| Abstract |
Max-min fair is widely used in network traffic engineering to allocate available resources among different traffic transfers. Recently, as data replication technique developed, increasing systems enforce multi-source transmission to maximize network utilization. However, existing TE approaches fail to deal with multi-source transfers because the optimization becomes a joint problem of bandwidth allocation as well as flow assignment among different sources. In this paper, we present a novel allocation approach for multi-source transfers to achieve global max-min fairness. The joint bandwidth allocation and flow assignment optimization problem poses a major challenge due to nonlinearity and multiple objectives. We cope with this by deriving a novel transformation with simple equivalent canonical linear programming to achieve global optimality efficiently. We conduct data-driven simulations, showing that our approach is more max-min fair than other single-source and multi-source allocation approaches, meanwhile it outperforms others with substantial gains in terms of network throughput and transfer completion time. |