testbed in network coding 学生:李腾飞 导师:舒炎泰. background bob and alice relay...
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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