Summary

This chapter presented our traffic generation method, source-level trace replay. The first step in source-level trace replay is to transform a packet header trace into a set of connection vectors, which describe its source-level behavior using the sequential or the concurrent version of the a-b-t model. Connection vectors also include three network-level parameters, round-trip time, TCP receiver window size and loss rate. The actual traffic generation consists of replaying the characteristics of each connection vector in an accurate manner. We demonstrated the possibility of this approach using an implementation in a network testbed, which includes a distributed traffic generator, tmix , that can replay source-level behavior, and coordinate with a packet manipulation layer, usernet , to impose specific round-trip times and loss rates to each connection. The approach, and its implementation, was then validated by comparing the statistical characteristics of three traces and those of their replays. This comparison focused on how well the replay preserved the original parameters, i.e., the source-level description and the network-level characteristics.

The validation results showed a good match between original traces and their replays, which confirms the highly accurate reproduction of source-level properties that can be achieved with our approach. The differences, which are shown to be small or nonexistent in every case, are due to the following causes:

• There is no guarantee that the replay of a concurrent connection exhibits measurable concurrency, i.e., that a pair of concurrent data segments can be observed in the generated trace. This results in connections that are replayed as concurrent but classified as sequential in , therefore adding spurious samples to the characterization of sequential connections, and removing samples from the characterization of concurrent connections. In general, this affects the comparison of concurrent connections more substantially, since the number of samples from concurrent connections is usually far smaller. This problem is inherent to the form of the concurrent a-b-t model used in this dissertation.
• Our measurement of quiet times tended to overestimate their durations, since it did not compensate for the delay between the end host and the monitor. This difference is only significant for the smallest quiet times, whose magnitude is similar to that of network delays. A possible refinement of our measurement method that would eliminate the overestimation of quiet times and make the replay of quiet times even more accurate, is to subtract the corresponding one-side transit time from each measured quiet time.
• Usernet uses independent dropping to simulate losses, and this is not completely accurate. Connections often have too few packets to converge to the intended loss rate per connection. If loss rates per byte are considered, the replay is shown to be very close to the original distribution. Achieving a close approximation of the original loss rate would involve some form of dependent dropping.
• Measured drop rates consider only data segments, but the loss rate simulation also drops pure acknowledgments with the same probability. This makes the distributions of loss rates in the lossy replays slightly above the intended values. Addressing this inaccuracy requires developing a measurement algorithm that can determine the loss rate of pure acknowledgments, which seems rather difficult, or modifying usernet to drop only data segments, which is a somewhat artificial solution.

The analysis of the validation results also served us to verify the robustness of our data acquisition and generation method to the introduction of losses with regard to the source-level characteristics. We found very little difference, if any, between the results from the lossless and lossy replays, which confirms the accuracy of the analysis even in the face of packet losses and reordering. TCP timeouts, which can sometimes confuse the heuristic used to split ADUs in the same direction, do not appear to have any significant effect.