1. University of Kansas A KTEC Center of Excellence 1 Soshant Bali *, Yasong Jin **, Victor S. Frost * and Tyrone Duncan ** Information and Telecommunication Technology Center *Electrical Engineering & Computer Science ** Department of Mathematics frost@eecs.ku.edu, 785-864-4833 A New Perspective on Internet Quality of Service: Measurement and Predictions

2. University of Kansas A KTEC Center of Excellence 2 What is the perceived QoS for this end-to-end path?

3. University of Kansas A KTEC Center of Excellence 3 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

4. University of Kansas A KTEC Center of Excellence 4 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.

5. University of Kansas A KTEC Center of Excellence 5 Network states Noticeable impairments for Real-time multi- media (RTM) services occur when the end- to-end connection is in one or more of the following states: Burst loss, High random loss, Disconnected, High Delay. Two other connection states are defined: Congested, Route change.

6. University of Kansas A KTEC Center of Excellence 6 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

7. University of Kansas A KTEC Center of Excellence 7 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)

8. University of Kansas A KTEC Center of Excellence 8 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

9. University of Kansas A KTEC Center of Excellence 9 Goal

10. University of Kansas A KTEC Center of Excellence 10 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

11. University of Kansas A KTEC Center of Excellence 11 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 IP header did not change; layer 2 route change

12. University of Kansas A KTEC Center of Excellence 12 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<V RTT –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)

13. University of Kansas A KTEC Center of Excellence 13 Congested State Observed from M/M/1 Queues is an indicator of congestion The end-to-end flow is in the Congested sate if: Where = Ave waiting time = Packet loss rate

14. University of Kansas A KTEC Center of Excellence 14 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

15. University of Kansas A KTEC Center of Excellence 15 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 > D playout then a delay impairment has occurred. Estimated one-way delays and minimum playout delay planetlab2.ashburn.equinix.planet-lab.org and planetlab1.comet.columbia.edu Feb, 2004

16. University of Kansas A KTEC Center of Excellence 16 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 N th loss < N L then network in high lose state

17. University of Kansas A KTEC Center of Excellence 17 Measurement data

18. University of Kansas A KTEC Center of Excellence 18 Congestion Events observed over a period of one week (DC1)

19. University of Kansas A KTEC Center of Excellence 19 Statistics of user-perceived impairments

20. University of Kansas A KTEC Center of Excellence 20 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.

21. University of Kansas A KTEC Center of Excellence 21 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 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 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

22. University of Kansas A KTEC Center of Excellence 22 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.

23. University of Kansas A KTEC Center of Excellence 23 Predicting Properties of Congestion Events Queue Size in Bits

24. University of Kansas A KTEC Center of Excellence 24 Predicting Properties of Congestion Events Queue Size in Bits

25. University of Kansas A KTEC Center of Excellence 25 Predicting Properties of Congestion Events Traffic Model  fractional Brownian motion (fBm) Q o (t) = Queue length at t  =Service rate m=average input rate a=variance of the input rate B H (t)=standard fBm with parameter H c=scaled surplus rate

26. University of Kansas A KTEC Center of Excellence 26 Sojourn Time

27. University of Kansas A KTEC Center of Excellence 27 Inter congestion event time

28. University of Kansas A KTEC Center of Excellence 28 Congestion duration

29. University of Kansas A KTEC Center of Excellence 29 Amplitude

30. University of Kansas A KTEC Center of Excellence 30 Conclusions Developed methods to measure impairments using end-to-end measurements Developed techniques to predict several properties of congestion events for fBM traffic: 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.

31. University of Kansas A KTEC Center of Excellence 31 A Few Words about our Graduate Program 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 F’05) Almost all our Ph.D. students are supported

32. University of Kansas A KTEC Center of Excellence 32 EECS Research Space

33. University of Kansas A KTEC Center of Excellence 33 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. W esam 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

34. University of Kansas A KTEC Center of Excellence 34 Ph.D. Focus Areas Communication Systems and Networking Computer Systems Design Interactive Intelligent Systems Bioinformatics Radar Systems and Remote Sensing

35. University of Kansas A KTEC Center of Excellence 35 Communication Systems and Networking Advancing knowledge of systems interconnected via radio and other technologies New methodologies to determine the performance and protection of Internet- based systems Theory and technologies that enable the delivery of reliable information in support of end-user applications independent of the access technology

36. University of Kansas A KTEC Center of Excellence 36 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

37. University of Kansas A KTEC Center of Excellence 37 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

38. University of Kansas A KTEC Center of Excellence 38 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 life- sciences-related problems

39. University of Kansas A KTEC Center of Excellence 39 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

40. University of Kansas A KTEC Center of Excellence 40 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 13 courses Semester 23 courses Semester 32-3 courses + research Semesters 4-100-2 courses + research

41. University of Kansas A KTEC Center of Excellence 41 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.

42. University of Kansas A KTEC Center of Excellence 42 Websites www.ittc.ku.edu www.cresis.ku.edu www.eecs.ku.edu www.ku.edu