Traffic-based Load Balance for Scalable Network Emulation

Load balance is critical to achieving scalability for large network emulation studies, which are of compelling interest for emerging Grid, Peer to Peer, and other distributed applications and middleware. Achieving load balance in emulation is difficult because of irregular network structure and unpredictable network traffic. We formulate load balance as a graph partitioning problem and apply classical graph partitioning algorithms to it. The primary challenge in this approach is how to extract useful information from the network emulation and present it to the graph partitioning algorithms in a way that reflects the load balance requirement in the original emulation problem. Using a large-scale network emulation system called MaSSF, we explore three approaches for partitioning, based on purely static topology information (TOP), combining topology and application placement information (PLACE), and combining topology and application profile data (PROFILE). These studies show that exploiting static topology and application placement information can achieve reasonable load balance, but a profile-based approach further improves load balance for even large scale network emulation. In our experiments, PROFILE improves load balance by 50% to 66% and emulation time is reduced up to 50% compared to purely static topology-based approaches.