面向SDN数据中心网络最大概率路径流量调度算法

随着数据中心网络规模的迅速增长，网络带宽利用率低下导致的网络拥塞问题日益突出，通过负载均衡提高数据中心网络链路带宽利用率和吞吐量成为了研究热点.如何结合流量特征、链路状态和应用需求进行流量的合理调度，是实现网络链路负载均衡的关键.针对数据中心突发性强、带宽占用率高的大象流调度问题，提出一种面向SDN数据中心网络最大概率路径流量调度算法，算法首先计算出满足待调度流带宽需求所有路径，然后计算流带宽与路径最小链路带宽之间的带宽比，结合所有路径的带宽比为每一条路径计算路径概率，最后利用概率机制选择路径.算法不仅考虑了流带宽需求和链路带宽使用情况，而且全局地考虑了流调度和链路带宽碎片问题.实验结果表明，最大概率路径调度算法能够有效地缓解网络拥塞，提高带宽利用率和吞吐量，减少网络延迟，从而提高数据中心的整体网络性能和服务质量.

Maximum Probability Path Scheduling Algorithm for Elephant Flow in Data Center Networks Based on SDN

With the rapid growth of the scale of the data center networks, the low network bandwidth utilization has posed a problem due to network congestion. How to improve data center network link bandwidth utilization and throughput by load balancing has become a research focus. How to reasonably schedule flow by making use of traffic characteristics, link state and application requirements is the key to realize the network link load balancing. Aiming at scheduling problem of elephant flow that bursts and highly occupies bandwidth in the data center, this paper proposes a maximum probability path scheduling algorithm (MPP_SA) for SDN data center network. The algorithm firstly computes all paths that can meet the scheduled flow''s demand, and then calculates bandwidth ration between flow bandwidth and minimum link bandwidth combining with all the bandwidth ratio to compute path probability for each path. Finally, the path of largest path probability will be likely selected. The algorithm does not only consider flow''s bandwidth and usage of link bandwidth, but also the global flow scheduling and bandwidth fragmentation. The experimental results show that the MPP_SA algorithm can effectively alleviate network congestion, improve the bandwidth utilization and throughput, and reduce network delay, so as to improve the overall network performance and quality of service.