internet qos umkc frost

8/9/2019 Internet QoS UMKC Frost

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University of KansasA KTEC Center of Excellence 1

Soshant Bali*, Yasong Jin**, Victor S. Frost* andTyrone Duncan**

Information and Telecommunication Technology Center*Electrical Engineering & Computer Science

**Department of [email protected], 785-864-4833

A New Perspective on Internet

Quality of Service: Measurement andPredictions


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Outline

Develop end-to-end measurement techniques Develop prediction methodologies for fBM

traffic

A Few Words about our Graduate andResearch Programs at EECS@KU


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Premise

Voice networks had a very understandable QoS metric-Blocking Internet QoS metrics must correlate to end-user experience. Metrics such as delay and loss may have little direct meaning

to the end-user because knowledge of specific coding and/oradaptive techniques is required to translate delay and loss tothe user-perceived performance.

Detecting observable impairments must be independent ofcoding, adaptive playout or packet loss concealment techniquesemployed by the multimedia applications.

Time between impairments and their duration are metrics thatare easily understandable by network user.

This research developed methods to detect these impairmentevents using end-to-end measurements.


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Network states

Noticeable impairments for Real-time multi-media (RTM) services occur when the end-to-end connection is in one or more of thefollowing states: Burst loss, High random loss,

Disconnected,

High Delay.

Two other connection states are defined: Congested, Route change.


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Background End-to-end argument

end nodes: most functions implemented here including applicationspecific functions core: important forwarding and routing functions are implemented

here; not burdened by application specific functions, e.g., reliabledelivery

Anomalous events failures: fiber cuts, power failures etc. congestion cause user-perceived impairments

Inferring anomalous events from end-to-endobservations core nodes implement simple functions; do not inform end nodes of

anomalous events need to infer anomalous events from end-to-end observations

Several benefits if anomalous events are accuratelyinferred


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Significance

A new QoS metric for RTM applications ISPs can use impairments metric in service level agreements (SLAs)

Fault diagnosis tools for ISPs an alternative to traceroute for detecting layer 3 route changes

method for detecting layer 2 failures

Routing for overlay / content delivery networks Increasing TCP throughput

Confidence interval for minimum RTT estimate(byproduct)


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Goal

Given a set of active end-to-end networkmeasurements determine the networkstate and the temporal characteristics ofimpairment events

Network

Round Trip Time

Packet Loss Rate

Traceroute

Time-to-live

Network

State

Impairment Events:

-Frequency

-Duration


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Goal


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Route Change

Motivation Route changes can cause user perceived impairments

Need to divide observations into homogenous regions

Layer 3 route changes

TTL Traceroute

Not all route changes result in TTL change

Not all routers respond to ICMP massages for traceroute

Layer 2 route changes are not visible end-to-end


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Route change state

RTT based route change detection TTL change: not all route changes result in TTL change traceroute change: inefficient, not all routers respond to ICMP massages for traceroute both layer 2 and layer 3 route changes can be detected using RTT based route change

detection

in figure below, minimum RTT changed but traceroute and TTL field of IPheader did not change; layer 2 route change


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Route Change

Layer 2 Route ChangeIf

the time between changes > T

and the RTT difference acrossthe route change > RTT

and variation in RTT


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Congested State

Observed from M/M/1Queues

is an indicator of congestion

The end-to-end flow is in theCongested sate if:

Where

= Ave waiting time

= Packet loss rate


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Congested State

RTTs and a congestion event detected using the discussed procedure

planetlab2.ashburn.equinix.planet-lab.org and planetlab1.comet.columbia.edu, 2/04


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Delay Impairment State

Given the RTT data, anestimate is made of theminimum playout delaybuffer size that isneeded to avoid

excessive packet losses. If minimum playout

delay > Dplayout then a delayimpairment has

occurred.

Estimated one-way delays and

minimum playout delay

planetlab2.ashburn.equinix.planet-lab.org

and planetlab1.comet.columbia.edu

Feb, 2004


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Other Networks States

Disconnected state Period of consecutive packet losses > sec

Burst loss state sec < Period of consecutive packet losses < sec

High Random Loss State Insure enough observed losses, e.g., N, for valid loss

probability estimate, RoT N > 10

Observe N losses, if number of packets between the firstand Nth loss < NL then network in high lose state


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Measurement data


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Congestion Eventsobserved over a period of one week (DC1)


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Statistics of user-perceived impairments


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Other observations

Layer 2 route change 96 events were manually classified as layer 2 route changes

~71.8% layer 2 route changes were detected by thealgorithm

~4% of the detected events were false positives.

~8% of all layer 3 route changes werepreceded by burst or disconnect loss events.


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Summary of measurement results

mean time between impairments: from 3.52hrs to 268hrs

mean duration of impairments: from 4.4mins to 92.5mins on 2 paths congestion for 6-8 hrs during day (weekdays)

burst loss, high random loss and high delay events were observed whenconnection was in congested state

mean time between burst loss events that occurred during congestion = 14min, mean duration = 22.64 sec

mean time between layer 3 route changes = 7.23 hrs 18% of all layer 3 route changes 1 sec apart, 15% 2 sec apart, 80% less

than 45 mins apart 8% of all layer 3 route changes were preceeded by burst or disconnect

loss events mean duration of burst loss events that precede layer 3 route changes =

113.5 sec

mean time between layer 2 route changes = 58.22 hrs

none of the layer 2 route changes were preceded by burst loss events


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Experimental Conclusions Developed procedures to detect impairment

states for RTM services using end-to-endmeasurements.

Developed techniques to detect layer tworoute changes and congestion

The developed techniques consider multiplemetrics at the same time to infer thepresence of user perceived impairments.

Details in Characterizing User-perceived Impairment Events UsingEnd-to-End Measurements, Soshant Bali, Yasong Jin, V. S. Frost and T. Duncan,International Journal of Communication Systems.


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Predicting Properties of Congestion Events

QueueSize

inBits


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QueueSize

inBits


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Traffic Model fractional Brownian motion (fBm)

Qo(t) = Queue length at t

=Service rate

m=average input rate

a=variance of the input rate

BH(t)=standard fBm with parameter H

c=scaled surplus rate


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Sojourn Time


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Inter congestion event time


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Congestion duration


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Amplitude


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Conclusions

Developed methods to measure impairmentsusing end-to-end measurements Developed techniques to predict several

properties of congestion events for fBMtraffic: Rate, Duration, Amplitude For details see: Predicting Properties of Congestion Events

for a Queueing System with fBM Traffic, Yasong Jin,Soshant Bali. Tyrone Duncan, Victor S. Frost, acceptedpending revisions for the IEEE Transactions on Networking.

A F W d b t G d t


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A Few Words about our GraduateProgram at EECS@KU

37 faculty 4 Fellows of the IEEE Ex-Program Managers from DARPA, NSF, NASA 10 new faculty in the past 3 years Currently recruiting one more faculty member

MS degrees in EE, CoE, CS

150 MS students Ph.D. degrees in EE, CS

75 Ph.D. students

Two major research labs: ITTC and CReSIS Research volume of over $20 million, with research expenditures of $5.5

million in 2005 >50% of our graduate students are supported (over 140 in F05)

Almost all our Ph.D. students are supported


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EECS Research Space

Wh t S f O R t G d t


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What Some of Our Recent GraduatesAre Doing Now

Cory Beard (PhD EE 1999) Associate Professor UMKC

Jennifer Leopold (PhD CS 2000) - Professor of CS at Missouri, Rolla Amit Kulkarni (PhD CS 2000) - GE Global Research Center

Daniel Cliburn (PhD CS 2001) - Professor of CS at Hanover College

Nathan Goodman (PhD EE 2002) - Professor of ECE at the University of Arizona

Cindy Kong (PhD CS 2004) - Intel Corp.

Wesam Alanqar (PhD EE 2005) - Sprint Corp.

Jungwoo Ryoo (PhD CS 2005) - Professor at Arizona State University

David Janzen (PhD CS 2006) - Professor at Cal Poly, San Louis Obispo


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Ph.D. Focus Areas

Communication Systems and Networking Computer Systems Design

Interactive Intelligent Systems

Bioinformatics

Radar Systems and Remote Sensing


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Communication Systems and Networking

Advancing knowledge of systemsinterconnected via radio and othertechnologies

New methodologies to determine the

performance and protection of Internet-based systems

Theory and technologies that enable thedelivery of reliable information in support of

end-user applications independent of theaccess technology


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Computer Systems Design

Design of computing systems, ranging from small,embedded elements to large, distributed computingenvironments

All aspects of the system life cycle, includingspecification, verification, implementation and

synthesis, and testing and evaluation of bothhardware and software system components

Principle application area of embedded and real-timesystems with special emphasis on the interaction

between hardware and software system components


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Interactive Intelligent Systems

Create intelligent and interactive systems withsufficient intelligence to help humans accomplishimportant tasks

Multi-modal interfaces to respond intelligently touser requests, process and present large quantities

of information in many forms, and to perform taskswith minimal supervision Artificial intelligence, intelligent agents, information

retrieval, data mining, human-computer interaction,modeling, visualization, multimedia systems, androbotics


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Bioinformatics

Information technology to process, analyze,and present biological data in new,meaningful, and efficient ways

Knowledge discovery and data mining andanalysis as they relate to life sciences

Making key advances in bioinformaticsmethods and tools for genomics andproteomics data analysis and other life-sciences-related problems


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Radar Systems and Remote Sensing

Radars, microwaves, communications, andremote sensing technologies New ways to use electromagnetic waves in

the remote sensing of the land (surface andsubsurface), sea, polar ice, and theatmosphere

Developing new remote sensing sensors(primarily radar), and new methods forsolving electromagnetic problems


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FastTrack Ph.D.

Enter the Ph.D. program directly from theB.S.

Finish in 5 years

42 course credit hours past B.S.

Possible schedule:Semester 1 3 courses

Semester 2 3 courses

Semester 3 2-3 courses + research

Semesters 4-10 0-2 courses + research


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Deadlines

The application deadline is March 1st, but forfull consideration for fellowships andresearch/teaching assistantships,applications should be received by January

1st. For more details about the applicationprocess please see our graduate admissionspage.


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Websites

www.ittc.ku.edu www.cresis.ku.edu

www.eecs.ku.edu

www.ku.edu

internet qos umkc frost

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