Efficient Dynamic Load-Balance Flow Scheduling in Cloud for Big Data Centers

Abstract

Big data centers in cloud, large amount of data needs to be transferred frequently among thousands of interconnected servers. In which load balancing and flow scheduling is a challenging issue. The OpenFlow is a auspicious solution to balance data flows in big data center network through programmatic traffic controller. Existing solution can able to statically set up routes only at initialization stage of data transmissions, which experiences from dynamical ow distribution and network changing state so it results in decrease system performance. In this paper, we will propose a new dynamical load-balanced scheduling (DLBS) approach for increase the network throughput to dynamically balance workload. This approach formulate the DLBS problem, and then develop a set of improved heuristic scheduling algorithms for the two typical OpenFlow network models, which balance data flows time slot by time slot. Experimental results demonstrate that our DLBS approach signicantly outperforms other load-balanced scheduling algorithms Round Robin and LOBUS; and the higher imbalance degree data ows in data centers exhibit, the more improvement our DLBS approach will bring to the data centers.