Self-configuring network traffic generation
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
| Hi-index | 0.00 |
It is commonly believed that the Internet has deficiencies that need to be fixed. However, making changes to the current Internet infrastructure is not easy, if possible at all. Any new protocol or design to be implemented on a global scale requires extensive experimental testing in sufficiently realistic settings; simulations alone are not enough. On the other hand, performing network experiments is intrinsically difficult for several reasons: i) Creating a network with multiple routers and a topology that is representative of a real backbone network requires significant resources, ii) Network components have proprietary architectures, which makes it almost impossible to figure out all of their internal details, iii) Making changes to network components is not always possible, iv) We cannot always use real network traces and generating high volumes of artificial traffic which closely resemble operational traffic is not trivial, and v) We need a measurement infrastructure which collects traces and measures various metrics throughout the network. These problems become even more pronounced in the context of time-sensitive network experiments. These are experiments that need very high-precision timings for packet injections into the network, or require packet-level traffic measurements with accurate timing. Experimenting with new congestion control algorithms, buffer sizing in Internet routers, and denial of service attacks which use low-rate packet injections are all examples of time-sensitive experiments, where a subtle variation in packet injection times can change the results significantly. In this work we study the challenges of conducting time-sensitive network experiments in a testbed. We provide a set of guidelines that aim at eliminating sources of inaccuracy in a time-sensitive network experiment. We should note that these guidelines are not meant to be comprehensive. For the sake of space, we only focus on issues that are most likely to be overlooked, and thus unknowingly distort the results of a time-sensitive network experiment.