Coflow scheduling in mapreduce framework for cloud data center

In the cloud computing environment, more and more enterprises need to build or rent data center infrastructure to deploy their own cloud computing applications. However, in the application layer, the existing task placement and scheduling methods cannot reasonably optimize the real-time network state and node capacity. Therefore, this paper solves the problem of network resource sharing of cloud data center for MapReduce task from the network layer and application layer from bottom to top. We propose coflow scheduling algorithms for optimizing the network resources of cloud data center. We conduct many simulation experiments to verify the performance of the algorithms.