| 陈倩,许媛.基于收敛启发机制的人工网络安全迁移算法研究[J].井冈山大学自然版,2019,40(5):40-45 |
| 基于收敛启发机制的人工网络安全迁移算法研究 |
| RESEARCH AND SIMULATION OF ARTIFICIAL NETWORK SECURITY MIGRATION ALGORITHM BASED ON CONVERGENCE HEURISTIC MECHANISM |
| 投稿时间：2019-03-07 修订日期：2019-03-07 |
| DOI：10.3969/j.issn.1674-8085.2019.05.008 |
| 中文关键词: 人工网络 收敛启发 社区网络 数据迁移 启发映射 网络瘫痪频率 |
| 英文关键词: artificial network convergence heuristics community network data migration heuristic mapping network paralysis |
| 基金项目:安徽省高校自然科学基金项目（KJH2015B02）；安徽省教育厅自然科学研究项目（KJ2018A0953） |
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| 中文摘要: |
| 针对当前人工网络安全迁移算法研究中存在迁移时间长、误码率高且容易造成网络瘫痪等不足，提出了一种基于收敛启发机制的人工网络安全迁移算法。首先，利用社区网络进行网络迁移时具有的波动特性，通过带宽函数均值起伏率和网络存储冗余率两个指标进行迁移裁决，有效减缓了迁移过程中网络出现拥塞的概率，实现数据迁移并提高网络安全迁移过程中的鲁棒性。随后，针对当前算法迁移过程中难以进行误差评估的不足，通过启发映射机制设计了网络存储冗余带宽迁移方法，用以改善网络数据传输过程中的抖动，改善网络迁移时的效率，具有很强的迁移质量。仿真实验表明：与当前常用的超混沌云网络预估迁移机制（Predictive Migration Mechanism of Hyperchaotic Cloud Networks，PMM-HCN机制）、社区网络大数据峰值安全迁移机制（Peak Security Migration Mechanism of Large Data in Community Network，PSMM-LDCN机制）相比，本文算法具有网络迁移时间少、网络迁移数据误码率小、网络抖动时间短、网络瘫痪频率低等特性，具有很强的实际部署价值。 |
| 英文摘要: |
| In order to overcome the shortcomings of longmigration time, high bit error rate and easy to cause network paralysis in the current research of artificial network security migration algorithm, a new artificial network security migration algorithm based on convergence heuristic mechanism is proposed. Firstly, by using the fluctuation characteristics of community network in network migration, the migration decision is made through two indicators:the fluctuation rate of bandwidth function mean and the redundancy rate of network storage, which greatly reduces the probability of network congestion in the migration process, realizes data migration efficiently, and improves the robustness in the process of network security migration. Then, aiming at the shortcomings of error evaluation in current algorithm migration process, a method of network storage redundant bandwidth migration is designed through heuristic mapping mechanism to improve the jitter in the process of network data transmission, improve the efficiency of network migration, and have a strong migration quality. The simulation results show that the proposed algorithm has less network migration time than the commonly used Predictive Migration Mechanism of Hyperchaotic Cloud Networks and Peak Security Migration Mechanism of Large Data in Community Network. The characteristics of network migration, such as lowbit error rate, short network jitter time and lower network paralysis frequency, make it have great practical deployment value. |
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