1. Development of a QoE Model Himadeepa Karlapudi 03/07/03

2.  What is Quality of Experience (QoE)?  Why do we need QoE ?

3. How is QoE measured?  Predicting TCP throughput and obtaining other network observables  Converting network observables into representative inputs for the QoE model.  The QoE model gets these inputs and conveys the predicted QoE.

4. Predicting TCP throughput  Development of a model that is representative of majority of TCP traffic on the internet.  Three models have been used as a basis for the development of the new model. Amherst Model Amherst Model Roch Guerin’s Model Roch Guerin’s Model Cardwell’s Model Cardwell’s Model

5. Parameters considered for the development of the model  Network environment and nature of flows  Startup effects  Losses during connection establishment  Acknowledgement type  Inference of packet loss  Assumptions about loss

6. Cont.  Receiver and Sender buffering limitation  Congestion Control Algorithm  Maximum Congestion Window  Delayed acks  Retransmission timeout  Initial congestion window  Other assumptions

7. Simulation Environment Router 100Mbps client server Test client Link Capacity 10 Mbps or 1.5 Mbps Ping,WRT, SNMP,MRTG

8. SURGE  Scalable URL Reference Generator  SURGE is used to generate a sequence of URL requests  SURGE consists of three main parts  Client set up  Server set up  Client request generator

9. SURGE (cont.)  The SURGE client setup spawns a number of threads each of which behaves like an individual client.  The client request generator makes the requests for files from the server.  The server setup generates the set of files which are requested by SURGE clients.

10. SURGE (cont.)  As it’s output SURGE gives the start time and end time of each client process.  We can also obtain the mean and variance of server throughput and the total amount of data transferred by the server in unit time.  SURGE can be modified to obtain statistics at regular intervals instead of waiting for the completion of entire simulation.

11. Development of QoE model  Two major issues are involved in the development of QoE model  How should the network be sampled (non invasive sampling)  Transforming these raw samples into input parameters of the chosen model (WRT metric)

12. Sampling Techniques  We use non invasive network sampling where in the network element itself communicates its status (statistics) to the network manager instead of examining tcpdump traces.  Sampling is mostly done at network level and not at application level.

13. Non invasive sampling techniques  Probing: Probes can be implemented using ping packets. We can obtain an estimate of RTT and loss rates.  Polling: This refers to periodic querying by SNMP MIBs maintained in routers to retrieve performance data.

14. Development of QoE model  Once this raw data is obtained this has to fed into a QoE model.  We use a simple WRT metric to obtain QoE initially. The WRT metric gives a variation in the response time.  Our QoE model should be able to predict the mean sample time and also it’s variation.

15. Future work  Comparison of non-invasive and invasive sampling techniques  Impact of congestion control/ avoidance algorithm on the assessment of metrics  Once this is done we assess how our predicted QoE is correlated to the QoE perceived by the user.

16. Future work  Develop a survey methodology to help us validate our assessment algorithm (i.e., have a set of users tell us what they think of their web browsing experience when the WRT metric is 2.5 seconds).  Use the results of this survey to further strengthen the algorithm and make the metrics as close as possible to the quality perceived by end user.  to validate the QoE assessment

17. Future work  Possibly extend Surge to model streaming or conferencing flows  Compare Surge traffic with other approaches to generating realistic traffic loads  Extend the QoE model to Real time applications.