Testbed in Network Coding 学生：李腾飞 导师：舒炎泰. Background Bob and Alice Relay...

Testbed in Network Coding

学生：李腾飞导师：舒炎泰

Background

• Bob and Alice

Relay

Require 4 transmissions

Alice Bob

Background

• Bob and Alice

Relay

Require 3 transmissions

XOR

XORXOR

Alice Bob

Outline

• MIT Testbed (COPE,MORE,MIXIT)• Toronto• Aalborg-Denmark• Harvard(Rainbow)• What can we learn from?

MIT-Testbed

Outline• Objective & Function• Configuration• Work & Paper on Network Coding

Objective & Function

• Build a two-floors Indoor Testbed• First putting network coding into practice• Mainly for test Network Coding

Routing/Mac/Phyical Layer Algorithm(wireless 802.11a/b/g,zigbee, etc ) on Laptop

• Large number of Nodes support(about 30)

Configuration

Software:• System is Linux,and using Click Routing

Module[1] toolkit send 802.11a/b/g tcp and udp datagram

• Implement with Srcr,EXOR and other classic Routing or Mac Layer Algorithm

Configuration(2)

Hardware:• 802.11a/b/g wireless card with an omni-directional

antenna (MIXIT use zigbee(802.15))• Cards based on the NETGEAR 2.4&5GHz 802.11a/g

chipset(or NETGEAR WAG311 802.11chipset)• RTS/CTS disabled• Power level : Adjustable• Mode: Adjustable

Testbed Work & Paper on Network Coding• COPE[2](Sigcomm 06)• MORE[3](Sigcomm 07)• MIXIT[4](Sigcomm 08)

COPE(Coding Opportunistically)

• Consider multiple unicast flows– Generalize Alice-Bob scenario

• Exploits Shared Nature of Wireless Medium– Store Overheard Packets for Short Time– These packets are used for decoding perspective

packets • First implement Wireless Network Coding in

the real world

MIT-MORE

• Spatial reuse and thus underutilize the wireless medium.

• MAC-independent opportunistic routing protocol• The first intra flow (single flow) in Network Coding• It combines random network coding with

opportunistic routing to address its current limitations.

MIT-MIXIT

• Not apply an error detection code• Use Physical Layer Hint to guess bit error/right• Cross-layer • Most Based on More

Outline

• MIT Testbed (COPE,MORE,MIXIT)• Toronto• Aalborg-Denmark• Harvard(Rainbow)• What can we learn from?

Toronto Testbed Hardware• NVIDIA GTX 280 Graphics Process Unit, 240 computing

cores.• NVIDIA GeForce 8800 GT GPU with 112 cores, which is

supported by the CUDA platform.• 8-core Intel Xeon serverSoftware• NVIDIA’s Tesla GPU architecture• C language using the Compute Unified Device

Architecture (CUDA) programming model and development tools

Work & Paper on Network Coding

• Parallelized Progressive Network Coding With Hardware Acceleration[5](IWQOS07)

• Nuclei: Graphics accelerated Many-core Network Coding[6](Infocom 09)

• Pushing the Envelope:Extreme Network Coding on the GPU[7]( ICDCS 09)

• UUSEE[8](Infocom 2010)

Parallelized Progressive Network Coding

• hardware acceleration• Take advantage of symmetric multiprocessor

(SMP) systems• packaged as a C++ class library

Platform comparison of coding performance at (n = 128, k = 4 KB).

Nuclei: GPU-accelerated Many-core Network Coding

• Hundreds of computing cores in GPU• Not affected by competing threads and

background tasks• combined CPU-GPU encoding & decoding

Pushing the Envelope: Extreme Network Coding on the GPU

• Super GPU set CPU free• Table-based encoding technique• parallel decoding ofmultiple segments

UUSEE

Objectives• Minimized server bandwidth costs.• Minimized buffering delay after a random seek• Consistently satisfactory playback quality

Outline

• MIT Testbed (COPE,MORE,MIXIT)• Toronto• Aalborg-Denmark• Harvard(Rainbow)• What can we learn from?

Aalborg University

Outline• Objective & Function• Configuration• Work & Paper on Network Coding

Objective & Function

• Mainly Build a Mobile PhoneTestbed• Easy for movement Scene• Mainly for wireless Network Research Work.• Nearly 150 Papers in recent 10 years(most on this

Testbed)• Recently years most of Testbed work is about

Network Coding

Configuration-Ex

Hardware:• Nokia N810 Internet Tablet Large Screen ,for

Visualization• WLAN Interface• Processor - TI OMAP 2420, 400 MHz ARM11.

Configuration(2)

Software:• Operating System - Maemo1 OS2008 (Linux kernel 2.6.21-

omap1)• Cross-compilation toolkit Scratchbox• SDK:Maemo SDK

Not just N810

• Nokia N95-8GB, ARM 11 332 MHz CPU, 128 MB ram,Symbian OS 9.2.

support IEEE802.11b/g

Lots of work on it!

• Laptop:Lenovo T61p, 2.53 GHz Intel Core2Duo, 2 GB ram,Kubuntu 8.10 64bit.

Work & Paper on Network Coding

• Cautious View on Network Coding - From Theory to Practice“ JCN 2008• Evolutionary Theory for Cluster Head Election in Cooperative Clusters impl

ementing Network Coding", Europe Wireless 2009

• Implementation and Performance Evaluation of Network Coding for Cooperative Mobile Devices“ ICC2008

• Implementation of Random Linear Network Coding on OpenGL-enabled Graphics Cards Europe Wireless 2009

• Network Coding Opportunities for Wireless Grids Formed by Mobile Devices ICST 2008

• Network Coding for Mobile Devices - Systematic Binary Random Rateless Codes ICC09

• …

Outline

• MIT Testbed (COPE,MORE,MIXIT)• Toronto• Aalborg-Denmark• Harvard(Rainbow)• What can we learn from?

Harvard-Rainbow

• MAC priority scheme• Priority computed by the information collect

from neighbor,decide the rate of TX• Priority based on the rank of coefficient matrix

of the Buffer of node• Network Coding scheme for the outgoing data

at each node.

Rainbow-Testbed

• 29 OLPC Beta-2[9] nodes wireless testbed• Outdoor Experiment(wireless interference (802.11)is

small compare with indoor)• Broadcast Ethernet packets at the 2Mbit/s rate for

all protocol implementations• The size of the file we distributed was 6.1 MBytes,

which at the 1.7 Mbit/s link rate of our testbed takes about 30 seconds to transfer.We limited the experiment run time to 300 seconds.

Hardware• i386 compatible systems based on the AMD

Geode GX processor running at 366MHz, and equipped with 128MB RAM.

• Each node has one Marvell Libertas 88W8388 802.11b/g radio, with tunable transmit power.

Harvard Implementation,We can learn ?

Developed implementation:• Test Application:GUI has been implemented to show the

distribution of packets• Framework:A Virtual Layer between MAC and IP Layer,just

call basic Berkely Function,easy for implement• Logistics Platform:It contains all the data structures and

functions for the logistics of network coding.• Schemes:This level is the algorithms for encoding and

decoding. One scheme for reliable broadcast, and one for network coding.

Outline

• MIT Testbed (COPE,MORE,MIXIT)• Toronto• Aalborg-Denmark• Harvard(Rainbow)• What can we learn from?

Testbed Objective

Architectural objectives• Research Requirements• Fast control connectivity and easy management• Flexible wireless components• Extendability• Financial cost• ……

Research Requirements

• Be able to observe findings that have been published in the past.(reproductive)

• Indoor and Outdoor Experiments• New Idea

Fast control connectivity and easy management

• Node with more number and kinds of interfaces• NFS Mounting Strategy

– All Update link to the server– Remote turn off the node?– Central Control

Flexible wireless components

• hardware and software should support modifications

• Wifi Cards Driver should be opensource(or Partly Open)

• Click Modular Router software framework is a good idea.

• Linux-based wireless applications are used

The Driver-chipset Architecture

Example

• Three open-source Linux drivers available today.

Click

• Refer:http://read.cs.ucla.edu/click/• MIT and many University using Click• modular software based router approach. The

components of Click are packet processing modules called elements.

Extendability

• Multiply Interface for future Application• Big waterproof box,for future more Device• Through NFS ,Software could be easy for

Update

Financial cost

• Complicate Problem

References• [1] http://read.cs.ucla.edu/click/• [2] Sachin Katti, Hariharan Rahul, Wenjun Hu, Dina Katabi, Muriel

Medard, and Jon Crowcroft "XORs In The Air: Practical Wireless Network Coding," ACM SIGCOMM, 2006.

• [3] Szymon Chachulski, Michael Jennings, Sachin Katti, and Dina Katabi, "Trading Structure for Randomness in Wireless Opportunistic Routing," ACM SIGCOMM, 2007.

• [4] Sachin Katti, Dina Katabi, Hari Balakrishnan, and Muriel Medard, "Symbol-Level Network Coding for Wireless Mesh Networks," ACM SIGCOMM, 2008.

• [5] H. Shojania and B. Li, “Parallelized Network Coding With Hardware Acceleration,” in Proc. of the 15th IEEE International Workshop on Quality of Service (IWQoS), 2007.

• [6] H. Shojania, B. Li, and X. Wang, “Nuclei: Graphicsaccelerated Many-core Network Coding,” in Proc. of IEEE INFOCOM 2009, August 2009.

References[7] Hassan Shojania, Baochun Li. "Pushing the Envelope: Extreme Network

Coding on the GPU," to appear in the Proceedings of the 29th International Conference on Distributed Computing Systems (ICDCS 2009), Montreal Canada, June 22-26, 2009.

[8] Zimu Liu, Chuan Wu, Baochun Li, Shuqiao Zhao. "UUSee: Large-Scale Operational On-Demand Streaming with Random Network Coding," to appear in the Proceedings of IEEE INFOCOM 2010, San Diego, California, March 15-19, 2010.

[9] http://zh.wikipedia.org/wiki/OLPC