异常入侵数据干扰下网络资源调度优化研究

发布时间：2018-06-08 18:22

本文选题：异常入侵数据干扰 + 网络资源　；参考：《计算机仿真》2017年03期

【摘要】：对异常入侵数据干扰下对网络资源进行合理的调度,可提高网络资源的可利用率。进行资源调度时,应获取异常入侵数据干扰下网络资源调度过程中资源的动态优先级,根据优先级的大小进行资源调度,而传统方法依据各个路径上的传送列表信息以及拥塞情况对网络情况进行预测,依据网络实际情况分派适应的传送任务量完成调度,忽略了不同资源的优先级大小,容易出现紧急任务未被调度而非紧急任务提前被调度的情况,降低了资源效率的有效性。提出一种采用遗传算法和蚁群算法的动态融合的异常入侵数据干扰下网络资源调度优化算法。该方法先根据网络资源数据任务调度价值密度以及实行迫切性,构造动态优先级。对于网络资源数据在异常入侵数据干扰下的时间序列上的调度过程进行定义分析,给出了融合算法对网络资源数据任务调度策略进行优化的最佳动态融合过程,将上述方法应用于异常入侵数据干扰下的网络资源数据任务调度中,仿真结果表明,改进方法可以提高异常入侵数据干扰下网络资源调度的效率和准确性。
[Abstract]:The reasonable scheduling of network resources under the interference of abnormal intrusion data can improve the availability of network resources. When scheduling resources, we should obtain the dynamic priority of resources in the process of resource scheduling under the interference of abnormal intrusion data, and schedule resources according to the size of priority. The traditional methods predict the network situation according to the transmission list information on each path and the congestion situation, assign the appropriate transfer tasks according to the actual situation of the network to complete the scheduling, and ignore the priority of different resources. It is easy to have the emergency task not scheduled but not the emergency task to be scheduled ahead of schedule, which reduces the efficiency of resources. In this paper, a dynamic fusion of genetic algorithm and ant colony algorithm is proposed to optimize network resource scheduling under the interference of abnormal intrusion data. The dynamic priority is constructed according to the value density and urgency of resource task scheduling. This paper defines and analyzes the scheduling process of network resource data on the time series under the interference of abnormal intrusion data, and presents the optimal dynamic fusion process in which the fusion algorithm optimizes the task scheduling strategy of network resource data. The simulation results show that the improved method can improve the efficiency and accuracy of network resource scheduling under abnormal intrusion data interference.
【作者单位】：新疆大学科学技术学院;
【分类号】：TP393.0;TP18

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