Mario GerlaCurrent Network Research Projects

• Ad hoc, wireless networks (DARPA, NSF, ONR)• Wireless, mobile access to Internet (NSF, Intel)

• Internet : QoS Routing and multicasting (CISCO, NASA, NSF)

• Internet control models: TCP (EPRI,NASA)

• Internet II: high speed traffic models and measurements (NSF, EPRI)

www.cs.ucla.edu/NRL

Cellular Vs Multihop

Ad Hoc, Multihop wireless Networks

Base BaseBase

Standard Base-Station Cellular Networks

Challenging problem: multihop routing

• mobility• need to scale to large numbers (100’s to 1000's)• unreliable radio channel (fading etc)• limited bandwidth• limited power• need to support multimedia (QoS)

Conventional routing: Distance Vector

0

5

1

2

4

3

Destination Next Hop Distance

0 2 31 2 2… … …

Routing table at node 5 :

Conventional wired routing limitations

• Distance Vector (eg, Bellman-Ford, DSDV):– routing control O/H linearly increasing with net size

– convergence problems (count to infinity); potential loops

CONVENTIONAL ROUTING DOES NOT SCALE TO SIZE AND MOBILITY

Fisheye State Routing

• Routing information is periodically exchanged with neighbors (as in Distance Vector)

• BUT: Routing update frequency decreases with distance to destination – Higher frequency updates within a small radius and lower frequency

updates to remote destinations

– Result: Highly accurate routing information about immediate neighborhood; progressively less detail for areas further away

Scope of Fisheye

1

2

3

4

5

67

8

9

9

10

11

12

14 1516 17

18 19

20

21

2223

2425

26

27

28

29

30

31

3234

35

36

Hop=1

Hop=2

Hop>2

13

How to deal with remote destination inaccuracy? Landmark Routing

LandmarkLandmark

Logical SubnetLogical Subnet

Snapshot

A

B

C D

HI

JK L

O

P

LM1

LM2

LM3

LM4

GloMoSim Simulation Layers

Application Processing

Propagation Model Mobility

Frame Processing Radio Status/Setup

CS/Radio SetupRTS/CTSFrame Wrapper

Ack/Flow Control

Clustering

Packet Store/Forward VC Handle

FlowControl Routing

IP Wrapper IP/Mobile IP

RSVPTransport Wrapper TCP/UDP Control

Channel

Radio

MAC Layer

Network

IP

Transport

Application

RTP Wrapper RCTP

Packet Store/Forward

Clustering

Routing

Link Layer

Application Setup

Data PlaneData Plane Control PlaneControl Plane

Ad Hoc, Personal Networking with Bluetooth

headset

cell phone

storage

palmtop

PDA

What Is Bluetooth?

Personal Ad-hoc Personal Ad-hoc NetworksNetworks

Cable Cable ReplacementReplacement

Landline

Data/Voice Data/Voice Access PointsAccess Points

Wireless Network

UCLA Adaptive Speech Experiment

Multihop Testbed

client

• Adjustable Parameters - sampling rate - packet size

• QoS Monitoring: - packet loss - jitter

Audio(UDP)

Control(TCP)

A d a p t a t I o n S t r a t e g y :

Audio source adapts to QoS feedback

Increase in Packet loss packet size is reduced

sampling rate is reducedIncrease in jitter network congested

channel noise/interference

Piggybacked Text Stream(UDP)

server

TTSSync

SpeechRecognition

iMASH: Interactive Mobile Application Support for Heterogeneous clients

CS: R. Bagrodia, M. Gerla, S. Lu, L. Zhang

Medical School: D. Valentino, M. McCoy

Campus Admin: A. Solomon

UCLA

Supported by NSF

Diverse Display Devices

Use of different devices for different components of medical care

Imaging Workstation: high-quality medical imagery and multimedia patient records

Medical Workstation: multimedia patient records, including moderate-resolution images

Mobile Medical Notes: for reviewing and taking medical notes

Physician’s PDA: for messaging and scheduling

Hardware & Connectivity

ApplicationServer

High bandwidthIntranet

MiddlewareServers

MiddlewareServers

MiddlewareServers

iMASH: Components

• Target application: Mobile physicians

• Middleware infrastructure to support anytime, anywhere, any-device access to electronic multimedia data

• Protocols to provide reliable QoS in a mobile, heterogeneous network

• Simulation/emulation capability to evaluate scalability of system to many users over large geographic areas

• Limited evaluation via deployment within UCLA medical school

QoS Routing and Multicast in wired nets

• Supported by CISCO and by NASA AMES• Intradomain environment• Quality of Service Routing/Multicast for

Real Time traffic (IP telephony,video etc)• Call Admission Control• Traffic load balancing

Example of QoS Routing

A

B

D = 30, BW = 20D = 25, BW = 55

D = 5, BW = 90

D = 3, BW = 105

D =

5, B

W =

90

D = 1, BW = 90

D = 5, B

W = 90

D =

2, B

W =

90

D = 5, BW = 90D = 14, BW = 90

Constraints: Delay (D) <= 25, Available Bandwidth (BW) >= 30

Multiple constraints QoS Routing

Given:

- a (real time) connection request with specified QoS requirements (e.g., Bdw, Delay, Jitter, packet loss, path reliability etc); examples: IP telephony, video streaming

Find:

- a min cost (typically min hop) path which satisfies such constraints

- if no feasible path found, reject the connection

2 Hop Path --------------> Fails (Total delay = 55 > 25 and Min. BW = 20 < 30)3 Hop Path ----------> Succeeds!! (Total delay = 24 < 25, and Min. BW = 90 > 30)5 Hop Path ----------> Do not consider, although (Total Delay = 16 < 25, Min. BW = 90 > 30)

A

B

D = 30, BW = 20D = 25, BW = 55

D = 5, BW = 90

D = 3, BW = 105

D =

5, B

W =

90

D = 1, BW = 90

D = 5, B

W = 90

D =

2, B

W =

90

D = 5, BW = 90D = 14, BW = 90

Constraints: Delay (D) <= 25, Available Bandwidth (BW) >= 30

We look for feasible path with least number of hops

Benefits of QoS Routing

Without QoS routing: • must probe path & backtrack; non optimal path, control traffic

and processing OH, latency

With QoS routing:• optimal route; “focused congestion” avoidance• more efficient Call Admission Control (at the source)• more efficient bandwidth allocation (per traffic class)• resource renegotiation possible

High Speed Networks Performance Measurement and Analysis

Mario Gerla and Medy Sanadidi

Project Focus

• High speed : backbone links at 2.4 Gbps and above, as in Abilene and vBNS

• Heterogeneous networks: wired and wireless• High performance distributed applications:

processor intensive, large data bases, high traffic volume, low latency

• Application performance : measure the network performance as perceived by network applications/users; tune protocols to improve performance

Example: Urban Simulation(R. Muntz and B. Jepson)

• Real-time visual simulation for design, urban planning, emergency response, and education

• Built Virtual Los Angeles model

• Challenge: remote/distributed access through high speed net

Current Measurement Activities

• TCP performance over wireless Internet access links (wireless LAN, satellite); wireless, lossy channel emulator; TCP Westwood

• Characterization of long range dependent traffic in the Internet; traffic generators

• Measure performance of dataView (3 D rendering of scientific data): impact of propagation time and network bottlenecks