大数据分析下网络动态数据通信效率控制研究

Research on Dynamic Data Communication Efficiency Control of Network under Big Data Analysis

针对当前通信效率控制方法存在吞吐量大、收敛速度慢、传输效率低等问题,提出基于蚁群算法的网络动态数据通信效率控制方法。在进行网络动态数据通信效率控制前要先满足带宽、时延、费用等多项指标;通过网络动态数据优化指标对网络动态数据负载进行路径规划,以达到网络动态数据资源消耗与负载分配均衡;以此为基础建立目标函数;采用蚁群算法对目标函数进行求解,引用逆向思维计算相邻两个节点间状态转移概率;以奖惩方式按照动态数据信息素的浓度来选择最优路径,以此降低网络拥塞几率,提高网络动态数据通信效率。实验结果表明,所提方法能够更好的提高网络动态数据通信传输效率、加快收敛速度、减小吞吐量。

Due to large throughput, slow convergence speed and low transmission efficiency in current methods, a method to control dynamic data communication efficiency based on ant colony algorithm was proposed. The network dynamic data optimization index was applied on path planning for network dynamic data load so as to balance the dynamic data resource consumption and load distribution. On this basic, the objective function was established. Moreover, ant colony algorithm was used to solve the objective function and the reversed thinking was used to calculate the state transition probability between two adjacent nodes. The optimal path was selected based on the concentration of dynamic data pheromone in the way of rewards and punishments and thus to reduce the probability of network congestion and improve the communication efficiency of network dynamic data. Simulation results show that the proposed method can improve the efficiency of network dynamic data communication transmission. Meanwhile, it can speed up the convergence and reduce the throughput.