Scaling Issues for Multi-Paradigm Network Modeling Rajive L. Bagrodia Professor Computer Science Dept UCLA [email protected]

Scaling Issues for Multi-Paradigm Network Modeling

Rajive L. Bagrodia Professor

Computer Science DeptUCLA

[email protected]

2 DAWN Meeting Sept, 2006© Rajive Bagrodia, 2006;

Adequate feedback from PHY is one of the key requirements to success

Cross Layer Interactions• ‘Cross-layer interactions’ are key to provisioning dynamic QoS among

the voice, video, and data traffic of next generation wireless networks.• Traditional approaches based on simulation or physical testbed are unable

to sufficiently capture the impact of the cross-layer interactions on performance of real applications and protocol stacks.

• Networked systems increasingly encompass heterogeneous networks.• Need for a new generation of multi-paradigm evaluation approaches.

ApplicationMiddleware Service

NetworkMACPHY

Cro

ss L

ayer

Others(802.11b…)

OFDMSmart Antenna(SISO/MIMO)

UWB

Transport

PDA application with intermittent connectivity… Location service…

Wireless aware TCP… Multi-path routing…

Power conservation…


Past studies

Scalability

Fidelity

Flexibility

Simulation

Analytical models

Physical testbed

Emulation

Roofnet (MIT), PlanetLab (Princeton etc), ORBIT (Rutgers), MVWT (CalTech)

Realistic, but not scalable, limited flexibility and controllability

flexible, transparent execution of application and protocol, but not scalable

Fluidflow model(UMass), Bianchi-model (JSAC’03)

scalable, but use statistical application model, limited fidelity and flexibility

ns-2(ISI), QualNet(SNT), GloMoSim(UCLA), OpNet

scalable, flexible, but use statistical application model

Fidelity of past wireless emulation tools below expectation

NIST Net (NIST), EMPOWER (MSU), MobiEmu (HRL)


Simulation, Emulation or Physical ?

Our Approach

Select and operate at any point in the scalability vs. reality curve

Or,

Cover the entire range by moving along the curve

Simulation

Real code Emulation

Physical Test-bed

Deployment

Reality

Fle

xibi

lity

S

cala

bili

ty


Our approach

• A multi-paradigm framework offering combined uses of analytical models, simulation, emulation, and physical testbed and used for heterogeneous networks and devices:

Computer

Computer ComputerComputer

Email, file, or content distribution server

WLAN with mobile hosts

Backbone network Community mesh networkPhysical

TestbedAnalytical

Model Simulation

Emulation or Physical testbed

Emulation

Simulation orAnalytical model

Sensor Networks


Challenges• Achieving emulation fidelity

– Realistically model characteristics of wireless channel in real time at microseconds granularity

• Seamless integration of MAC and PHY models in the real protocol stack– Minimizing emulation overhead

– Support easy integration of new models

• Real time synchronization of simulation and emulation entities– Accurately model the communications among emulated,

simulated and physical hosts


Achievements

• Scaling Emulations – Emulate ‘multiple target nodes’ to a single host cpu

– Integrate detailed simulations into emulation testbeds

• Environmental mobility in wireless systems– Modeling environmental mobility

• Using scaled emulations to evaluate real application and protocol performance– Multi-hop MANET protocols

– Impact of transport subsystems on end-end performance


Scalability of Emulation Entity• Setup

– 1 laptop emulating 1~4 wireless hosts and 4 laptops each emulating one wireless host.

• Scenario 1: udp sources listen to orthogonal channels

• Scenario 2: udp sources listen to single channel

– 1~4 backlogged UDP sessions

– Data rate: 11Mbps

backlogged UDP traffic

backlogged UDP traffic


Orthogonal channel• Metric

– Ratio of late packets, i.e. cannot be delivered to real application before the scheduled time (= time_of_transmission + network_delay)

• Observation– Due to small emulation overhead, the ratio of late packets remains very low, and its

impacts on application throughput negligible


Single channel

• Observation– Ratio of late packets remain rather constant, thanks to the optimization

to aggregate emulation events with close timestamps.

– Emulation fidelity remains rather constant as we increase size of emulated WLAN


Scalable Emulations for MANET

• Setup – String topology with 1~9 hops (each spanning a distance of 500m),

running AODV-UU(U. Uppsala) kernel implementation

– One backlogged UDP (512B) session


1 ~ 9 hops

UDP traffic


Scalable Emulations for MANET (2)• Metric

– Processing delay of emulation kernelDelay between the timestamp of radio hardware interrupt to the actual delivery of emulation framework to network layer

• Observation:– Processing delay of emulation kernel is within that of actual wireless device

(15~20s) at 94% probability

Good Bad


Simulation scalability (1)• Setup

– Two stationary wireless LAN (emulated)


– The client in one wireless LAN generates backlogged UDP packets (200B) to the client in the other wireless LAN

• One stationary ad hoc network (simulated using QualNet)– Operate at 11Mbps and has a fixed node density of 1node/250m*250m

– 10% nodes generate CBR traffic at 10pkts/second


Simulation scalability (2)

• Observations– A simulation entity running on a COTS machine can simulate 60 nodes at ease

(less than 0.4% late packets), while being synchronized with real time at microseconds granularity

– A simulation entity executed in a parallel and distributed manner can have even better scalability


Case Study: Environmental Mobility

• Environmental Mobility:

–Ambient motion of entities (people, vehicles etc) in the vicinity of wireless communication

–Different from nodal mobility


Problem Statement

I. What are the effects of environmental mobility on wireless channel?

II. Can these effects be modeled– .. in an effective and efficient manner suitable for

wireless network simulations?

III. What is the impact of environmental mobility on network protocols?

– Or, is it really important to study?


Related Work

• [Echkardt96] showed that presence of single person can cause significant shadowing loss➡No detailed investigations or theoretical results

• [Castro04] used FDTD techniques to model effects of human head on cell phone reception➡ In 802.11 the separation is large, models are very computationally

expensive

• [Wysocki00] showed motion of people in immediate vicinity of people is Ricean distributed➡This is special case of what we are studying

➡ In general case we observe Ricean fading is distorted

• [Viallanese00] and [Obayashi98] used ray tracing techniques ➡Cannot be used in network simulations

➡Assumed constant shadowing, which we show is not the case


Shadowing loss due to Single Person

2 ft2 ft

Rx

Tx

No Movement Close to Receiver

Observation 2:Observation 2:Absolute value of Absolute value of

shadowing shadowing depends on depends on

distance from distance from receiverreceiver

Observation 1:Observation 1:Shadowing upto Shadowing upto

15dB due to 15dB due to presence of presence of

single personsingle person


Shadowing Loss by Multiple People

Observation 3: Shadowing loss due to blocking of line-of-sight cause fading which is dependent on the relative distance of people from the receiver and transmitter and also between

themselves.

Case I Case II Case III


Channel Fading in Presence of Environmental Mobility

• Observation 4:

–The fading distribution is distorted to exhibit a secondary peak

–The relative magnitude of second peak depends on the number of people and their speed.


Modeling

• Model for Shadowing Loss

– Use modified Fresnel theory

– Modifications for multiple people case

– Validate the models with experimental data

• Model for Channel Fading

– Two state Markov process

– Monte-Carlo simulations to obtain model parameters

– Validation


Two State Markov Process Fading Model

• Two states correspond to two peaks

• In both states, fading is Ricean distributed but with different K values

Good StateRSSI = Tx_Power

– Lpathloss

– Ricean_Fading(K)

Good StateRSSI = Tx_Power

– Lpathloss

– Ricean_Fading(K)

Shadowed StateRSSI = Tx_Power

– Lpathloss

– Ldiffraction

– Ricean_Fading(K’)

Shadowed StateRSSI = Tx_Power

– Lpathloss

– Ldiffraction

– Ricean_Fading(K’)

Good Channel

State

Shadowed Channel StateL: Diffraction

Loss

λ

μ


Validation


MAC Data Rate Adaptation

• Observation 5–Link throughput is

degraded

–Large fraction of packets are sent at lower rate

–Dependent not only on density and speed of people, but also on link range


Sensitivity to External Interference


Recap

• Measurement results on environmental mobility

– Shadowing loss depends on location of people with respect to Tx and Rx and with each other

– Fading is distorted to exhibit secondary peak

• Modeling

– Three knife-edge diffraction model

– Two state Markov-process fading model

• Effects on protocol stack

– Link throughput is degraded - more packets are sent at lower rate

– Links become more sensitive to interference

– Routes become more unstable


Case Study: Using Hybrid Testbed

• Problem: Better understand impact of system-related overheads on performance of two reliable transport protocols (TCP and XCP) over wireless links– E.g. wireless NIC, OS, node hardware, system config. parameters

– Largely overlooked in performance evaluation studies

– Bigger impact expected with new radio technology, e.g. soft radio

Network Application

Operating System

Network Protocols

Network Devices (NIC)

Network Application

Operating System

Network Protocols

Network Devices (NIC)


Related Work

• Impact of network subsystem studied in high-speed wired networks– Memory copy and other OS operations identified as dominant TCP

processing overhead. (Clark et al, IEEE Comm. Magazine, 1989)

• Impact of channel effects extensively studied in wireless systems– Effectiveness of various protocol designs subject to channel effects.

(Choi et al. , Proc. ACM Sigmetrics, 2005)

• Impact of system-dependent overhead on wireless network systems investigated from energy consumption perspective– Quantification of energy consumption of various wireless NICs in

MANET. (Feeney et al., Proc. IEEE Infocom, 2001)– Quantification of computational energy consumption of various

function involved in TCP processing, (Wang et al., Proc. IEEE Infocom, 2004)


Approach

• Identifying potential effects in the network subsystem of end hosts– Wireless NICs– Operating System and Node Hardware– System Configuration Parameters

• Isolating and quantifying impact of individual components in the network subsystem

• Eliminating influence of wireless channel effects• Improving fidelity of simulation models for wireless

system and application performance evaluation


Experimental Setup• Transport protocol: TCP and XCP

• Traffic model: bulk file transfer

• Topology: – One-hop wireless LAN

– Wireless LAN with one wired link

Platform

Platform1: Dell Latitude D600 (1.6GHz, 512MB@266MHz)

Platform2: IBM ThinkPad T43 (1.8GHz, 512MB@400MHz)

OSOS1: Linux Fedora Core 3 (2.6.9)

OS2: Linux Red Hat 9.0 (2.4.20)

802.11 NIC

NIC1: Proxim Gold 11b/g (Atheros, MADWIFI)

NIC2: Linksys WPC11 v3.0 (Intersil Prism, HostAP)

NIC3: Intel PRO/Wireless 2200BG (Intel, IPW2200)


Impact of Wireless NICs

• Performance difference due to NIC-related overhead comparable to that of protocol-specific parameters, e.g. preamble length.– Significant differences in min RTT observed in both

scenarios

• Relative performance between TCP and XCP is NIC-dependent

Short Preamble Long Preamble


• Impact of OS on TCP throughput

• Impact of Node Hardware on TCP throughput

Impact of OS and Node Hardware


Sensitivity to System Configuration Parameters

• TCP/XCP performance is sensitive to system configuration parameters, which are not captured by existing wireless network simulators

Field Measurement (Download Flow) SimulationField Measurement (Upload Flow)


Modeling Impact of Network Subsystem (I)

• Improved simulation modeling– Wireless network simulator: QualNet

– Modeling of NIC-dependent overhead


Future Work

• Scaling Multi-Paradigm Framework:– Extend scalability via parallel computation & fine-

grained emulation

• Heterogeneous networking– Incorporate sensor networks and vehicle-vehicle

networks

• Environmental Mobility –Integration with nodal mobility

–Other data rate adaptation algorithms and routing protocols

–How can protocols be aware of environmental mobility and can dynamically adapt to improve performance?

Documents

Scaling Issues for Multi-Paradigm Network Modeling Rajive L. Bagrodia Professor Computer Science Dept UCLA [email protected]