Traffic Engineering Concepts
for Cellular Packet Radio Networks with Quality of Service Support
Presented by Yujing Wu Based on
Peter Stuckmann„s
Public PhD defense on 20/06/2003
Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
Peter Stuckmann‘s thesis work 20/06/2003
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Motivation: Cost-effective Network Evolution
•
Traffic Engineering and Traffic Management
Design and upgrade the network in a cost-effective way Based on traffic-performance relation Service differentiation ensured by admission control and scheduling ->Influence on traffic-performance relation
term traffic QoS parameter resources tool methodology
circuit-switched offered traffic in Erlang blocking probability traffic channels simple formula or table Erlang-B formula
packet-switched amount of data per time in kbit/s throughput, delay,... packet data channels dimensioning graphs or tables simulation results, analytical/algorithmic techniques
3
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Evolution from 2G to 3G
Requirements for 3G systems:
high data rate (144kbit/s outdoor and 2Mbit/s indoor) ; asymmetric traffic; packet switched; high spectrum efficiency.
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Assignment of GSM Channels for GPRS
pool of GSM physical channels
GPRS packet data channels
x fixed PDCHs y on-demand PDCHs
• Packet Data Channels (PDCHs) assigned out of pool of GSM
• •
physical channels Fixed PDCHs are permanently available On-demand PDCHs only available if not used for GSM circuit-switched traffic
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Dimensioning Approach
• • • •
Dimensioning graphs for application-specific performance measures Valid for the cell and load scenarios of interest Applicability: only based on user number/ traffic volume in the busy hour Accuracy: derived from realistic models for the protocol stacks, traffic patterns and radio channel
QoS
QoS
resource configuration 3
QoS limit
QoS limit
resource configuration 2
resource configuration 1
acceptable traffic
offered traffic
predicted traffic
offered traffic 6
Peter Stuckmann‘s thesis work 20/06/2003
TE Methodology and Evaluation Scenarios
1. Analytical and algorithmic models:
2. Measurement: 3. Simulation:
Lack of details of protocol stacks and realistic traffic model (close-loop control of TCP and heavy tailed traffic)
Lack of tunable traffic load and different protocol options
Detailed implementations of GPRS and Internet protocols Traffic generator for common applications Models of the radio channel
Simulation Scenarios: • Per cell: max PDCH no 8; max IP throughput 80kbits; 1-40 active stations; • Traffic: Web browsing and email with small obj size; not much WAP traffic; no mobility model.
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Traffic Management
• •
Increase performance for best-effort services
Coupled RLC/MAC implementation considering urgency of RLC blocks for MAC scheduling MAC scheduler considering link quality
Priority queuing Fairer scheduling algorithms introducing weights for traffic classes
QoS application 2 application 2 QoS limit 2 QoS limit 1
Support application-specific QoS (class differentiation on MAC level)
QoS
QoS limit 2 QoS limit 1
application 1
application 1 capacity gain acceptable traffic offered traffic (aggregate) acceptable traffic offered traffic (aggregate) 8
Peter Stuckmann‘s thesis work 20/06/2003
Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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Multimedia Traffic Modelling
• Aim • Predicted applications for mobile users
Internet (WWW, e-mail, FTP) Wireless Application Protocol (WAP) Streaming (Video & Audio) Video-Conferencing, VoIP Use measurement results for fixed Internet from literature Perform own measurements Use standardized models (e.g. UMTS 30.03) Use market prediction studies
definition of user profiles characterization of sessions
• Methodology
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WWW Session / Structure of Web Page
Sdfsadfsda safsdfsafd sadfasfdsaf sdfasfdsaf Sdfsadfsda safsdfsafdSdfsadfsdasaf sdfsafd Sdfsad fsdasafsdfsafdSdfs adfs a safs dfsafd Sdfsadfsda safsdfsafd Sdfsadfs da fgdfg dfg afsdfs afd gfdgs fgsdf sdfg sdg sdfg
Sdfsadfsda safsdfsafd sadfasfdsaf sdfasfdsaf Sdfsadfsda safsdfsafdSdfsadfsdasaf sdfsafd Sdfsad fsdasafsdfsafdSdfs adfs a safs dfsafd Sdfsadfsda safsdfsafd Sdfsadfs da fgdfg dfg afsdfs afd gfdgs fgsdf sdfg sdg sdfg
tread
page 1
picture links text
page n
object 1
tobject
object 2
object m
size m
page 2
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Mosaic Traffic Model [Arlitt and Williamson 1995]
Parameter Pages per session Objects per page [byte] Object size [byte]
Distribution Geometric Geometric
Mean Variance 5 12 s 2.5 20.0 144.0 3.75 1.36 x 10e6 5.2
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Reading time between pages [s] Exponential
Log2-Erlang-k (k=17) 3700 Transformed Erlang 9.4
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Choi‟s Behavioral Model of Web Traffic
• Larger WWW pages with higher object sizes • Not yet suitable for GPRS traffic engineering • Important when performance of wireless Internet access will
be comparable to today„s fixed networks, e.g. with EGPRS or UMTS
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E-mail Traffic Model
• Parameters derived by measurements made at the Lawrence • • •
Berkeley Laboratory (California, USA) by Paxson in 1994 Fixed overhead of 300 byte Bimodal distribution of e-mail sizes Lower 80% can be interpreted as text-based mails Upper 20% represents mails with attached files Maximum size 100 kbyte
Distribution Log2-Normal Transformed Normal E-mail size (upper 20%) [byte] Base quota [byte] Log2-Normal Transformed Normal C onstant Mean 1700 10.0 15700 9.5 300
Parameter E-mail size (lower 80%) [byte]
Variance 5.2 x 10e6 2.13 115 x 10e9 12.8 ---
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WAP Traffic Model
• Parameters are depending on the content • Values derived by measurements performed at a WAP gateway
in test operation
Suitable for introduction scenarios Will change over the next years (today: 1 kbyte for monochrome decks, 3 kbyte for colored decks)
Distribution Geometric Exponential Log2-Normal Log2-Normal Mean 20.0 14.1 96.1 562.6 Variance 3800 198.8 3.75 x 10e3 0.71 3.5 x 10e5 1.55
Parameter Decks per session Reading time between decks [s] Packet size 'Get Request' [byte] Packet size 'C ontent' [byte]
Transformed Normal 6.34 Transformed Normal 8.60
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Video Streaming Traffic Model
• Traffic model based on three real video sequences • •
coded with the H.263 codec specified by the ITU-T (similar to MPEG) Sequences proposed by the Video Quality Expert Group each one representing a particular group of motion intensity Sequences are randomly concatenated producing a continuous video stream
Q20 C laire C arphone Foreman 10.9 kbit/s 26.7 kbit/s 31.7 kbit/s Offered IP traffic 80-10-10 Mix
Sequences
}
14.39 kbit/s
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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GPRS Architecture
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GPRSIM
GPRSim Load Generator
Manager FTP
HTTP SMTP
Video WAP
WTP
Circuit Switched Generator
Session Arrival Process
• •
TCP IP
UDP
CAC
session mgmt.
Event-driven Simulator based on C++ and SDL Prototype implementations of protocol stacks at
SNDCP LLC (SDL) RLC/ MAC (SDL) Transc.
Channel Mgmt.
SNDCP
LLC Relay
RLC/ MAC (SDL)
BSSGP Frame Relay Gb Downl. GbUplink
LLC (SDL)
BSSGP
•
Mobile Station (MS) Base Station (BS) SGSN
Channel Error Model Um
Frame Relay
Transc.
SGSN
MS
BS
G b
• • •
GIST Web Interface Statistical Evaluation
Throughput (S) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 Funet 0.9 24 RA Slots 0.8 56 RA Slots 88 RA Slots 0.7 0.6 0.5 0.4 0.3 0.2 0.1 1000 3000500070009000 2000 400060008000 10000 Offered Load [byte/s] 1 Blocking Rate
Railway Mobitex 0.2 0.6 0.4 0.81 1.2 1.6 2 1.4 1.8 Offered Load (G)
Peter Stuckmann‘s thesis work 20/06/2003
Stochastic traffic models to generate well-defined traffic load Channel and mobility models Evaluation and graphical representation Validation by measurement
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Simulation Results
IP user/cell throughput IP datagram delay application response time
session blocking rate,
circuit switch call blocking rate PDCH utilization assigned PDCHs
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Validation I (Analytical TCP Model, Meyer2001])
TCP Client TCP Server
SLOW START
PSH + Data ACK
} }
1st RTT
Transmission time t for a file of size F: ( F BSS ) t ( F ) N SS ( RTT TBFsetup ) DLCH RTCP Transition to steady state with the number of Round-trip periods Nss: R ( RTT TBF )
W MSS N SS RTT init k SS RTCP
N SS log
TCP setup
2nd RTT
Winit MSS log(kss )
STEADY STATE
}
Amount of data Bss transmitted in slow start:
3rd RTT
1 kSS N SS BSS Winit MSS 1 kSS
Model Analytical Simulation
WWW (3700 byte) 14.9 kbit/s 17.2 kbit/s
e-mail (1 kbyte) 22.7 kbit/s 22.9 kbit/s
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Peter Stuckmann‘s thesis work 20/06/2003
Validation II (Measurement)
Downlink IP throughput [kbit/s]
Vodafone NL GPRS measurement settings • CS-2 • 4 fixed PDCHs • Multislot (dl/ul) 3/1
BTS
Downlink IP throughput (FTP) 30 GPRSim Measured
25
20
15
Notebook & GPRS mobile
10
Um
BSC
PPP infrared (WinDump)
5 G b SGSN 0 1 1.5 2 2.5 3 3.5 4 Number of mobile stations 4.5 5
Measurement Point
IP-Backbone
Network
GGSN Gi
External IP-Network Internet Web Server
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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Fluid-flow Model Approach
• Basic concept:
Traffic sources are water taps, being randomly turned on and off Regarded network element is a water reservoir with constant depletion rate C Single source: behavior controlled by two-state Markov Chain Multiple sources: Superimposing N equal MMRP„s again leads to an MMRP ON state probability (activity factor) Mean burst length ENB Transmission rate during ON state h
alpha = 0.187, h = 3272 byte/s , EN_B = 9150 byte 100000 GPRSim with ON/OFF sources GPRSim with WWW sources Fluid-flow Analysis 10000
• Source model: Markov-modulated Rate Process (MMRP) • MMRP parameters:
OFF ON
h EN B
Mean IP Datagram Delay [ms]
1000
1
100
4 5 6 7 Number of MS Peter Stuckmann‘s thesis work 20/06/2003
10 0
1
2
3
8
9 10
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MMAP/G/1 Queue [Vornefeld 2002]
• Arrival Process: analytically tractable representation of Choi„s
WWW model using Marked Markovian Arrival Process:
• • • •
Sojourn times in on and off phase approximated with PH-type distributions (EM algorithm) Poisson arrivals of single IP datagrams during on phase
Accounts for complicated stochastic nature of arrival process Traffic sources can have individual service time distributions No batch arrivals of IP datagrams
Service Process: n-point distribution describing the number of time slots required for transmission of an IP datagram
• Approximation of n-point distribution by cont. PH-dist. (EM alg.)
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Link-level simulations, models of channel coding and radio channel Each IP packet (576 byte) leads to batch arrival of RLC blocks Size of batch determined by applied Coding Scheme (CS)
Result Comparison: System Capacity and CIR
Scenario parameters: • 1 MS • CS-2 • MSC = #PDCHs
Mean IP packet delay [ms]
1e+06
Simulation 2 PDCHs Analysis 2 PDCHs Simulation 4 PDCHs Analysis 4 PDCHs
1e+05
Deviations caused by TCP protocol behavior: Batch arrivals on IP level Slow start and congestion avoidance (elastic traffic)
1e+04
1e+03
1e+02
1e+01 5 10 15 20 25 30
Mean C/I [dB]
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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Performance and system measures
• • • •
Application response time
for each received file (WAP deck, e-mail or WWW page) the difference between the date of request from the client (GET request) and the date of reception at the client is calculated during an ongoing transmission the downlink IP throughput for each user is calculated for each TDMA frame for each received IP packet the difference between the date of transmission (IP data request) and the date of reception (IP data indication) is calculated the quotient of the total amount of received IP bytes in one radio cell divided by the regarded time period equals the offered IP traffic (loss-free system) the quotient of the number of transmitted radio blocks containing data or control information divided by the total number of transmitted radio blocks
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Downlink IP throughput per user
Downlink IP datagram delay
Downlink IP system throughput per radio cell
•
Downlink PDCH utilization
General Simulation Parameter Settings
multislot cap. (DL/UL) coding scheme PDCHs fixed PDCHs on-demand C/I [dB] cluster size cell radius [m] MS velocity [km/h] TCP version TCP MSS [byte] TCP maximum window size [kbyte] HTTP version Traffic mix WWW / email Traffic mix WAP / WWW / email Traffic mix Streaming / WWW / email 4/1 CS-2 8 0, 8 12 (BLER = 13.5 %) 3, 7 300, 3000 7, 100 Reno 512 8 1.1 30% / 70% 60% / 12% / 28% 10% / 27% / 63%
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GPRS with Fixed PDCHs
• Maximum user throughput of 22 kbit/s • Maximum system throughput of 56 kbit/s for 8 fixed PDCHs
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Effect of Multislot Capability and C/I
• Effect of multislot capability only visible in situations with low
•
traffic load Low sensitivity of performance to mean C/I
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GPRS with on-demand PDCHs
• Performance degradation only occurring with high coexisting •
speech traffic Effect of lower speech traffic visible in situations with medium GPRS traffic
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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WAP vs. Conventional Internet Applications (I)
• WAP and e-mail response times remain below 5 s for the whole •
load range for pure traffic scenarios, while WWW exceeds 30 s In the traffic mix scenario (60% WAP, 28% email and 12% WWW), WWW performance increases, while e-mail and WAP performance decreases slightly
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WAP vs. Conventional Internet Applications (II)
• Low throughput performance for WAP because of small deck size • E-mail performance remains stable in pure traffic scenario •
because of low offered traffic per session Similar behavior of WWW and e-mail in traffic mix scenario because of equal load conditions
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Streaming vs. Background Applications (I)
• Streaming performance in traffic mix scenario stable over the • •
whole load range for EGPRS, up to 20 MSs for pure Streaming For GPRS only 5 MSs (pure) and 15 MSs (mix) acceptable for Streaming applications WWW performance only affected in GPRS scenario
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and traffic
management
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Dimensioning for Fixed and On-demand PDCHs
• Dimensioning graph for fixed PDCHs based on the performance for •
different resource configurations over the offered IP traffic Dimensioning graph for on-demand PDCHs based on the performance for different coexisting speech loads over the offered IP traffic
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Traffic Engineering Rules
1)Define the QoS target 2)Estimate the number of users per cell 3)Define the offered IP traffic per user 4)Calculate the offered IP traffic per cell 5)Regard the operating point p defined by the QoS
target on the y-axis and the offered traffic per cell on the x-axis and choose the next curve that lies above p 6)Result: Number of fixed PDCHs to be allocated or the acceptable coexisting speech traffic
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Outline
• Motivation and objectives • Traffic models for existing and future applications • Simulation environment GPRSIM • Analytical Traffic Engineering Approaches • GPRS/EDGE performance analysis • Performance of different applications • Traffic engineering • QoS support • Mutual dependency of traffic engineering and
traffic management
Peter Stuckmann‘s thesis work 20/06/2003
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Dimensioning Graphs without QoS support
• Streaming performance starts to decrease with an offered traffic •
of 20 kbit/s and 4 fixed PDCHs Streaming application can be seen as the critical application
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Dimensioning Graphs with QoS support
• In using DWRR the performance of Streaming applications can be •
increased Depending on the QoS target for lower prioritized applications a resource configuration with 4 fixed PDCHs might be sufficient
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Conclusions
• Traffic engineering rules for the cost-effective evolution of
cellular packet radio networks
• Advanced traffic management techniques
Requirements: applicability and accuracy Approach: traffic models and prototype implementation (GPRSIM) Result: Dimensioning graphs for fixed and on-demand configurations
Proposed scheduling algorithms for best-effort services • DPARR very effective and easy to implement Proposed scheduling algorithms for traffic class support • Solution should be based on the operator´s strategy Connection admission control parameterization
• Mutual dependency of traffic engineering and traffic management
Estimate the effects of QoS support and best-effort scheduling on traffic engineering rules Stay inline with network evolution
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Research Contributions
• Development of a comprehensive GPRS/EDGE emulation tool for
•
•
•
• • • • •
radio interface performance analysis and capacity planning Identification and development of traffic models for existing and future mobile applications Comprehensive performance analysis for GPRS and EDGE networks considering a wide range of applications and system parameters Derivation of radio resources traffic engineering rules for the cost-effective evolution of cellular packet radio networks Development and performance evaluation of advanced QoS management algorithms for cellular packet radio networks Book publication “The GSM Evolution” (Wiley 2002) 2 journal publications More than 20 conference papers 1 patent
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What can we learn from this work? Thoughts on TE of CDMA Cellular Networks
• Can we borrow the TE methodology in this work? • Survey of simulators of CDMA networks (not complete yet): •NS-2, Glomosim, SSF, Telesim: not provide. any other free network simulator? •Several commercial products: e.g. Opnet wireless module, MACdma, Netplan (Motorola), CELLsim (Nomad Access) etc. • At the initial stage, can we build a simple simulator (without implementation of full protocol stack) for a good enough evaluation? Must consider the key features of CDMA systems: interference-limited capacity. • Theoretic analysis is always a good starting point.
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