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Data multicast is an important data traffic pattern in today’s data center running big data oriented applications. The physical layer multicast capability enabled by the emerging technologies used to build circuit switches exhibits huge benefit in transferring multicast data. This paper tackles the problem of scheduling multicast data transfer in high-bandwidth circuit switch. The scheduler aims at minimizing the average demand completion time to deliver the most benefit to the applications. Our algorithm exhibits up to 13.4 improvement comparing with the state-of-the-art solution. 

This paper promotes convertible data center network architectures, which can dynamically change the network topology to combine the benefits of multiple architectures. We propose the flat-tree prototype architecture as the first step to realize this concept. Flat-tree can be implemented as a Clos network and later be converted to approximate random graphs of different sizes, thus achieving both Clos-like implementation simplicity and random-graph-like transmission performance. We present the detailed design for the network architecture and the control system. Simulations using real data center traffic traces show that flat-tree is able to optimize various workloads with different topology options. We implement an example flat-tree network on a 20-switch 24-server testbed. The traffic reaches the maximal throughput in 2.5s after a topology change, proving the feasibility of converting topology at run time. The network core bandwidth is increased by 27.6% just by converting the topology from Clos to approximaterandom graph. This improvement can be translated into acceleration of applications as we observe reduced communication time in Spark and Hadoop jobs.