. 2003

• Approximate Fairness through Differential
  Dropping
• Sample 1 out of every s packets and store in a
  shadow buffer of size b
• Estimate Flows rate as rest R (matches/b)
• Drop packet with probability p 1- rfair/rrest

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Another Alternate ApproachRIO-PSGuo and Matta
2001

• Edge Routers maintain per-flow counters and
  classify flows into two classes short or
  long
• Core Routers
• Use different RED engines for short and long
  flows
• Use different RED parameter settings to give
  preferential treatment to short flows

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Another Alternate ApproachRIO-PS Guo and Matta
2001

• Edge Routers maintain per-flow counters and
  classify flows into two classes short or
  long
• Core Routers
• Use different RED engines for short and long
  flows
• Use different RED parameter settings to give
  preferential treatment to short flows

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Outline

• Background Router-based congestion control
• Active Queue Management (AQM)
• Explicit Congestion Notification (ECN)
• Do AQM schemes works?
• The case for differential congestion notification
  (DCN).
• A DCN prototype and its empirical evaluation.

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Evaluation MethodologySIGCOMM2003

• Evaluate AQM schemes through live simulation
• Evaluate the browsing behavior of a large
  population users surfing the web in a laboratory
  test bed.
• Construct a physical network emulating a
  congested peering link between two ISPs
• Generate synthetic HTTP requests and responses
  but transmit over real TCP/IP stacks, network
  links, and switches
• Also perform experiments with mix of TCP
  applications.

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Experimental MethodologyHTTP traffic generation

• Synthetic web traffic generated using the UNC
  HTTP model SIGMETRICS 2001, MASCOTS 2003
• Primary random variables

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Experimental MethodologyTestbed emulating an ISP
peering link

• AQM schemes implemented in FreeBSD routers using
  ALTQ kernel extensions
• End-systems either a traffic generation client or
  server
• use dummynet to provide to provide per-flow
  propagation delays
• Two-way traffic generated, equal load generated
  in each direction

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Experimental Methodology1 Gbps Network
calibration experiments

• Experiments run on a congested 100 Mbps link
• Primary simulation parameter Number of simulated
  browsing users browsing users
• Run calibration experiments on an un-congested 1
  Gbps link to relate simulated user populations to
  average link utilization
• (And to ensure offered load is linear in the
  number of simulated users -- i.e., that
  end-systems are not a bottleneck)

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Experimental Methodology1 Gbps Network
Calibration Experiments
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DCN EvaluationExperimental Plan

• Run experiments with DCN, AFD, RIO-PS, and PI at
  different offered loads
• PI always uses ECN, test AFD and RIO-PS with and
  without ECN
• DCN always signals congestion via drops
• Compare DCN results against
• The better of PI, AFD, and RIO-PS (the
  performance to beat)
• The un-congested network (the performance to
  approximate)

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Experiment Results 90 LoadDCN Performance
(figure 5)
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Experimental Results 98 LoadDCN Performance
(figure 5)
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Experimental Result 90 LoadDCN Performace
(figure 9)
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Experiment Results 98 LoadComparison of all
schemes(figure-11)
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DCN EvaluationSummary

• DCN uses a simple, tunable two-tired
  classification scheme with
• Tunable storage overhead
• O(1) Complexity with High Probability
• DCN, without ECN, meets or exceeds the
  performance of the best performing AQM designs
  with ECN
• The performance of 99 flows is improved
• More small and medium flows complete per unit
  time.
• On heavily congested networks, DCN closely
  approximates the performance achieved on an
  un-congested network

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Summary and Conclusions

• For offered loads of 90 or greater there is
  benefit to control theoretic AQM but only when
  used with ECN
• bandwidth Heuristically signaling only
  long-lived, high-bandwidth flows improves the
  performance of most flows and eliminates the
  requirement for ECN
• One can operate links carrying HTTP traffic at
  near saturation levels with performance
  approaching that achieved on an un-congested
  network
• Identification of high-bandwidth flows can be
  performed with tunable overhead and effectively
  complexity

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Experimental Results 90 LoadComparison of all
schemes (CCDF)
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Experimental Results 98 LoadComparison of all
schemes (CCDF)
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Experimental Results With General TCP
TrafficComparison of all schemes (Figure 19)
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Experimental Results With General TCP
TrafficComparison of all schemes CCDF (Figure 20)
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Reference

• Authors slides in ICNP 2004
• http//www.cs.unc.edu/jeffay/talks/ICNP-04-slides
  .pdf
• Authors slides for SIGCOMM2003
• http//www.cs.unc.edu/jeffay/talks/Penn-DCN-ECN-S
  tudy-04.pdf
• Research Group Websites
• http//www.cs.unc.edu/Research/dirt

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Differential Congestion NotificationTaming the
Elephants (IEEE ICNP 2004)
Thank you