一种基于DAG动态重构的认知网络服务迁移方法

针对认知网络高度动态性带来的服务随机失效问题,提出了一种服务迁移方法以保障认知网络的 QoS.首先,采用先迁移、后优化的思想,重新生成关联服务有向无环图(directed acyclic graph,简称DAG),并在此基础上提出 DAG 动态重构算法,将关联服务转化为层次化 DAG 服务；其次,计算关键服务迁移路径,并给出可迁移服务死锁避免理论分析,将迁移服务提前迁移到当前网络空闲资源运行,以缩短服务的执行时间.仿真实验测试了3种故障注入类型下网络服务迁移方案的服务性能.实验结果显示,该方法在弹性网络负载与未知故障情况下具有较好的 QoS保障效果.

Service Migration Method for Cognitive Network Based on DAG Dynamic Reconstruction

College of Computer Science and Technology, Harbin Engineer University, Harbin 150001, ChinaAbstract: According to randomness of service failure for high dynamicity of cognitive networks, a service migration method is proposed to ensure QoS of cognitive networks. Firstly, with the principle of optimization-after-migration, the directed acyclic graph (DAG) of correlated service is regenerated according to the proposed DAG dynamic reconstruction algorithm to transform the correlated service to layered DAG service. Secondly, the critical service migration route is computed and the analysis of migration service deadlock avoidance is provided. By migrating critical service to current idle resources, service execution time can be reduced markedly. Finally, simulation experiments are conducted to test the service speedup performance of both service migration method and waiting-recovery method with three kinds of faults injected. The experiment results show that service migration method can achieve better QoS assurance quality under the flexible network load and unknown fault injection.