The Networking Lab in the School of Computing (and how we are helping to evolve broadband access technology) 1 Dr. Jim Martin Associate Professor School

The Networking Lab in the School of Computing(and how we are helping to evolve broadband access

technology)

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Dr. Jim MartinAssociate ProfessorSchool of ComputingClemson [email protected]://www.cs.clemson.edu/~jmarty

Networking Lab’s Website: http://www.cs.clemson.edu/~jmarty/netlab/

Don’t worry Homer, the guys/girls in the Networking Lab will fix things!!

WooHoo….I don’t know what this means

There are no more /8 TCP/IP V4 Addresses!!

Research Group’s Mission• Vision Statement:

– Computing and the Internet are converging– Traditional broadcast video (Cable/Satellite) is converging with the Internet– Networks are becoming more and more ‘heterogeneous’– The scope of the lab’s interests is more than networking, it includes operating systems, distributed

systems, secure and trustworthy systems, and next generation Internet. • Collectively these define the term ‘cybersystems’

• The networking group focuses on a range of problems that are at the heart of developing and analyzing emerging cybersystems. The mission of the lab is to support cutting edge research in cybersystems AND to train researchers to address the needs of the changing world.

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Now that’s what I’m talking about!

Brief Introduction

• The Networking Group focuses on a range of problems in the area of computer networking. The focus the last several years has been on broadband Internet access.

– Web site: http://www.cs.clemson.edu/~jmarty/netlab/• The CyberInfrastructure Group focuses on cloud computing, cluster systems, virtualization, high-

throughput computing, high performance computing, and grid computing systems.– Web site: http://www.ciresearchgroup.org/

• Funding– NSF, Cisco, CableLabs, Department of Justice/National Institute of Justice, NASA, IBM

• Team– Faculty: Jim Martin, Mike Westall, Sebastien Goesguen, Brian Dean, Juan Gilbert, KC Wang,

Harlan Russel, Richard Brooks– Students: Rahul Amin, Yunhui Fu, Gongbing Hong, and many MS and undergrad students

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NETLAB Activities

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Research Pedagogy Outreach

Wireless Systems

Broadband Access

Internet Protocols and Issues

Networking and Systems Course Development

VM-based labs

Emerging Scholars

CyberTiger Creative Inquiry

Statewide Broadband Wireless Initiatives

Networking Seminar

CyberTigerBroadband Service Website

Testbeds and Experimental Deployments

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CyberInfrastructure

CyberSystems Research

ApplicationDomain (e.g., connected vehicle)

Trustworthy computing

System Performance

Computational Theory Algorithms Software Engineering

CyberInfrastructure: The hardware/software systems that operate harmoniously to meet the requirements of domain specific applications and systems. CI includes operating systems, networks, distributed systems, secure and trustworthy systems, HPC.

Common Theme For All Lab Research

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• Resource Allocation: The process by which network elements try to meet the competing demands that applications have for network resources

• Broadband access: • FCC: "Internet access that is

always on and faster than the traditional dial-up access“

• The edges of the Internet

– Cooperative Wireless Hetnets– Supporting Video Multicasting in Wireless Crowd Spot Locations– Internet Video Streaming (IPTV??) using Dynamic Adaptive Streaming

over HTTP (DASH)– CyberTiger Broadband Mapping

Building Cooperative Heterogeneous Wireless Networks With Re-Configurable

Devices

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InternetAccess Network Exit

SmartPhoneGlobal Resource Controller

Autonomous Wireless Systems

Open Spectrum

• Future handhelds will contain multiple radios that can be used concurrently AND that are reconfigurable.

• Future wireless networks will be heterogeneous with cooperative mechanisms in place (early examples are femtocells and WiFi off loading)

Research:•Problem today is wireless systems are operated independently•Our work is finding practical methods for building heterogeneous wireless systems based on cooperative AWSs•Research has been primarily analysis driven (analytic and simulation) although we are moving towards a prototype

Introducing…crowd spots• A crowd spot involves hundreds, thousands, or tens of thousands of people (and wireless devices) temporarily

grouped together in dense formation.

– Drivers: deployment of smartphones, move towards multi-modal devices, availability of infrastructure

– Of particular interest are sports and entertainment venues

• 802.11 has supported large events since the early 1990’s.

– Many studies point out the deficiencies of 802.11b – it does not scale.

• Several works found many handoffs cause service interruption without ever moving the user (the device connects back to the same AP >50% of the time in one study)

• Crowd spots supported on managed networks – society is evolving….

– Satellite to mobile devices – early form of the concept

– NASCAR events provide handheld device video using Sprint’s licensed spectrum (now considered ‘outdated’

• Wireless carriers recognize the need to support crowd spots – one way is to offload application data onto WiFi.

• Economic models are being invented…..

• Another approach to support crowd spots – multicast!!

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Stream of UDP packets APFEC - coder

Video streaming encoder

Network

Stream including redundant information

Video content (avi, mp4)

1234512345r1

APFEC -decoder

Stream – possibly lost packets

1 3 4 5 r1

1 3 4 52

Video streaming decoder

Performance assessment (loss rate, latency, jitter)

Video quality assessment (PSNR, mean time between artifacts)

Application FEC Model (Crowd Spots …)

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FEC Send Side

FEC Rx Side

CBR traffic generator

CBR traffic generator

Introducing…crowd spots

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Research:

•Problem today is wireless systems are operated independently – crowd spots break all engineering assumptions•Cisco funded us to explore video streaming based on multicast and Application Forward Error Correction ….. (APFEC defined on next slide)•Research issue with APFEC:

• APFEC works well if tuned to match network conditions• Lots of work that adapts APFEC parameters to track changing conditions in a

single network • No one has considered crowd spots OR wireless hetnets

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•Today the majority of US internet bandwidth during prime time is video, and the majority of that is adaptively streamed long form content such as TV and movies•Cisco projects video will be over 90% of internet bandwidth by 2014 – the Internet will mainly become a video network•Content Delivery Networks (CDNs) make this possible by “edge caching” frequently viewed content•Adaptive streaming has been engineered for efficient edge caching, and to withstand network congestion and unreliable network bandwidth and latency

•DASH is a standard application protocol that allows one content provider to support Internet streaming on all devices

Dynamic Adaptive Streaming with HTTP

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•A segment is an independent, viewable period of video/audio/timing data..•Segment sizes of 2 seconds or 10 seconds are reasonable.•Segments are uniquely identified by an HTTP URL.•A client requests the segment, the bit rate, and optionally a specific byte range in the segment.•Clients can issue requests and receive segments over any number of concurrent TCP connections.•The video segment is sent back by the HTTP server in a ‘burst’.•The implementation of the client determines how frequently segments are requested, when bit rate adaptation occurs.


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HTTP Server

DASH Client

Playback buffer queues segments• clientbuffersize: Capacity in time, • maxSegments: max queue size (segs)• maxSize: max buffer size (bytes)• Highwatermark:• Lowwatermark:

Representations

Player

Controller

HTTP Get Requests{SegmentNumber,BitRate}

Segment data

Controller parameters• Throughput Monitor Timescale• Segment size in seconds• Number of outstanding requests

Monitor Arrival Process

Rendering

Performance updates

… …

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•The figure shows the throughput consumed by a Netflix stream.•We see several levels of video quality•The input to the plotting program is dataset1.dat•Write a program that reads an input file of throughput samples. Devise a method by which the program is able to identify the transitions

500 1000 1500 2000 2500 30000

1

2

3

4

5

6

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x 106 TCP Cx Dynamics

Time (seconds)

Bits

/sec

ond


0 100 200 300 400 500 600 700 800 9000

2

4

6

8

10

12

14

16

18

20

Time (seconds)

Thr

oug

hp

ut (

Mb

ps)

TCP Cx 1

2.0 second samples50 second samples

Steady State 1

Steady State 2 Steady State 4

0 % Artificial Loss 0 % Artificial Loss

3% Artificial Loss

http://www.cs.clemson.edu/~jmarty/courses/matlabTutorial/procDASHStates.m

State Changes:125.000000 STATE CHANGE TO 1 325.000000 STATE CHANGE TO 0 375.000000 STATE CHANGE TO 1 425.000000 STATE CHANGE TO 0 525.000000 STATE CHANGE TO 1 775.000000 STATE CHANGE TO 0 825.000000 STATE CHANGE TO 1 875.000000 STATE CHANGE TO 0

Steady State 3


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Internet

Linux RouterCentOS+

Netem

Xbox Wireless

Netflix Clemson’s Network

Windows Wired

Trace Point (tcpdump)

Netflix Server

Xbox Wired

WiFi

Linux Traffic Generator 3

Roku Wireless


Linux Traffic Generator 1Client Devices1.Xbox Wired2.Windows Wired3.Xbox WiFi4.Roku WiFi5.Android WiFi

Android Droid Razr


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Internet

Linux RouterCentOS+

Netem

Xbox Wireless


Windows Wired


Netflix Server

Xbox Wired

WiFi


Roku Wireless



Android Droid Razr


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Internet

Linux RouterCentOS+

Netem

Xbox Wireless


Windows Wired


Netflix Server

Xbox Wired

WiFi


Roku Wireless



Android Droid Razr


Roku Wireless, Downstream TCP

0 100 200 300 400 500 600 700 8000

5

10

15

20 TCP Cx 1

Time (seconds)

Thr

oug

hp

ut (

Mb

ps)

2.0 second samples30 second samples

0 100 200 300 400 500 600 700 8000

20

40

60

TCP Cx 2

Time (seconds)

Thr

oug

hp

ut (

Mb

ps)

Research:•Problem is there are large deployments of high bandwidth, adaptive applications….•Research Issues include:

• Optimal DASH control decisions• Predicting future available

bandwidth allocations• Size of the playback buffer• Sensitivity of the adaptation

• Impacts on Internet fairness


CyberTiger: Broadband Mapping• Use various metrics to evaluate the broadband wireless

coverage of locations ‘out in the wild’• Examples:

– Ekahau HeatMapper• Creates heat map of Wifi coverage in an area

– OpenSignalMaps• Crowdsourced data via Android app• Heat map, signal strength only

– Root Metrics • Phone app to perform tests and plot data• Limited in metrics and no Wifi network support• Does not provide user with a personal map

– MobiPerf • University of Michigan researchers• Many metrics consolidated in to a single test• Mobile networks, plotted as a heat map• Does not provide user with a personal map

– FCC’s Broadband Mapping project is soon to start a broadband wireless effort (Wired Access work is http://www.broadbandmap.gov/)

• Outreach for our research program• Audit the claims of broadband providers• Use user-collected or “Crowdsourced” data to

generate a publicly available universal wireless coverage map

• Our perspective is unique:– Application (End User) oriented assessment– Focus is on wireless hetnets

CyberTiger: Broadband Mapping


Research:•How to ‘normalize’ the results over a range of Radio Access Technologies?•‘Big Data’ problem : how to visualize the data, mining the data for technical or human oriented insights•How to sample the system in an accurate yet efficient manner?•As wireless systems become more cooperative, what metrics can be used to assess system operation?

Broader Impacts:•provide broadband wireless users the ability to assess system they are getting what they paid for•Provides technology that can be used by grass roots initiatives to improve broadband coverage to all demographics

Wrap Up…..Final Message– The Internet is evolving because of technology but also because of economic and societal

change– The evolution of broadband access is happening quickly

• Convergence of cable/over-the-air broadcasting, the Internet, and Mobile devices• Convergence of applications, operating systems, and the Internet

– Human Centered Computing is very evident in the Internet…. As networks connect humans, the effects of HCC are social interactions, social-systems effects, and the subsequent economics that attempt to capitalize on how things play out.

– Final thoughts:• Our broad focus is to develop theoretical yes practical frameworks for developing

and analyzing future broadband access networks.• Preparation for research in networking might include

– required: CPSC851,852,854, – helpful: math classes on optimization, random processes, ECE 848.

• Please contact me if you would like to discuss our group’s direction

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Appendix

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Carrier 2: WiFi

Carrier 1: WiFi

Carrier 1: WiFi

Carrier 1: WiFi

Carrier 2: WiFi

Carrier 2: WiFi

Carrier 1:

WiMAX

Carrier 2: LTE

Carrier 2:

HSPA

Carrier 1:

EVDO


Devices

• Which user should connect to which technology? • Letting the user decide leads to sub-optimal performance because the user is unaware of current network conditions• Even letting the access technologies independently decide on how they should distribute their own resources to each user leads to sub-optimal performance • Solution: A centralized Global Resource Controller (GRC) should co-ordinate with all access technologies to come up with this decision. GRC can optimize network-wide metrics of interest such as overall system throughput and user fairness

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Devices

– Estimate the level of correlated loss using the Mean Burst Loss Length Metric

• The MBL estimates the 1/r parameter assuming the loss process can be modeled by a two-state GE model. Given a loss event

• Given that a loss event occurs, the MBL describes the average number of consecutive packets that are dropped

– APFEC Effectiveness

– Channel Zapping Time: the amount of time it takes to fill the playback buffer.

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a. Bernoulli loss model

b. GE loss model (1/r = 25 packets)

c. Channel Zapping Time (seconds)


• Clients: C++, Android• Server: C++ & MySQL• Visualizer: Python & Javascript



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The Networking Lab in the School of Computing (and how we are helping to evolve broadband access technology) 1 Dr. Jim Martin Associate Professor School