Transcript A New Perspective on Internet Quality of Service

A New Perspective on Internet
Quality of Service: Measurement and
Predictions
Soshant Bali*, Yasong Jin**, Victor S. Frost* and
Tyrone Duncan**
Information and Telecommunication Technology Center
*Electrical Engineering & Computer Science
**Department of Mathematics
[email protected], 785-864-4833
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What is the perceived QoS for this
end-to-end path?
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Outline
• Develop end-to-end measurement techniques
• Develop prediction methodologies for fBM
traffic
• A Few Words about our Graduate and
Research 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/or
adaptive techniques is required to translate delay and loss to
the user-perceived performance.
• Detecting “observable impairments” must be independent of
coding, adaptive playout or packet loss concealment techniques
employed by the multimedia applications.
• Time between impairments and their duration are metrics that
are easily understandable by network user.
• This research developed methods to detect these impairment
events using end-to-end measurements.
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Network states
• Noticeable impairments for Real-time multimedia (RTM) services occur when the endto-end connection is in one or more of the
following states:
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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 application
specific functions
• core: important forwarding and routing functions are implemented
here; not burdened by application specific functions, e.g., reliable
delivery
• Anomalous events
• failures: fiber cuts, power failures etc.
• congestion
• cause user-perceived impairments
• Inferring anomalous events from end-to-end
observations
• 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 accurately
inferred
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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 network
measurements  determine the network
state and the temporal characteristics of
impairment 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
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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-toend
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Route change state
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RTT based route change detection
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in figure below, minimum RTT changed but traceroute and TTL field of IP
header did not change; layer 2 route change
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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
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Route Change
Layer 2 Route Change
If
– the time between changes > ΔT
– and the RTT difference across
the route change > ΔRTT
– and variation in RTT<VRTT
– Then the proposed algorithm
can detect the change
Route Change detected using the discussed procedure
(planetlab1.cambridge.intel-research.net and
planet1.berkeley.intel-research.net,
August 2004)
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Congested State
Observed from M/M/1
Queues
The end-to-end flow is in the
Congested sate if:
Where
= Ave waiting time
is an indicator of congestion
= 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, an
estimate is made of the
minimum playout delay
buffer size that is
needed to avoid
excessive packet losses.
• If minimum playout
delay > Dplayout then a
Estimated one-way delays and
minimum playout delay
delay impairment has
planetlab2.ashburn.equinix.planet-lab.org
occurred.
and planetlab1.comet.columbia.edu
Feb, 2004
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Other Networks States
• Disconnected state
• Period of consecutive packet losses > Ψ sec
• Burst loss state
• ξsec < Period of consecutive packet losses < Ψ 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 first
and Nth loss < NL then network in high lose state
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Measurement data
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Congestion Events
observed 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 the
algorithm
• ~4% of the detected events were false positives.
• ~8% of all layer 3 route changes were
preceded 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)
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burst loss, high random loss and high delay events were observed when
connection was in congested state
mean time between burst loss events that occurred during congestion = 14
min, mean duration = 22.64 sec
• mean time between layer 3 route changes = 7.23 hrs
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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-end
measurements.
• Developed techniques to detect layer two
route changes and congestion
• The developed techniques consider multiple
metrics at the same time to infer the
presence of user perceived impairments.
Details in “Characterizing User-perceived Impairment Events Using
End-to-End Measurements, Soshant Bali, Yasong Jin, V. S. Frost and T. Duncan,
International Journal of Communication Systems.
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Queue Size in Bits
Predicting Properties of Congestion Events
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Queue Size in Bits
Predicting Properties of Congestion Events
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Predicting Properties of Congestion Events
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Traffic Model fractional Brownian motion (fBm)
Qo(t) = Queue length at t
m=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 impairments
using end-to-end measurements
• Developed techniques to predict several
properties of congestion events for fBM
traffic:
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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, accepted
pending revisions for the IEEE Transactions on Networking.
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A Few Words about our Graduate
Program at EECS@KU
• 37 faculty
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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
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150 MS students
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75 Ph.D. students
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Almost all our Ph.D. students are supported
• MS degrees in EE, CoE, CS
• Ph.D. degrees in EE, CS
• 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 F’05)
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EECS Research Space
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What Some of Our Recent Graduates
Are 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
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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 systems
interconnected via radio and other
technologies
• New methodologies to determine the
performance and protection of Internetbased systems
• Theory and technologies that enable the
delivery of reliable information in support of
end-user applications independent of the
access technology
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Computer Systems Design
• Design of computing systems, ranging from small,
embedded elements to large, distributed computing
environments
• All aspects of the system life cycle, including
specification, verification, implementation and
synthesis, and testing and evaluation of both
hardware and software system components
• Principle application area of embedded and real-time
systems with special emphasis on the interaction
between hardware and software system components
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Interactive Intelligent Systems
• Create intelligent and interactive systems with
sufficient intelligence to help humans accomplish
important tasks
• Multi-modal interfaces to respond intelligently to
user requests, process and present large quantities
of information in many forms, and to perform tasks
with minimal supervision
• Artificial intelligence, intelligent agents, information
retrieval, data mining, human-computer interaction,
modeling, visualization, multimedia systems, and
robotics
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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 and
analysis as they relate to life sciences
• Making key advances in bioinformatics
methods and tools for genomics and
proteomics data analysis and other lifesciences-related problems
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Radar Systems and Remote Sensing
• Radars, microwaves, communications, and
remote sensing technologies
• New ways to use electromagnetic waves in
the remote sensing of the land (surface and
subsurface), sea, polar ice, and the
atmosphere
• Developing new remote sensing sensors
(primarily radar), and new methods for
solving electromagnetic problems
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FastTrack Ph.D.
• Enter the Ph.D. program directly from the
B.S.
• Finish in 5 years
• 42 course credit hours past B.S.
• Possible schedule:
Semester 1
Semester 2
3 courses
3 courses
Semester 3
Semesters 4-10
2-3 courses + research
0-2 courses + research
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Deadlines
• The application deadline is March 1st, but
for full consideration for fellowships and
research/teaching assistantships,
applications should be received by January
1st.
• For more details about the application
process please see our graduate admissions
page.
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Websites
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www.ittc.ku.edu
www.cresis.ku.edu
www.eecs.ku.edu
www.ku.edu
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