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"

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 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.

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

5. University of Kansas A KTEC Center of Excellence 5 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

6. University of Kansas A KTEC Center of Excellence 6 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

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

8. University of Kansas A KTEC Center of Excellence 8 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

9. University of Kansas A KTEC Center of Excellence 9 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

10. University of Kansas A KTEC Center of Excellence 10 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

11. University of Kansas A KTEC Center of Excellence 11 Other Networks States Disconnected state Period of consecutive packet losses > Ψ Burst loss state ξ< Period of consecutive packet losses < Ψ 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

12. University of Kansas A KTEC Center of Excellence 12 Measurement data

13. University of Kansas A KTEC Center of Excellence 13 Congestion Events observed over a period of one week (DC1)

14. University of Kansas A KTEC Center of Excellence 14 Statistics of user-perceived impairments

15. University of Kansas A KTEC Center of Excellence 15 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.

16. University of Kansas A KTEC Center of Excellence 16 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, accepted for publication in International Journal of Communication Systems.