The Effects of Active Queue The Effects of Active Queue Management on Web PerformanceManagement on Web Performance

SICOMM 2003SICOMM 2003

Long Le, Jay Aikat, Kevin Jeffay, F.Donelson SmithLong Le, Jay Aikat, Kevin Jeffay, F.Donelson Smith

29th January, 2004Presented by Sookhyun, Yang

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Contents

• IntroductionIntroduction

• Problem StatementProblem Statement

• Related WorkRelated Work

• Experimental MethodologyExperimental Methodology

• Result and AnalysisResult and Analysis

• ConclusionConclusion

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IntroductionIntroductionDrop policyDrop policy– Drop tail : Drop tail : whenwhen a queue overflows a queue overflows– Active queue management (AQM) : Active queue management (AQM) : beforebefore a queue overflows a queue overflows

Active queue management (AQM)Active queue management (AQM)– Keep the Keep the average queue size smallaverage queue size small in routers in routers– RED (Random early detection) algorithmRED (Random early detection) algorithm

Most widely studied and implementedMost widely studied and implemented

Various design issues of AQMVarious design issues of AQM– How to detect congestionHow to detect congestion– How to control for achieving a stable point for queue sizeHow to control for achieving a stable point for queue size– How congestion signal is delivered to the senderHow congestion signal is delivered to the sender

Implicitly by dropping packets at the routerImplicitly by dropping packets at the routerExplicitly by signal explicit congestion notification (ECN)Explicitly by signal explicit congestion notification (ECN)

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Problem StatementProblem Statement

GoalGoal– Compare the performance of Compare the performance of control theoretic control theoretic AQM algorithms with AQM algorithms with

original original randomized dropping paradigmsrandomized dropping paradigms

Considered AQM schemesConsidered AQM schemes– Control theoretic AQM algorithmsControl theoretic AQM algorithms

Proportional integrator (PI) controllerProportional integrator (PI) controllerRandom exponential marking (REM) controllerRandom exponential marking (REM) controller

– Original randomized dropping paradigmsOriginal randomized dropping paradigmsAdaptive random early detection (ARED) controllerAdaptive random early detection (ARED) controller

Performance metricsPerformance metrics– Link utilizationLink utilization– Loss rateLoss rate– Response time for each request/response transactionResponse time for each request/response transaction

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Contents

• IntroductionIntroduction• Problem StatementProblem Statement• Related WorkRelated Work• Experimental MethodologyExperimental Methodology

• PlatformPlatform• CalibrationCalibration• ProcedureProcedure

• Result and AnalysisResult and Analysis• AQM Experiments with Packet DropsAQM Experiments with Packet Drops• AQM Experiments with ECNAQM Experiments with ECN• DiscussionDiscussion

• ConclusionConclusion

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Random Early DetectionRandom Early Detection

Original REDOriginal RED– Measure of congestion: weighted-average queue size (Measure of congestion: weighted-average queue size (AvgQLenAvgQLen))

minth maxth

Drop packetslinearly

Dropall packetsDrop probability

AvgQLenminth maxth

1

maxp

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Random Early DetectionRandom Early Detection

Modification of the original REDModification of the original RED– Gentle modeGentle mode

– Mark or drop probability increases linearlyMark or drop probability increases linearly

minth maxth

Drop packetslinearly

Dropall packetsDrop probability

AvgQLenminth maxth

1

maxp

2 * maxth

2 * maxth

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Random Early DetectionRandom Early Detection

Weakness of REDWeakness of RED– Does not consider the number of flows sharing a bottleneck linkDoes not consider the number of flows sharing a bottleneck link– In TCP congestion control mechanismIn TCP congestion control mechanism

Packet mark or drop reduces the offered load by a factor of Packet mark or drop reduces the offered load by a factor of

Self-configuring REDSelf-configuring RED– AdjustAdjust maxmaxpp every timeevery time AvgQLen AvgQLen

AREDARED– Adaptive and gentle refinements to original REDAdaptive and gentle refinements to original RED

1- 0.5/n (n: number of flows sharing the bottleneck link)

minth maxth

multiplicative decrease additive/multiplicative increase

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Control Theoretic AQMControl Theoretic AQM

Misra Misra et al.et al.– Applied control theory to develop a model for TCP/AQM dynamicsApplied control theory to develop a model for TCP/AQM dynamics

– Used this model for analyzing REDUsed this model for analyzing RED

– Limitation of REDLimitation of REDResponse to changes in network trafficResponse to changes in network traffic

Use of a weighted average queue lengthUse of a weighted average queue length

PI controller PI controller (Hollot (Hollot et al.et al.))– Regulate queue length to target value called “queue reference” (Regulate queue length to target value called “queue reference” (qqrefref ))

– Use instantaneous samples of the queue length at a constant sampling fUse instantaneous samples of the queue length at a constant sampling frequencyrequency

– Drop probabilityDrop probability p(kT)p(kT)

((q(kT)q(kT): instantaneous sample of queue length, : instantaneous sample of queue length, T=1/sampling-frequencyT=1/sampling-frequency))

p(kT) = a * (q(kT) – qref) – b * (q((k-1)T) – qref) + p((k-1)T)

link capacity, maximum RTT, expected number of active flows

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Control Theoretic AQMControl Theoretic AQM

REM schemeREM scheme (Athuraliya (Athuraliya et alet al.).)– Periodically updates a Periodically updates a congestion measurecongestion measure called “price” called “price”– Price Price p(t)p(t)

Rate mismatch between packet arrival and departure rate at the linkRate mismatch between packet arrival and departure rate at the link

Queue difference between the actual queue length and target valueQueue difference between the actual queue length and target value

– Drop probabilityDrop probability

p(t) = max( 0, p(t-1) + γ * (α * (q(t) – qref)) + x(t) –c )

c : link capacity, q(t) : queue length, qref : target value – queue size, x(t) : packet arrival rate

prob(t) = 1 - ,where Φ >1 is a constant1Φ( )

p(t)

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Contents

• IntroductionIntroduction• Problem StatementProblem Statement• Related WorkRelated Work• Experimental MethodologyExperimental Methodology

• PlatformPlatform• CalibrationCalibration• ProcedureProcedure

• Result and AnalysisResult and Analysis• AQM Experiments with Packet DropsAQM Experiments with Packet Drops• AQM Experiments with ECNAQM Experiments with ECN• DiscussionDiscussion

• ConclusionConclusion

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PlatformPlatform

Emulate one peering link carrying web traffics between sources and Emulate one peering link carrying web traffics between sources and destinationsdestinations

…… ……

100Mbps100Mbps

EthernetSwitches

EthernetSwitches

Networkmonitor

Networkmonitor

ISP 1router

ISP 2router

1Gbps 1Gbps100/1000

Mbps

ISP1Browser/Servers

ISP2Browser/ServersIntel-based machines with FreeBSD 4.5

Web request generator (browser) : 14 machinesWeb response generator (server) : 8 machinesTotal number of flows = 44

100 Mbps Ethernet interface3Com 10/100/1000 Ethernet switches

ALTQ extensions to FreeBSD (PI, REM, ARED)1GHz Pentium Ⅲ1GB of memory1000-SX fiber gigabit Ethernet NIC100Mpbs Fast Ethernet NICs

1000-SX fiber gigabit Ethernet NIC100Mpbs Fast Ethernet NICs

UncongestedNetwork

Bottleneck1Gbps 1Gbps

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Monitoring ProgramMonitoring Program

Program 1Program 1: monitoring router interface: monitoring router interface– Effects of the AQM algorithmsEffects of the AQM algorithms– Log of queue size sampled every 10ms alongLog of queue size sampled every 10ms along

Number of entering packetsNumber of entering packetsNumber of dropped packetsNumber of dropped packets

Program 2Program 2: link-monitoring machine: link-monitoring machine– Connected to the links between the routersConnected to the links between the routers

Hubs on the 100Mbps segmentsHubs on the 100Mbps segmentsFiber splitters on the Gigabit linkFiber splitters on the Gigabit link

– Collect TCP/IP headersCollect TCP/IP headersLocally-modified version of theLocally-modified version of the tcpdump tcpdump utility utility

– Log of Log of link utilizationlink utilization

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Emulation of End-to-End LatencyEmulation of End-to-End Latency

Congestion control loop is influenced by Congestion control loop is influenced by RTTRTT

Emulate different RTTs on each TCP connection (Emulate different RTTs on each TCP connection (per-flowper-flow delay) delay)– Locally-modified version of Locally-modified version of dummynetdummynet component of FreeBSD component of FreeBSD– Add a randomly chosen minimum delay to all packets from each flowAdd a randomly chosen minimum delay to all packets from each flow

Minimum delayMinimum delay– Sampled from a discrete uniform distribution Sampled from a discrete uniform distribution – Internet RTTs within the continental U.S.Internet RTTs within the continental U.S.

RTT RTT – Flow’s minimum delayFlow’s minimum delay + additional delay (caused by queues at the route + additional delay (caused by queues at the route

rs or on the end systems)rs or on the end systems)

TCP window size = 16Kbyte on all end systems (widely used value)TCP window size = 16Kbyte on all end systems (widely used value)

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Web-Like Traffic GenerationWeb-Like Traffic GenerationModel of [13]Model of [13]– Based on empirical dataBased on empirical data

– Empirical distributions describing the Empirical distributions describing the elementselements necessary to generate necessary to generate synthetic to generate synthetic HTTP workloadssynthetic to generate synthetic HTTP workloads

Browser program and server programBrowser program and server program– Browser program logs Browser program logs response timeresponse time for each request/response pair for each request/response pair

thinkingthinkingrequesting

awebpage

requestinga

webpage

request

After random time

Server’s service time = 0

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CalibrationsCalibrationsOffered loadsOffered loads– Network traffic resulting from emulating the browsing behavior of a fixed Network traffic resulting from emulating the browsing behavior of a fixed

size population of web userssize population of web users

Three critical calibrationsThree critical calibrations before experimentsbefore experiments– Only one primary Only one primary bottleneckbottleneck

100Mbps links between two routers100Mbps links between two routers

– Predictably controlled Predictably controlled offered loadoffered load on the network on the network

– Resulting packet arrival time-series (packet counts per ms)Resulting packet arrival time-series (packet counts per ms)Long-range dependent (LRD) behavior [14]Long-range dependent (LRD) behavior [14]

Calibration experimentCalibration experiment– Configure the network connecting the routers at 1GbpsConfigure the network connecting the routers at 1Gbps

– Drop-tail queues having 2400 queue elementsDrop-tail queues having 2400 queue elements

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CalibrationsCalibrations

One direction of the 1Gbps linkOne direction of the 1Gbps link

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CalibrationsCalibrations

Heavy-tailed distributionfor both user “think” time and response size [13]Heavy-tailed distributionfor both user “think” time and response size [13]

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ProcedureProcedure

Experimental settingExperimental setting– Offered loads by user populationsOffered loads by user populations

80%, 90%, 98%, or 105% of the capacity of the 100Mbps link80%, 90%, 98%, or 105% of the capacity of the 100Mbps link

– Run for 120 min over 10,000,000 request/response exchangesRun for 120 min over 10,000,000 request/response exchangesCollect data during 90min intervalCollect data during 90min interval

– Repeat three times for each AQM schemes PI, REM, AREDRepeat three times for each AQM schemes PI, REM, ARED

Experimental focusExperimental focus– End-to-end End-to-end response timeresponse time for each request/response pair for each request/response pair– Loss rateLoss rate : fraction of IP datagram dropped at the link queue : fraction of IP datagram dropped at the link queue– Link utilizationLink utilization on the bottleneck link on the bottleneck link– Number of request/response exchanges Number of request/response exchanges completedcompleted

Contents

• IntroductionIntroduction• Problem StatementProblem Statement• Related WorkRelated Work• Experimental MethodologyExperimental Methodology

• PlatformPlatform• CalibrationCalibration• ProcedureProcedure

• Result and AnalysisResult and Analysis• AQM Experiments with Packet DropsAQM Experiments with Packet Drops• AQM Experiments with ECNAQM Experiments with ECN• DiscussionDiscussion

• ConclusionConclusion

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AQM Experiments with Packet DropsAQM Experiments with Packet Drops

Two target queue lengthTwo target queue length of PI, REM, and ARED of PI, REM, and ARED– Tradeoff between link utilization and queuing delayTradeoff between link utilization and queuing delay

24 packets for minimum latency24 packets for minimum latency240 packets for high link utilization240 packets for high link utilizationRecommended in [1,6,8]Recommended in [1,6,8]

– Set the maximum queue size sfficient to ensure drop-tail do not Set the maximum queue size sfficient to ensure drop-tail do not occuroccur

BaselineBaseline– Conventional drop-tail FIFO queuesConventional drop-tail FIFO queues– Queue size for drop-tailQueue size for drop-tail

24, 240 packets : comparing with AQM schemes24, 240 packets : comparing with AQM schemes2400 packets : recently favorable buffering equivalent to 100ms at t2400 packets : recently favorable buffering equivalent to 100ms at the link’s transmission speed (from mailing list)he link’s transmission speed (from mailing list)

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Queue Size for Drop-TailQueue Size for Drop-Tail

Drop-tail queue size = 240Drop-tail queue size = 240

Drop-Tail Performance

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Response Time at 80% LoadResponse Time at 80% LoadAQM Experiments with Packet Drops

AREM show some degradation relative to the resultson the un-congested link at 80% load

AREM show some degradation relative to the resultson the un-congested link at 80% load

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Response Time at 90% LoadResponse Time at 90% LoadAQM Experiments with Packet Drops

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Response Time at 98% LoadResponse Time at 98% LoadAQM Experiments with Packet Drops

No AQM scheme can offset the performance degradation at 98% loadNo AQM scheme can offset the performance degradation at 98% load

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Response Time at 105% LoadResponse Time at 105% LoadAQM Experiments with Packet Drops

All schemes degrades uniformly from the 98% caseAll schemes degrades uniformly from the 98% case

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AQM Experiments with ECNAQM Experiments with ECN

Explicitly signal congestion to end-systems with an ECN bitExplicitly signal congestion to end-systems with an ECN bit

Procedure of signal congestion with ECNProcedure of signal congestion with ECN– [Router] : mark a ECN bit in the TCP/IP header of the packet[Router] : mark a ECN bit in the TCP/IP header of the packet

– [Receiver] : mark TCP header of the next outbound segment (typically [Receiver] : mark TCP header of the next outbound segment (typically an ACK) destined for sender of original marked segmentan ACK) destined for sender of original marked segment

– [Original sender][Original sender]React as if a single segment had been lost within a send windowReact as if a single segment had been lost within a send window

Mark the next outbound segment to confirm that it reacted to the congestionMark the next outbound segment to confirm that it reacted to the congestion

ECN has no effect on response time of PI, REM, and ARED up to ECN has no effect on response time of PI, REM, and ARED up to 80% offered load80% offered load

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Response Time at 90% Load Response Time at 90% Load AQM Experiments with ECN

Both PI and REM provide response time performancethat is both close to that on un-congested link

Both PI and REM provide response time performancethat is both close to that on un-congested link

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Response Time at 98% LoadResponse Time at 98% LoadAQM Experiments with ECN

Degradation, but far superior to Drop tailDegradation, but far superior to Drop tail

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Response Time at 105% LoadResponse Time at 105% LoadAQM Experiments with ECN

REM shows the most significant improvementin performance with ECN

REM shows the most significant improvementin performance with ECN

ECN has very little effect on the performance Of ARED

ECN has very little effect on the performance Of ARED

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Loss ratio/Completed requests/Link utilizationLoss ratio/Completed requests/Link utilization

AQM Experiments with Packet Drops or with ECN

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SummarySummaryFor 80% loadFor 80% load– No AQM scheme provides better response time performance than No AQM scheme provides better response time performance than

simple drop-tail FIFO queue managementsimple drop-tail FIFO queue management– Not changed by the AQM schemes with ECNNot changed by the AQM schemes with ECN

For 90% load or greater without ECNFor 90% load or greater without ECN– PI is better than drop-tail and other AQM schemes without ECNPI is better than drop-tail and other AQM schemes without ECN

With ECNWith ECN– Both PI and REM provide significant response time improvementBoth PI and REM provide significant response time improvement

ARED with recommended parameter settingsARED with recommended parameter settings– poorest response time performancepoorest response time performance– Lowest link utilizationLowest link utilization– Not changed with ECNNot changed with ECN

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DiscussionDiscussion

Positive Impact of ECNPositive Impact of ECN– Response time performance under PI and REM with ECN at loads of Response time performance under PI and REM with ECN at loads of

90% and 98%90% and 98%

– 90% load: approximately achieved on an un-congested network90% load: approximately achieved on an un-congested network

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DiscussionDiscussion

Performance gap between PI and REM with packet Performance gap between PI and REM with packet dropping was closed through the addition of ECNdropping was closed through the addition of ECN

Difference in performance between ARED and the other Difference in performance between ARED and the other AQM schemesAQM schemes– PI and REM operate in “byte mode” in default, but ARED in PI and REM operate in “byte mode” in default, but ARED in

“packet mode”“packet mode”– Gentle mode in REMGentle mode in REM– PI and REM periodically sample the queue length when deciding PI and REM periodically sample the queue length when deciding

to mark packets, but ARED uses a weighted averageto mark packets, but ARED uses a weighted average

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Contents

• IntroductionIntroduction• Problem StatementProblem Statement• Related WorkRelated Work• Experimental MethodologyExperimental Methodology

• PlatformPlatform• CalibrationCalibration• ProcedureProcedure

• Result and AnalysisResult and Analysis• AQM Experiments with Packet DropsAQM Experiments with Packet Drops• AQM Experiments with ECNAQM Experiments with ECN• SummarySummary

• ConclusionConclusion

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ConclusionConclusion

Unlike a similar earlier negative study on the use of Unlike a similar earlier negative study on the use of AQM, the AQM scheme AQM, the AQM scheme with ECNwith ECN can be realized in can be realized in practicepractice

Limitation of this paperLimitation of this paper– Comparison between only two classes of algorithmsComparison between only two classes of algorithms

Control theoretic principlesControl theoretic principlesOriginal randomized dropping paradigmOriginal randomized dropping paradigm

– Studied a link carrying only web-like trafficStudied a link carrying only web-like trafficMore realistic mixed of HTTP, other TCP traffic, and UDP trafficMore realistic mixed of HTTP, other TCP traffic, and UDP traffic