1. Detecting Traffic Differentiation in Backbone ISPs with NetPolice Patrick Wong Yinzhi Cao

2. Heisenberg Uncertainty Principle Because you measure the system, you influence it. The system is different from what you are measuring. In this example, your added flow may cause more loss. (How much? Does it matter?) We want to see. How do you minimize this potential source of error?

3. Biased System There is a possibility that AS doesn’t differentiate between contents originating from different sources (http vs. BitTorrent) But your system itself makes the content itself different. For example, when you add probing flow of certain content, that behavior makes loss rate of these kinds of content increase.

4. Root Cause? Let us temporarily admit the results are right. But is content, source the root cause? For example, loss rate is related to load. In the daytime, load is high while in the night load is low. But in the daytime, there are more HTTP traffic. And in the night, there are more P2P traffic. So what we observe is that AS differentiate traffic contents. But the root cause is load.

5. Several Problems (1) How large is the package they are using? Loss rate may be different due to different package size. Do they consider that? What is the package size they used anyway? They cannot fully mimic the temporal behavior of real application traffic. But the problem is speed did have influence on loss rate.

6. Several Problems (2) They discard all the loss rate samples affected by routing changes. Problem is how often this changes happen. Does it matter?

7. Several Problems (3) They divide one set into two equally- sized subsets and apply the K-S test to these two subsets to infer noise. Problem: They assume that noise is independent of content. Is that true? (No proof.) It is possible that some noise are related to contents ISPs monitored: What happened to Asia?