Incorporating Additional Network-Level Parameter

While our methods to measure and simulate network parameters appear sufficiently accurate in our experimental evaluation, there are several directions in which this part of the work can be refined. Path round-trip times are not fixed for each connection, but follow a distribution of delays. It seems possible to refine our measurement to incorporate this fact, at least to some extent, into our approach, although the lack of samples for most connections greatly complicates this problem. It is also unclear whether this refinement would have any significant impact on the generated traffic. Improving the measurement and simulation of losses could have a more substantial effect. Figure 4.18 already revealed some level of inaccuracy, and our experimentation revealed the need to take into account pure acknowledgment losses and not just data segment losses. More importantly, the assumption of independent losses and their simulation using random dropping seems unrealistic, which explains some of the differences between original and synthetic traffic.

There are other network parameters that could be taken into account. In general, we believe that only two of them would have a significant impact on the quality of synthetic traffic: maximum segment sizes and path capacity. Maximum segment sizes are straightforward to measure, and their incorporation into the experiments would improve the realism of packetization in the generated traffic. Its implementation in a network testbed experiments requires some careful handling of resources, since maximum segment sizes are often a machine-wide constant. The impact of this refinement is not expected to be dramatic, given that most connections are known to use the same maximum segments size (the one derived from Ethernet's MTU, which we employed in our experiments).

Path capacity presents a much more difficult measurement problem, both when defined as bottleneck capacity and as available bandwidth. Recent work by Huang and Dovrolis [JD04] provides a useful foundation. While it is only applicable with confidence to connections with large amounts of data, ``bulk connections'', this is precisely the type of connection whose throughput could be dominated by capacity limits. Throughput in connections with small amounts of data is mostly a function of round-trip time. As discussed in Section 3.3, most connections are in this case. However, bulk connections are responsible for a large fraction of the bytes, so their accurate replay is important. We also believe that combining our ADU analysis with the Huang and Dovrolis approach can provide less noisy samples, improving the accuracy of the method. In the case of capacity, the implementation in the experiment is not difficult by making use of dummynet 's per-connection capacity.

Besides these concrete specific network parameters, we believe that a better understanding of the impact of traffic shapers and end host bandwidth quotas can help to explain some of the differences between source-level trace replay experiments and original traffic. This seems specially relevant for UNC, where the impact of losses appeared rather different from the ones in other sites. We hypothesized that the presence of a major data and software repository known to use bandwidth constraints was behind our finding. Another important factor in traffic characteristics is the growing impact of wireless networks. Our large-scale measurement effort in this area [HCP05], showed an insignificant increase of end-to-end losses in this environment (thanks to link-layer retransmission) but substantial increases in the magnitude and variability of round-trip times.