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23/4/19 CS577(Spring 2005) 1
Differential Congestion Notification:Taming the Elephants
Presented byFeng Li ([email protected])
Long Le, Jay Kikat, Kevin Jeffay, and Don SmithDepartment of Computer science
University of North Carolina at Chapel Hillhttp://www.cs.unc.edu/Research/dirt
Published on ICNP 2004
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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 packet’s 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 :AQM+ECN
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 Flow’s rate as rest = R * (#matches/b)
Drop packet with probability p= 1- rfair/rrest
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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
Another Alternate ApproachRIO-PS[Guo and Matta 2001]
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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 Methodology[SIGCOMM2003]
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-
Study-04.pdf
Research Group Websites
– http://www.cs.unc.edu/Research/dirt
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Differential Congestion Notification:Taming the Elephants (IEEE ICNP 2004)