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

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

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.

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

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

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

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

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

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.

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.

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)

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.

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.

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.

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.

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

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.