DiFS: Distributed Flow Scheduling for adaptive switching in FatTree data center networks
Abstract Data center networks leverage multiple parallel paths connecting end host pairs to offer high bisection bandwidth for cluster computing applications. However, the state-of-the-art routing protocols such as Equal Cost Multipath (ECMP) is load-oblivious due to the static flow-to-link assignment. They may cause bandwidth loss due to flow collisions. Recently proposed centralized scheduling algorithm or host based adaptive routing that requires network-wide state information may suffer from scalability problems. In this paper, we present Distributed Flow Scheduling (DiFS), a new adaptive switching method, for FatTree data center networks, which is a localized and switch-only solution. DiFS allows switches to cooperate to avoid over-utilized links and find available paths without centralized control. DiFS is scalable and can react quickly to dynamic traffic because it is independently executed on switches and requires no synchronization. DiFS provides global bounds of flow balance based on local optimization. Extensive simulations show that the aggregate throughput of DiFS using various traffic patterns is much better than that of ECMP, and is similar to or higher than those of two representative protocols that use network-wide optimization.