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Lecture on Wireless Measurements & Modeling

Prof. Maria PapadopouliUniversity of Crete

ICS-FORTHhttp://www.ics.forth.gr/mobile

Agenda

• Introduction on Mobile Computing & Wireless Networks• Wireless Networks - Physical Layer• IEEE 802.11 MAC• Wireless Network Measurements & Modeling • Location Sensing• Performance of VoIP over wireless networks• Mobile Peer-to-Peer computing

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Empirical measurements• Can be beneficial in revealing

– deficiencies of a wireless technology – different phenomena of the wireless access & workload

• Impel modelling efforts to produce more realistic models & synthetic traces

• Enable meaningful performance analysis studies using such empirical, synthetic traces and models

Highlight the ability of empirical-based models to capture the characteristics of the user-workload and provide a flexible framework for using them in performance analysis

Propagation Models• One of the most difficult part of the radio channel design• Done in statistical fashion based on measurements made specifically

for an intended communication system or spectrum allocation• Predicting the average signal strength at a given distance from the

transmitter

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Signal Power Decay with Distance

• A signal traveling from one node to another experiences fast (multipath) fading, shadowing & path loss

• Ideally, averaging RSS over sufficiently long time interval excludes the effects of multipath fading & shadowing general path-loss model:

P(d) = P0 – 10n log10 (d/do)

n: path loss exponent P(d): the average received power in dB at distance d P0 is the received power in dB at a short distance d0

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Monitoring

• Depending on type of conditions that need to be measured, monitoring needs to be performed at

• Certain layers • Spatio-temporal granularities

• Monitoring tools – Are not without flaws – Several issues arise when they are used in parallel for

thousands devices of different types & manufacturers:• Fine-grain data sampling• Time synchronization• Incomplete information• Data consistency

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Monitoring & Data Collection

• Fine spatio-temporal detail monitoring can Improve the accuracy of the performance estimates but also Increase the energy spendings and detection delay

Network interfaces may need to • Monitor the channel in finer & longer time scales• Exchange this information with other devices

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Challenges in Monitoring (1/2)

• Identification of the dominant parameters through – sensitivity analysis studies

• Strategic placement of monitors at – Routers– APs, clients, and other devices

• Automation of the monitoring process to reduce human intervention in managing the

• Monitors • Collecting data

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Challenges in Monitoring (2/2)

• Aggregation of data collected from distributed monitors to improve the accuracy while maintaining low overhead in terms of

• Communication • Energy

• Cross-layer measurements, collected data spanning from the physical layer up to the application layer, are required

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Wireless Networks

– Are extremely complex – Have been used for many different purposes– Have their own distinct characteristics due to radio

propagation characteristics & mobility e.g., wireless channels can be highly asymmetric and time varying

Note: Interaction of different layers & technologies creates many situations

that cannot be foreseen during design & testing stages of technology development

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Empirically-based Measurements

• Real-life measurement studies can be particularly beneficial in revealing– deficiencies of a wireless technology – different phenomena of the wireless access and the workload

• Rich sets of data can – Impel modeling efforts to produce more realistic models– Enable more meaningful performance analysis studies

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Unrealistic Assumptions

– Models & analysis of wired networks are valid for wireless networks

– Wireless links are symmetric– Link conditions are static– The density of devices in an area is uniform– The communication pairs are fixed – Users move based on random-walk models

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Wireless Access Parameters

• Traffic workload– In different time-scales– In different spatial scales (e.g., AP, client, infrastructure)– In bytes, number of packets, number of flows, application-mix

• Delays – Jitter and delay per flow– Statistics at an AP and/or channel

• User mobility patterns• Link conditions• Network topology

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Traffic Load Analysis

• As the wireless user population increases, the characterization of traffic workload can facilitate

• More efficient network management • Better utilization of users’ scarce resources

• Application-based traffic characterization

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Hourly Session arrival rates

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Traffic Load at APs

• Wide range of workloads that log-normality is prevalent

• In general, traffic load is light, despite the long tails

• No clear dependency with type of building the AP is located exists– Although some stochastic ordering is present in

• Tail of the distributions

• Dichotomy among APs is prominent in both infrastructures:

APs dominated by uploaders APs dominated by downloaders

• As the total received traffic at an AP ↑– There is also ↑ in its total traffic sent – ↓ in the sent-to-received ratio

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Traffic load at APs

• Substantial number of non-unicast packets• Number of unicast received packets strongly correlated with

number of unicast sent packets (in log-log scale)• Most of APs send & receive packets of relatively small size• Significant number of APs show rather asymmetric packet sizes

– APs with large sent & small receive packets– APs with small sent & large receive packets

• Distributions of the number of associations & roaming operations are heavy-tailed

• Correlation between the traffic load & number of associations in log-log scale

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In general, the traffic load is light

long tailsRARE EVENTS OF LARGESIZE

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Wide-range of workloads

As total received traffic at an AP ↑ its total traffic sent ↑

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APs with small amount of received traffic and large amount of sent traffic

As total received traffic at an AP ↑ its total traffic sent/received ↓

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The number of unicast received packets strongly correlated with the number of unicast sent packets (in log-log scale)

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Asymmetry in the sizes of sent & received packets at an AP

Majority of APs with small sent and received packet sizes

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Correlation between traffic load & # of associations

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Application-based Traffic Characterization

Using port numbers to classify flows may lead to significant amounts of misclassified traffic due to:– Dynamic port usage– Overlapping port ranges– Traffic masquerading

• Often peer-to-peer & streaming applications:– Use dynamic ports to communicate– Port ranges of different applications may overlap– May try to masquerade their traffic under well-known “non-

suspicious” ports, such as port 80

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Desirable Properties for Models

– Accuracy– Tractability– Scalability– Reusability– “Easy” interpretation

Related work• Rich literature in traffic characterization in wired networks

– Willinger, Taqqu, Leland, Park on self-similarity of Ethernet LAN traffic– Crovela, Barford on Web traffic– Feldmann, Paxson on TCP– Paxson, Floyd on WAN– Jeffay, Hernandez-Campos, Smith on HTTP

• Traffic generators for wired traffic– Hernandez-Campos, Vahdat, Barford, Ammar, Pescape, …

• P2P traffic– Saroiu, Sen, Gummadi, He, Leibowitz, …

• On-line games– Pescape, Zander, Lang, Chen, …

• Modelling of wireless traffic– Meng et al.

Dimensions in Modeling Wireless Access

• Intended user demand• User mobility patterns

– Arrival at APs– Roaming across APs

• Channel conditions• Network topology

Mobility models

• Group or individual mobility• Spontaneous or controlled• Pedestrian or vehicular• Known a priori or dynamic

• Random-walk based models– Randway model in ns-2

• Markov-based model

A Very Simple Channel Model

Idle BusyPii

Pbb

Pbi

Pib

• A channel can be in the idle or busy state• Markov-based model allows us to determine:

• How much time the system spends in each state• Probability of being in a particular state

In real rife, there is non-stationarity due dynamic phenomena

Compute stationary probabilities

Gilbert model

Main approaches for traffic generation

• Packet-level replay– An exact reproduction of a collected trace in terms of packet

arrival times, size, source, destination, content type Reflects specific traffic conditions

– Suffers from arbitrary delays e.g., interrupts, service mechanisms, scheduling processes difficult to incorporate feedback-loop characteristics

• Source-level generation Allows the underlying network, protocol, & application layer to

specify & control the packet arrival process– Simplest example: infinite source model

Our approach

Inspired by the source-level (or network independent) modelling

Assumptions:1. Client arrivals at an infrastructure (initiated by humans) at a large extent are not affected by the underlying network technology

2. Very low % of packet loss at the network layer flow arrivals & sizes approximate intended user traffic demand

1 2 3 0Session

Wired Network

Wireless Network

Router

Internet

User A

User B

AP 1 AP 2

AP3Switch

disconnection

Flow

time

Events

Arrivals

t1 t2 t3 t7t6t5t4

Traffic Demand Parameters

• Session– arrival process– starting AP

• Flow within session– arrival process– number of flows– size (in bytes)

Captures interaction between clients & network

Above packet-level analysis

Wireless infrastructure & acquisition

• 26,000 students, 3,000 faculty, 9,000 staff in over 729-acre campus• 488 APs (April 2005), 741 APs (April 2006)• SNMP data collected every 5 minutes• Several months of SNMP & SYSLOG data from all APs• Packet-header traces:

– Two weeks (in April 2005 and April 2006)– Captured on the link between UNC & rest of Internet via a high-

precision monitoring card

Related modeling approaches

Flow-level modeling by Meng [mobicom ‘04] No session concept Weibull for flow interarrivals Lognormal for flow sizes AP-level over hourly intervals

Hierarchical modeling by Papadopouli [wicon ‘06] Time-varying Poisson process for session arrivals

BiPareto for in-session flow numbers & flow sizes Lognormal for in-session flow interarrivals

Sessions capture the non-stationarity of traffic workload

Modeling methodology

1. Selection of models (e.g., various distributions)2. Fitting parameters using empirical traces3. Evaluation and comparison of models• Visual inspection e.g., CCDFs & QQ plots of models vs. empirical data• Statistical-based criteria e.g., QQ/simulation envelopes, Kullback-Liebler divergence• Systems-based criteria e.g., throughput, delay, jitter, queue size

4. Validation of models5. Generalization of models

Synthetic trace generation

Synthetic traces based on empirical ones

Generate session arrivals within each session: generate number of flows for each flow: generate flow arrivals & sizes based on specific models

• Session arrivals: using hourly, building-specific empirical traces• Flow-related data: using empirical traces of different spatial scales

original data from the real-life infrastructureProduced by this process:

Model validation

Use empirical data from different• tracing periods

April 2005 & 2006• spatial scales

AP-level < building-level < building-type-level < network-wide• traffic conditions @ AP

• campus-wide wireless infrastructures UNC, Dartmouth

Do the same distributions persist across these traces ?

Compare their performance (empirical traces: “ground truth”)

YES!

Model evaluation• Create synthetic data based on models• Analysis with metrics not explicitly addressed by the models

– Statistical-based• aggregate flow arrival count process• aggregate flow interarrival (1st & 2nd order statistics)

• System-based: performance of an IEEE802.11 LAN• traffic load and queue size in various time scales• per-flow & hourly aggregate throughput• per-flow delay and jitter

Compare their performance (empirical traces: “ground truth”)

Modeling in Various Spatio-temporal Scales

Sufficient spatial detail Scalable Amenable to analysis

Hourly period @ AP

Network-wide

ObjectiveScales

Tradeoff with respect to accuracy, scalability & reusability

Scalability vs. Accuracy: Flow Interarrivals

EMPIRICAL

BDLG(DAY)

BDLGTYPE(DAY)NETWORK(TRACE)

Spatial /Temporal Scales

Scalability vs. Accuracy: Number of Flow Arrivals in an Hour

EMPIRICAL

BDLG(DAY)

NETWORK(TRACE)

BDLGTYPE(TRACE)

Model evaluation• Create synthetic data based on models• Analysis with metrics not explicitly addressed by the models

– Statistical-based• aggregate flow arrival count process• aggregate flow interarrival (1st & 2nd order statistics)

• System-based: performance of an IEEE802.11 LAN• traffic load and queue size in various time scales• per-flow & hourly aggregate throughput• per-flow delay and jitter

Compare their performance (empirical traces: “ground truth”) Dominant parameters ? Impact of application mix?

Wireless Networks

RouterInternet

User A AP

AP

Switch

User B

Scenario

User C

Varioustraffic, channelconditions

Network monitor

Collected traces

StatisticalAnalysis

TestbedDeploymen

t

Monitoring &Data collection

CertainProtocol

Evaluationat AP

Iterative process

Networkmonitor

Select testbed Protocol evaluation Cross validation

Simulation/Emulation testbed• TCP flows• UDP

• Wired clients: senders• Wireless clients: receivers

Hourly aggregate throughput

EMPIRICAL

BIPARETO-LOGNORMAL

Fixed flow sizes & empirical flow arrivals (aggregate traffic as in EMPIRICAL)

Pareto flow sizes, empirical flow arrivals

FLOW SIZE—FLOW (INTER)ARRIVAL

BIPARETO-LOGNORMAL-AP

Impact of flow size

Per-flow throughput

EMPIRICALBIPARETO-LOGNORMAL-AP

BIPARETO-LOGNORMAL

Fixed flow sizes & empirical number of flows

FLOWSIZE—FLOWARRIVAL

Pareto flow sizes & uniform flow arrivals

Pareto flow sizesdue to large % of small size flows (= MSS)

Impact of application mix on per-flow throughput

AP with 85% web traffic

AP with 50% web & 40% p2p traffic

AP with 80% p2p traffic

TCP-based scenario

UDP traffic scenario Wireless hotspot AP Wireless clients downloading Wired traffic transmit at 25Kbps Total aggregate traffic sent in CBR & in empirical is the same

Empirical: 1.4 KbpsBipareto-Lognormal-AP: 2.4 KbpsBipareto-Lognormal: 2.6 Kbps

Large differences in the distributions

ConclusionsModel validation

over two different periods (2005 and 2006)

over two different campus-wide infrastructures (UNC & Dartmouth)BiPareto captures well the flow sizes

over heavy & normal traffic conditions @ AP

using statistical-based metrics

using system-based metrics hourly aggregate throughput per-flow delay per-flow throughput

Conclusions (con’t)

Accuracy:

• our models perform very close to the empirical traces

• popular models deviate substantially from the empirical traces

Scalability:

• same distributions at various spatial & temporal scales• group of APs per bldg addresses scalability-accuracy tradeoffs

Accurate and scalable models of wireless demand

Conclusions (con’t)

Application mix of AP traffic mostly web: very accurate models both web & p2p : models are ok mostly p2p: large deviations from empirical data

Modelling P2P traffic is challenging due to the increased number, diversity, complexity & unpredictability in user interaction

Both flow size and flow interarrivals

Impact of various parameters

Revisiting modelling approach• Physical meaning of the models and their parameters• Client profile

– e.g., depending on the application-mix, amount of traffic • Group mobility• Multiple network interfaces• Cooperative client models• Dependencies among traffic demand & network conditions

– Impact of underlying network conditions on application & usage patterns

UNC/FORTH web archive

Online repository of models, tools, and traces– Packet header, SNMP, SYSLOG, synthetic traces, …http://netserver.ics.forth.gr/datatraces/ Free login/ password to access it

Simulation & emulation testbeds that replay synthetic traces for various traffic conditions

Mobile Computing Group @ University of Crete/FORTH http://www.ics.forth.gr/mobile/

maria@csd.uoc.gr

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Application-based traffic characterization

• Most popular applications: web browsing and peer-to-peer (81% of the total traffic). Most users are also dominated by these two applications.

• Network management & scanning: responsible for 17% of the total flows.• While building-aggregated traffic application usage patterns appear similar, the application cross-section

varies within APs of the same building.• Most wireless clients appear to use the wireless network for one specific application that dominates

their traffic share.• File transfer flows (e.g., ftp and peer-to-peer) are heavier in the wired network than in the wireless one.• There is a dichotomy among APs, in terms of their dominant application type and downloading and

uploading behavior.