opnet full presentation

Copyright © 2002 OPNET Technologies, Inc. 1

Traffic Behavior and Queuing in a QoS Environment

Session 1813Traffic Behavior and Queuing in a QoS EnvironmentNetworking Tutorials

Prof. Dimitri P. BertsekasDepartment of Electrical EngineeringM.I.T.



Objectives• Provide some basic understanding of queuing phenomena• Explain the available solution approaches and associated

trade-offs• Give guidelines on how to match applications and solutions



Outline• Basic concepts• Source models• Service models (demo)• Single-queue systems • Priority/shared service systems • Networks of queues• Hybrid simulation (demo)



Outline• Basic concepts

– Performance measures– Solution methodologies– Queuing system concepts– Stability and steady-state– Causes of delay and bottlenecks

• Source models• Service models (demo)• Single-queue systems • Priority/shared service systems • Networks of queues• Hybrid simulation (demo)



Performance Measures• Delay• Delay variation (jitter)• Packet loss • Efficient sharing of bandwidth• Relative importance depends on traffic type (audio/video,

file transfer, interactive)• Challenge: Provide adequate performance for (possibly)

heterogeneous traffic



Solution Methodologies• Analytical results (formulas)

– Pros: Quick answers, insight– Cons: Often inaccurate or inapplicable

• Explicit simulation– Pros: Accurate and realistic models, broad applicability– Cons: Can be slow

• Hybrid simulation– Intermediate solution approach– Combines advantages and disadvantages of analysis and simulation



Examples of Applications Analytical Modeling Discrete-Event Simulation

M/G/./. & G/G/./. FIFO

Analysis

M/G/./. & G/G/./. Priority

Analysis

Decomposition with Kleinrock Independence Assumption

DES only with Explicit Traffic

Hybrid DES with Explicit

and Background

Traffic Single Link with FIFO Service

Best Effort Service for Standard Data Traffic Yes N/A N/A Yes Yes

Best Effort Service for LRD/Self-Similar Behavior Traffic

Yes N/A N/A Yes Yes

"Chancing It" with Best Effort Service for Voice, Video and Data

Yes N/A N/A Yes Yes

Single Link with QoS-Based QueueingUsing QoS to differentiate service levels for the same type of traffic

N/AYes (loss of

accuracy) N/A Yes Yes

Using QoS to support different requirements for different application types given as a detailed study of setting Cisco Router queueing parameters

N/AHighly

approximateN/A Yes Yes

Network of Queues

General network model extending the previous QoS queueing model

N/AHop-by-hop

Analysis (loss of accuacy)

Yes (some loss of accuracy - e.g., traffic

shaping)

Yes (Run time a function of network

complexity)

Yes [Fast with minimal loss of

accuracy]

Reduction of the general model to a representative end-to-end path

N/AHop-by-hop


N/AYes (Run time a

function of network complexity)


accuracy]

Analysis Scenarios



Queuing System Concepts: Arrival Rate, Occupancy, Time in the System• Queuing system

– Data network where packets arrive, wait in various queues, receive service at various points, and exit after some time

• Arrival rate– Long-term number of arrivals per unit time

• Occupancy– Number of packets in the system (averaged over a long time)

• Time in the system (delay)– Time from packet entry to exit (averaged over many packets)



Stability and Steady-State• A single queue system is stable if

packet arrival rate < system transmission capacity

• For a single queue, the ratiopacket arrival rate / system transmission capacity

is called the utilization factor– Describes the loading of a queue

• In an unstable system packets accumulate in various queues and/or get dropped

• For unstable systems with large buffers some packet delays become very large– Flow/admission control may be used to limit the packet arrival rate– Prioritization of flows keeps delays bounded for the important traffic

• Stable systems with time-stationary arrival traffic approach a steady-state



Little’s Law• For a given arrival rate, the time in the system is proportional

to packet occupancyN = Twhere N: average # of packets in the system

: packet arrival rate (packets per unit time) T: average delay (time in the system) per packet

• Examples:– On rainy days, streets and highways are more crowded– Fast food restaurants need a smaller dining room than regular

restaurants with the same customer arrival rate– Large buffering together with large arrival rate cause large delays



Explanation of Little’s Law• Amusement park analogy: people arrive, spend time at

various sites, and leave• They pay $1 per unit time in the park• The rate at which the park earns is $N per unit time (N:

average # of people in the park)• The rate at which people pay is $T per unit time (:

traffic arrival rate, T: time per person)• Over a long horizon:

Rate of park earnings = Rate of people’s paymentor N = T



Delay is Caused by Packet Interference• If arrivals are regular or sufficiently spaced apart, no queuing

delay occurs

Regular Traffic

Irregular but Spaced Apart Traffic

TimeArrival TimesDeparture Times13421342

TimeArrival TimesDeparture Times13421342



Burstiness Causes Interference• Note that the departures are less bursty

TimeQueuing DelaysBursty Traffic12341234



Burstiness ExampleDifferent Burstiness Levels at Same Packet Rate

Source: Fei Xue and S. J. Ben Yoo, UCDavis, “On the Generation and Shaping Self-similar Traffic in Optical Packet-switched Networks”, OPNETWORK 2002



Packet Length Variation Causes Interference

Regular arrivals, irregular packet lengths

TimeQueuing Delays



High Utilization Exacerbates Interference

As the work arrival rate: (packet arrival rate * packet length)

increases, the opportunity for interference increases

TimeQueuing Delays



Bottlenecks• Types of bottlenecks

– At access points (flow control, prioritization, QoS enforcement needed)– At points within the network core– Isolated (can be analyzed in isolation)– Interrelated (network or chain analysis needed)

• Bottlenecks result from overloads caused by: – High load sessions, or – Convergence of sufficient number of moderate load sessions at the

same queue



Bottlenecks Cause Shaping

• The departure traffic from a bottleneck is more regular than the arrival traffic

• The inter-departure time between two packets is at least as large as the transmission time of the 2nd packet

Time



Bottlenecks Cause Shaping

Bottleneck 90% utilization

Outgoing trafficIncoming traffic

Exponentialinter-arrivals

gap



Bottleneck 90% utilization

Outgoing trafficIncoming traffic

Large

Medium

Small



Packet Trains

Inter-departure times for small packets



Variable packet sizesHistogram of inter-departure times for small packets

sec

# of packets

Peaks smeared

Variable packet sizes

Constant packet sizes



Outline• Basic concepts• Source models

– Poisson traffic– Batch arrivals– Example applications – voice, video, file transfer

• Service models (demo)• Single-queue systems • Priority/shared service systems• Networks of queues• Hybrid simulation (demo)



Poisson Process with Rate

• Interarrival times are independent and exponentially distributed

• Models well the accumulated traffic of many independent sources

• The average interarrival time is 1/ (secs/packet), so is the arrival rate (packets/sec)

TimeInterarrival Times



Batch Arrivals• Some sources transmit in packet bursts • May be better modeled by a batch arrival process (e.g., bursts

of packets arriving according to a Poisson process) • The case for a batch model is weaker at queues after the first,

because of shaping

TimeInterarrival Times



Markov Modulated Rate Process (MMRP)

• Extension: Models with more than two states

Stay in each state an exponentially distributed time, Transmit according to different model (e.g., Poisson, deterministic, etc) at each state

State 0 State 1

OFF ON



Source Types• Voice sources• Video sources• File transfers• Web traffic• Interactive traffic• Different application types have different QoS requirements,

e.g., delay, jitter, loss, throughput, etc.



Source Type Properties

Characteristics QoS Requirements

Model

Voice * Alternating talk- spurts and silence intervals. * Talk-spurts produce constant packet-rate traffic

Delay < ~150 msJitter < ~30 msPacket loss < ~1%

* Two-state (on-off) Markov Modulated Rate Process (MMRP)* Exponentially distributed time at each state

Video * Highly bursty traffic (when encoded)* Long range dependencies

Delay < ~ 400 msJitter < ~ 30 msPacket loss < ~1%

K-state (on-off) Markov Modulated Rate Process (MMRP)

InteractiveFTPtelnetweb

* Poisson type * Sometimes batch- arrivals, or bursty, or sometimes on-off

Zero or near-sero packet loss Delay may be important

Poisson, Poisson with batch arrivals, Two-state MMRP



Typical Voice Source Behavior



MPEG1 Video Source Model

Diagram Source: Mark W. Garrett and Walter Willinger, “Analysis, Modeling, and Generation of Self-Similar VBR Video Traffic, BELLCORE, 1994

• The MPEG1 MMRP model can be extremely bursty, and has “long range dependency” behavior due to the deterministic frame sequence



Outline• Basic concepts• Source models• Service models

– Single vs. multiple-servers– FIFO, priority, and shared servers– Demo

• Single-queue systems • Priority/shared service systems • Networks of queues• Hybrid simulation (demo)



Device Queuing Mechanisms• Common queue examples for IP routers

– FIFO: First In First Out– PQ: Priority Queuing– WFQ: Weighted Fair Queuing– Combinations of the above

• Service types from a queuing theory standpoint– Single server (one queue - one transmission line)– Multiple server (one queue - several transmission lines)– Priority server (several queues with hard priorities - one transmission

line)– Shared server (several queues with soft priorities - one transmission

line)



Single Server FIFO• Single transmission line serving packets on a FIFO (First-In-

First-Out) basis• Each packet must wait for all packets found in the system to

complete transmission, before starting transmission– Departure Time = Arrival Time + Workload Found in the System +

Transmission time

• Packets arriving to a full buffer are dropped

ArrivalsTransmissionLine



FIFO Queue• Packets are placed on outbound link to egress device in FIFO order

– Device (router, switch) multiplexes different flows arriving on various ingress ports onto an output buffer forming a FIFO queue



Multiple Servers• Multiple packets are transmitted simultaneously on multiple

lines/servers• Head of the line service: packets wait in a FIFO queue, and

when a server becomes free, the first packet goes into service

ArrivalsTransmissionLines



Priority Servers• Packets form priority classes (each may have several flows)• There is a separate FIFO queue for each priority class• Packets of lower priority start transmission only if no higher

priority packet is waiting• Priority types:

– Non-preemptive (high priority packet must wait for a lower priority packet found under transmission upon arrival)

– Preemptive (high priority packet does not have to wait …)Class 1 Arrivals

High PriorityTransmission

LineClass 3 Arrivals

Low PriorityClass 2 ArrivalsInterm. Priority



Priority Queuing• Packets are classified into separate queues

– E.g., based on source/destination IP address, source/destination TCP port, etc.• All packets in a higher priority queue are served before a lower priority

queue is served– Typically in routers, if a higher priority packet arrives while a lower priority

packet is being transmitted, it waits until the lower priority packet completes



Shared Servers• Again we have multiple classes/queues, but they are served

with a “soft” priority scheme• Round-robin• Weighted fair queuing

Class 1 ArrivalsWeight 10

TransmissionLine





Round-Robin/Cyclic Service• Round-robin serves each queue in sequence

– A queue that is empty is skipped– Each queue when served may have limited service (at most k packets

transmitted with k = 1 or k > 1)

• Round-robin is fair for all queues (as long as some queues do not have longer packets than others)

• Round-robin cannot be used to enforce bandwidth allocation among the queues.



Fair Queuing• This scheduling method is inspired by the “most fair” of methods:

– Transmit one bit from each queue in cyclic order (bit-by-bit round robin)– Skip queues that are empty

• To approximate the bit-by-bit processing behavior, for each packet– We calculate upon arrival its “finish time under bit-by-bit round robin”

assuming all other queues are continuously busy, and we transmit by FIFO within each queue

– Transmit next the packet with the minimum finish time• Important properties:

– Priority is given to short packets– Equal bandwidth is allocated to all queues that are continuously busy

Finish Time of Packet ii-1Arrival timesDeparture timesiii-1Finish Time of Packet ii-1Arrival timesDeparture timesiii-1



Weighted Fair Queuing• Fair queuing cannot be used to implement bandwidth allocation and soft

priorities• Weighted fair queuing is a variation that corrects this deficiency

– Let wk be the weight of the kth queue– Think of round-robin with queue k transmitting wk bits upon its turn– If all queues have always something to send, the kth queue receives bandwidth

equal to a fraction wk / i wi of the total bandwidth• Fair queuing corresponds to wk = 1• Priority queuing corresponds to the weights being very high as we move to

higher priorities • Again, to deal with the segmentation problem, we approximate as follows:

For each packet:– We calculate its “finish time” (under the weighted bit-by-bit round robin

scheme)– We next transmit the packet with the minimum finish time



Weighted Fair Queuing IllustrationWeights:Queue 1 = 3Queue 2 = 1Queue 3 = 1



Combination of Several Queuing Schemes• Example – voice (PQ), guaranteed b/w (WFQ), Best Effort (Cisco’s LLQ implementation)



Demo: FIFO

FIFOBottleneck 90% utilization



Demo: FIFO Queuing Delay

Applications have different requirements

• Video» delay, jitter

• FTP» packet loss

Control beyond “best effort” needed

• Priority Queuing (PQ)• Weighted Fair Queuing (WFQ)



Demo: Priority Queuing (PQ)

PQBottleneck 90% utilization



Demo: PQ Queuing Delays

FIFO

PQ Video

PQ FTP



Demo: Weighted Fair Queuing (WFQ)

WFQBottleneck 90% utilization



Demo: WFQ Queuing Delays

FIFO

WFQ/PQ Video

PQ FTP

WFQ FTP



Queuing: Take Away Points• Choice of queuing mechanism can have a profound effect on

performance• To achieve desired service differentiation, appropriate queuing

mechanisms can be used• Complex queuing mechanisms may require simulation

techniques to analyze behavior • Improper configuration (e.g., queuing mechanism selection or

weights) may impact performance of low priority traffic



Outline• Basic concepts• Source models• Service models (demo)• Single-queue systems

– M/M/1……M/M/m/k– M/G/1……G/G/1– Demo: Analytics vs. simulation

• Priority/shared service systems• Networks of queues• Hybrid simulation (demo)



M/M/1 System• Nomenclature: M stands for “Memoryless” (a property of the

exponential distribution)– M/M/1 stands for Poisson arrival process (which is memoryless) – M/M/1 stands for exponentially distributed transmission times

• Assumptions:– Arrival process is Poisson with rate packets/sec– Packet transmission times are exponentially distributed with mean 1/– One server– Independent interarrival times and packet transmission times

• Transmission time is proportional to packet length• Note 1/ is secs/packet so is packets/sec (packet

transmission rate of the queue)• Utilization factor: = /stable system if 1)



Delay Calculation• Let

Q = Average time spent waiting in queueT = Average packet delay (transmission plus queuing)

• Note that T = 1/ + Q• Also by Little’s law

N = T and Nq = Q where

Nq = Average number waiting in queue• These quantities can be calculated with formulas derived by

Markov chain analysis (see references)



• The analysis gives the steady-state probabilities of number of packets in queue or transmission

• P{n packets} = n(1-) where = /• From this we can get the averages:

N = /(1 - )T = N/ = /(1 - ) = 1/( - )

N10λTμ01/μ

M/M/1 Results



Example: How Delay Scales with Bandwidth• Occupancy and delay formulas

N = /(1 - ) T = 1/( - ) = / • Assume:

– Traffic arrival rate is doubled– System transmission capacity is doubled

• Then:– Queue sizes stay at the same level ( stays the same)– Packet delay is cut in half ( and are doubled

• A conclusion: In high speed networks– propagation delay increases in importance relative to delay – buffer size and packet loss may still be a problem



M/M/m, M/M/ System• Same as M/M/1, but it has m (or ) servers• In M/M/m, the packet at the head of the queue moves

to service when a server becomes free• Qualitative result

– Delay increases to as= /mapproaches 1 • There are analytical formulas for the occupancy

probabilities and average delay of these systems



Finite Buffer Systems: M/M/m/k• The M/M/m/k system

– Same as M/M/m, but there is buffer space for at most k packets. Packets arriving at a full buffer are dropped

• Formulas for average delay, steady-state occupancy probabilities, and loss probability

• The M/M/m/m system is used widely to size telephone or circuit switching systems



Characteristics of M/M/. Systems• Advantage: Simple analytical formulas• Disadvantages:

– The Poisson assumption may be violated– The exponential transmission time distribution is an

approximation at best – Interarrival and packet transmission times may be

dependent (particularly in the network core)– Head-of-the-line assumption precludes heterogeneous input

traffic with priorities (hard or soft)



M/G/1 System• Same as M/M/1 but the packet transmission time

distribution is general, with given mean 1/ and variance 2

• Utilization factor = /• Pollaczek-Kinchine formula for

Average time in queue = (2 + 1/2)/2(1- )Average delay = 1/ + (2 + 1/2)/2(1- )

• The formulas for the steady-state occupancy probabilities are more complicated

• Insight: As 2 increases, delay increases



G/G/1 System• Same as M/G/1 but now the packet interarrival time

distribution is also general, with mean and variance 2

• We still assume FIFO and independent interarrival times and packet transmission times

• Heavy traffic approximation:Average time in queue ~ (2 + 2)/2(1- )

• Becomes increasingly accurate as



Demo: M/G/1

Packet inter-arrival timesexponential (0.02) sec

Capacity1 Mbps

Packet size 1250 bytes(10000 bits)

Packet size distribution:exponential

constantlognormal

What is the average delay and queue size ?



Demo: M/G/1 Analytical Results

Packet Size Distribution Delay T (sec) Queue Size (packets)

Exponentialmean = 10000

variance = 1.0 *108 0.02 1.0

Constantmean = 10000

variance = N/A 0.015 0.75

Lognormalmean = 10000

variance = 9.0 *108

0.06 3.0



Demo: M/G/1 Simulation Results

Average Delay (sec) Average Queue Size (packets)



Demo: M/G/1 LimitationsApplication traffic mix not memoryless

• Video » constant packet inter-arrivals

• Http» bursty traffic

Delay

P-K formula

Simulation



Outline• Basic concepts• Source models• Service models (demo)• Single-queue systems • Priority/shared service systems

– Preemptive vs. non-preemptive– Cyclic, WFQ, PQ systems– Demo: Simulation results

• Networks of queues• Hybrid simulation (demo)



Non-preemptive Priority Systems• We distinguish between different classes of traffic (flows)• Non-preemptive priority: packet under transmission is not

preempted by a packet of higher priority• P-K formula for delay generalizes

Class 1 ArrivalsHigh Priority

TransmissionLine

Class 3 ArrivalsLow Priority

Class 2 ArrivalsInterm. Priority



Cyclic Service Systems• Multiple flows, each with its own queue• Fair system: Each flow gets access to the transmission line in

turn• Several possible assumptions about how many packets each

flow can transmit when it gets access• Formulas for delay under M/G/1 type assumptions are

available

Class 1ArrivalsTransmission

LineClass 3ArrivalsClass 2Arrivals



Weighted Fair Queuing• A combination of priority and cyclic service• No exact analytical formulas are available



Outline• Basic concepts• Source models• Service models (demo)• Single-queue systems • Priority/shared service systems• Networks of queues

– Violation of M/M/. assumptions– Effects on delays and traffic shaping– Analytical approximations

• Hybrid simulation (demo)



Two Queues in Series• First queue shapes the traffic into second queue• Arrival times and packet lengths are correlated• M/M/1 and M/G/1 formulas yield significant error for second

queueTimeFirst QueueTimeSecond Queue



Two bottlenecks in series

Bottleneck

Exponentialinter-arrivals

Bottleneck

No queuing delayDelay



Approximations• Kleinrock independence approximation

– Perform a delay calculation in each queue independently of other queues

– Add the results (including propagation delay)

• Note: In the preceding example, the Kleinrock independence approximation overestimates the queuing delay by 100%

• Tends to be more accurate in networks with “lots of traffic mixing”, e.g., nodes serving many relatively small flows from several different locations



Outline• Basic concepts• Source models• Service models (demo)• Single-queue systems • Priority/shared service systems• Networks of queues• Hybrid simulation

– Explicit vs. aggregated traffic– Conceptual Framework– Demo: PQ and WFQ with aggregated traffic



Basic Concepts of Hybrid Simulation• Aims to combine the best of analytical results and simulation• Achieve significant gain in simulation speed with little loss of

accuracy• Divides the traffic through a node into explicit and

background– Explicit traffic is simulated accurately– Background traffic is aggregated

• The interaction of explicit and background is modeled either analytically or through a “fast” simulation (or a combination)

ExplicitBackgroundBackgroundBackgroundBackground



Explicit Traffic• Modeled in detail, including the effects of various protocols• Each packet’s arrival and departure times are recorded (together

with other data of interest, e.g., loss, etc.) along each link that it traverses

• Departure times at a link are the arrival times at the next link (plus propagation delay)

• Objective: At each link, given the arrival times (and the packet lengths), determine the departure times

Timea1a2a3a4d1d4d2d3. . .. . .DelayDelayDelayDelayArrival times at a linkDeparture times at the link



Aggregated Traffic• Simplified modeling

– We don’t keep track of individual packets, only workload counts (number of packets or bytes)

– We “generate” workload counts» by probabilistic/analytical modeling, or» by simplified simulation

• Aggregated (or background) traffic is local (per link)• Shaping effects are complex to incorporate• Some dependences between explicit and background traffic

along a chain of links are complicated and are ignored



Hybrid Simulation (FIFO Links): Conceptual Framework• Given the arrival time ak of the kth explicit packet

• Generate the workload wk found in queue by the kth packet

• From ak and wk generate the departure time of the kth packet as

Departure Time dk = ak + wk + sk

where sk is the transmission time of the kth packet

Time

aK aK+1wK wK+1

dK = aK + wK + sK

Explicit Explicit

Explicit ExplicitBackground Background

ARRIVAL TIMES

DEPARTURE TIMES



Simulating the Background Traffic Effects• Use a traffic descriptor for the background traffic (e.g., carried

by special packets)• Traffic descriptor includes:

– Traffic volume information (e.g., packets/sec, bits/sec)– Probability distribution of interarrival times– Probability distribution of packet lengths– Time interval of validity of the descriptor

• Generate wk using one of several ideas and combinations thereof– Successive sampling (for FIFO case)– Steady-state queue length distribution (if we can get it)– Simplified simulation (microsim - applies to complex queuing

disciplines)



Hybrid Simulation (FIFO Case)• Critical Question: Given arrival times ak and ak+1, workload wk, and background traffic

descriptor, how do we find wk+1?

• Note: wk+1 consists of wk and two more terms:– Background arrivals in interval ak+1 - ak

– (Minus) transmitted workload in interval ak+1 - ak

• Must calculate/simulate the two terms• The first term is simulated based on the traffic descriptor of the background traffic• The second term is easily calculated if the queue is continuously busy in ak+1 - ak

Time

a1 a2 a3. . .

. . .

Arrival times/Workload found

w1 w2 w3

d1 = a1 + w1 + s1 d2 = a2 + w2 + s2 d3 = a3 + w3 + s3

Departure times



Short Interval Case (Easy Case)• Short interval ak+1 - ak (i.e., ak+1 < dk)

• Queue is busy continuously in ak+1 - ak

• So wk+1 is quickly simulated – Sample the background traffic arrival distribution to simulate the new

workload arrivals in ak+1 - ak

– Do the accounting (add to wk and subtract the transmitted workload in

ak+1 - ak )

k d

ak

Time. . .

Short Interval

wk

wk+1 = wk + (New bkg arrivals) - (Old bkg transmissions)

d

ak+1 wk+1

k+1



Long Interval Case

• Long interval ak+1 - ak (i.e., ak+1 > dk)• Queue may be idle during portions of the interval ak+1 - ak

• Need to generate/simulate – The new arrivals in ak+1 - ak

– The lengths of the busy periods and the idle periods• Can be done by sampling the background arrival distribution in each busy

period• Other alternatives are possibleTime. . .Long Intervalakwkak+1wk+1dkIdle PeriodsBusy Periodsdk+1



Steady-State Queue Length Distribution• If the interval between two successive explicit packets is very

long, we can assume that the queue found by the second packet is in steady state

• So, we can obtain wk+1 by sampling the steady-state distribution

• Applies to cases where the steady-state distribution can be found or can be reasonably approximated– M/M/1 and other M/M/. Queues– Some M/G/. systems



Micro Simulation: Conceptual Framework

• Handles complex queuing systems– Micro-packets are generated to represent traffic load within the context

of the queue only (i.e., they are not transmitted to any external links)– For long intervals, where convergence to a steady-state is likely

» Try to detect convergence during the microsim » Estimate steady-state queue length distribution» Sample the steady state distribution to estimate wk+1

• Microsim speeds up the simulation without sacrificing accuracy

• Microsim provides a general framework– Applies to non-stationary background traffic– Applies to non-FIFO service models (with proper modification)



Examples of Applications Analytical Modeling Discrete-Event Simulation

M/G/./. & G/G/./. FIFO

Analysis

M/G/./. & G/G/./. Priority

Analysis

Decomposition with Kleinrock Independence Assumption

DES only with Explicit Traffic

Hybrid DES with Explicit

and Background

Traffic Single Link with FIFO Service

Best Effort Service for Standard Data Traffic Yes N/A N/A Yes Yes

Best Effort Service for LRD/Self-Similar Behavior Traffic

Yes N/A N/A Yes Yes

"Chancing It" with Best Effort Service for Voice, Video and Data

Yes N/A N/A Yes Yes

Single Link with QoS-Based QueueingUsing QoS to differentiate service levels for the same type of traffic

N/AYes (loss of

accuracy) N/A Yes Yes

Using QoS to support different requirements for different application types given as a detailed study of setting Cisco Router queueing parameters

N/AHighly

approximateN/A Yes Yes

Network of Queues

General network model extending the previous QoS queueing model

N/AHop-by-hop


Yes (some loss of accuracy - e.g., traffic

shaping)

Yes (Run time a function of network

complexity)


accuracy]

Reduction of the general model to a representative end-to-end path

N/AHop-by-hop


N/AYes (Run time a

function of network complexity)


accuracy]

Analysis Scenarios



Demo End-to-end Delay: Baseline Network

Traffic modeled as1) Explicit traffic2) Background traffic



Target Flow: ETE delay as a function of ToS

Target flow: Seattle Houston - modeled using explicit traffic– Varying its Type of Service (ToS)

» Best Effort (0)» Streaming Multimedia (4)



Explicit Simulation Results for Target Flow

– Total traffic volume» 500 Mbps

– Time modeled» 35 minutes

– Simulation duration» 31 hours



Hybrid Simulation Results for Target Flow

– Total traffic volume» 500 Mbps

– Time modeled» 35 minutes

– Simulation duration» 14 minutes



Comparison: Hybrid vs Explicit Simulation



References• Networking

– Bertsekas and Gallager, Data Networks, Prentice-Hall, 1992• Device Queuing Implementations

– Vegesna, IP Quality of Service, Ciscopress.com, 2001– http://www.juniper.net/techcenter/techpapers/200020.pdf

• Probability and Queuing Models– Bertsekas and Tsitsiklis, Introduction to Probability, Athena Scientific, 2002,

http://www.athenasc.com/probbook.html– Cohen, The Single Server Queue, North-Holland, 1992– Takagi, Queuing Analysis: A Foundation of Performance Evaluation. (3

Volumes), North-Holland, 1991– Gross and Harris, Fundamentals of Queuing Theory, Wiley, 1985– Cooper, Introduction to Queuing Theory, CEEPress, 1981

• OPNET Hybrid Simulation and Micro Simulation– See Case Studies papers in

http://secure.opnet.com/services/muc/mtdlogis_cse_stdies_81.html

http://www.juniper.net/techcenter/techpapers/200020.pdf

http://www.athenasc.com/probbook.html

http://secure.opnet.com/services/muc/mtdlogis_cse_stdies_81.html

opnet full presentation

Documents

kleinrock

differentiate

standard data

high priority

traffic arrival

similar behavior

queues hybrid

queues hybrid