Mario Gerla Current 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)

Cellular Vs Multihop Ad Hoc, Multihop wireless Networks Base 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 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

How to deal with remote destination inaccuracy? Landmark Routing Landmark Logical Subnet

Snapshot A B C D H I J KL 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 Flow Control Routing IP Wrapper IP/Mobile IP RSVP Transport Wrapper TCP/UDP Control Channel Radio MAC Layer Network IP Transport Application RTP WrapperRCTP Packet Store/Forward Clustering Routing Link Layer Application Setup Data Plane Control Plane

Ad Hoc, Personal Networking with Bluetooth headset cell phone storage palmtop PDA

What Is Bluetooth? Personal Ad-hoc Networks Cable Replacement Landline Data/Voice Access 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 reduced Increase in jitter network congested channel noise/interference Piggybacked Text Stream (UDP) server TTS Sync Speech Recognition

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 Application Server High bandwidth Intranet Middleware Servers Middleware Servers Middleware Servers

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 = 20 D = 25, BW = 55 D = 5, BW = 90 D = 3, BW = 105 D = 5, BW = 90 D = 1, BW = 90 D = 5, BW = 90 D = 2, BW = 90 D = 5, BW = 90 D = 14, BW = 90 Constraints: Delay (D) = 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 Succeeds!! (Total delay = 24 30) 5 Hop Path > Do not consider, although (Total Delay = 16 30) A B D = 30, BW = 20 D = 25, BW = 55 D = 5, BW = 90 D = 3, BW = 105 D = 5, BW = 90 D = 1, BW = 90 D = 5, BW = 90 D = 2, BW = 90 D = 5, BW = 90 D = 14, BW = 90 Constraints: Delay (D) = 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