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![Page 1: Mario Gerla Current Network Research Projects Ad hoc, wireless networks (DARPA, NSF, ONR) Wireless, mobile access to Internet (NSF, Intel) Internet : QoS.](https://reader036.fdocuments.in/reader036/viewer/2022062806/56649f585503460f94c7cf5d/html5/thumbnails/1.jpg)
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
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Cellular Vs Multihop
Ad Hoc, Multihop wireless Networks
Base BaseBase
Standard Base-Station Cellular Networks
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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)
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Conventional routing: Distance Vector
0
5
1
2
4
3
Destination Next Hop Distance
0 2 31 2 2… … …
Routing table at node 5 :
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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
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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
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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
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How to deal with remote destination inaccuracy? Landmark Routing
LandmarkLandmark
Logical SubnetLogical Subnet
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Snapshot
A
B
C D
HI
JK L
O
P
LM1
LM2
LM3
LM4
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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
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Ad Hoc, Personal Networking with Bluetooth
headset
cell phone
storage
palmtop
PDA
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What Is Bluetooth?
Personal Ad-hoc Personal Ad-hoc NetworksNetworks
Cable Cable ReplacementReplacement
Landline
Data/Voice Data/Voice Access PointsAccess Points
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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
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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
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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
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Hardware & Connectivity
ApplicationServer
High bandwidthIntranet
MiddlewareServers
MiddlewareServers
MiddlewareServers
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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
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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
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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
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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
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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
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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
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High Speed Networks Performance Measurement and Analysis
Mario Gerla and Medy Sanadidi
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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
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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
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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