Risk Analysis of Blocked Rate Predictions for SDN Load Balancing Using Monte Carlo Simulation The emergence of large data centers and virtualization needs better and smarter solutions for traffic scheduling and load balancing. Data centers benefit from SDN regarding centralized monitoring and management for traffic routing. In general, the traffic in the data center environment can be classified as elephant and mice flow. Researchers showed that there is a significant amount of data carried over elephant flows; therefore, it should be conserved and maintained thoroughly. In this work, we introduce a stochastic performance evaluation model for estimating blocked rate prediction and risk analysis of the elephant flows for a load balancing data center with fattree topology using the SDN paradigm. The general procedure of the evaluation includes the estimation of the distribution of the path available bandwidth, including bandwidth error tolerance. The proposed model relies on Monte Carlo simulation to generate future prediction behavior of the load balancing technique. The achieved results examined with Value at Risk (VaR) along with statistics to percept the complete picture of the load balancing behavior.

An SDNbased Hybrid Strategy for Load Balancing in Data Center Networks th for various services. Yet todayxe2x80x99s widely used load balancing scheme, i.e., ECMP, may cause serious congestion when hash collision happens. Recent proposals either push load balancing function to a centralized controller or network edges. However, the centralized schemes are too slow for latencysensitive flows, while the distributed schemes lack the global view and usually cannot make the best choices. In this paper, based on SoftwareDefined Networking (SDN), we present a new hybrid load balancing scheme called BLEND. It promotes the cooperation among network components and takes advantage of both global view and fast endhost action. BLEND aims to improve the throughput of big flows and reduce the latency of small and medium flows. It employs a controller to assign paths to big flows to achieve high throughput. In addition, in order to provide guidance for fast distributed load balancing decisions, the controller also calculates the optimal network delay thresholds for small and medium flows, while hosts utilize these thresholds to decide whether to change the current paths. BLEND is practical and easily deployable in the current data center networks. Comprehensive experiments demonstrate that BLEND outperforms both the centralized and distributed schemes and achieves at most 40% reduction in FCT and at most 2.8 times improvement in throughput.