无尺度网络自适应管理策略

针对建立在随机网络模型上的传统网络管理中所存在的数据采集流量偏大、同步采集发生率高等管理性能问题,提出一种适合无尺度网络的自适应网络管理策略,该策略可以通过对比被管网元地位度量阈值,动态探测被管网元在网络中所处的地位,根据网元所处地位不同自动调整数据采集周期.实验证明,这种策略在不降低网络管理功效前提下,能够减少需采集的网络管理数据,并避免了数据采集的同步,从而提高网络管理的性能.

Self-Adaptive Management Policy for Scale-Free Networks

Traditional network management policy built on the theory of random network model has some disadvantages such as redundant management data, high probability of synchronized data collecting and so on. The shortcomings will lead management performance degradation. So a self-adaptive policy based on scale-free network is mentioned to avoid disadvantages of traditional network management. The policy dynamically probes the rank of the entity based on the threshold of the metric. It can adjust automatically the period of the data collectors according to the rank of the network entity. Experiments confirm that the self-adaptive policy can reduce amount of collected data and eliminate the synchronization of data collecting. The policy improves management performance and keeps the same function of traditional network management.