Blocking cost-driven multicast scheduling in fat-tree data center networks

Multicast traffic scheduling in data center networks can not only improve network efficiency but also save network resources. However, the existing multicast scheduling algorithms cannot appropriately schedule flows to achieve traffic load balance so that the network may occur heavy blocking. This prevents the full utilization of high degree of link parallelism and causes unpredictable reduction of network performance. To address the problem, in this paper, we propose a blocking cost-driven multicast scheduling algorithm by using optimization theory in fat-tree data center networks. In particular, a model of multicast traffic subnetwork is established on the basis of the blocking probability of available paths at next time slot, which can reflect the blocking characteristics of multicast network and predict network state at next time slot. With the multicast blocking model, we derive the minimum blocking probability of multicast subnetworks, denoted as blocking cost. In addition, the algorithm can select the multicast subnetwork with minimum blocking cost to transfer multicast flows. Time complexity analysis and simulation results demonstrate the effectiveness and efficiency of our proposed multicast scheduling algorithm for different network traffic intensities.