Towards Realistic Networks for Simulating Large-Scale Distributed Systems

Research in large-scale distributed systems, such as P2P systems, often relies critically on simulations to validate research results. Though systems such as PlanetLab can be used to test on real networks in some cases, there are still significant practical challenges to evaluating large-scale distributed systems on actual hardware. Actual measured datasets such as the ones measured with King´s method also have an important role, but often do not provide enough scale or are not representative of the network for which the research is intended. Typically, in such cases, tools such as GT-ITM are used to generate network topologies for the evaluation simulation. These tools work reasonably well at generating physical topologies that are representative of real systems. The behavior of distributed systems, however, depends not just on the physical topologies, but also the routing policies and other factors that affect latency and bandwidth. These aspects may have a considerable impact on any evaluation performed on the generated network, and can lead to significant differences between simulated performance and actual performance. In particular, triangle inequality violations and path inflation can adversely impact large-scale distributed systems. In this paper, we present techniques and approaches for adding such real-world effects to generated networks, in a parametrized approach. We show the parameters can be varied to generate a variety of networks with different characteristics, and compare them to measured datasets.