云计算环境下,针对用户无法准确评价服务实体,客观选择服务提供商的问题,结合信息熵和相关比理论来分析实体之间动态信任关系,将服务细粒度化,提出一种基于服务感知的可信QoS(quantity of service)评价模型.在该模型中,引入时间因子刻...云计算环境下,针对用户无法准确评价服务实体,客观选择服务提供商的问题,结合信息熵和相关比理论来分析实体之间动态信任关系,将服务细粒度化,提出一种基于服务感知的可信QoS(quantity of service)评价模型.在该模型中,引入时间因子刻画信任的衰减特性,综合直接信任、推荐信任以及QoS反馈信任描述实体之间的信任关系;对于新加入节点以某先验初始概率参与服务交互,能在一定程度上有效地抵御漂白攻击;在推荐信任度量与直接信任度量计算中引入信息熵和相关比理论测度,能有效地减弱恶意评价对信任度量的影响.模拟实验表明,该模型在很大程度上满足用户选择服务的需求,并提高资源调度的成功率,有效地保障服务资源调度的安全性.展开更多
We present the logistic growth model to study the stochastic resonance （SR） in a bacterium growth system under the simultaneous action of two external multiplicative cross-correlation noises and periodic external fo...We present the logistic growth model to study the stochastic resonance （SR） in a bacterium growth system under the simultaneous action of two external multiplicative cross-correlation noises and periodic external forcing. The expression of the signal-to-noise ratio （SNR） for a bacterium growth system is derived by using the theory of SNR in the adiabatic limit. Based on SNR, we discuss the effects of self-correlation time τ1 and τ2, cross-correlation time 3-3 and cross-correlation strength λ on the SNR. It is found that the self-correlation time τ1 and τ2, and cross-correlation strength λ enhance the SR of the bacterium growth system, while cross-correlation time τ3 weakens the SR of the bacterium growth system.展开更多
文摘云计算环境下,针对用户无法准确评价服务实体,客观选择服务提供商的问题,结合信息熵和相关比理论来分析实体之间动态信任关系,将服务细粒度化,提出一种基于服务感知的可信QoS(quantity of service)评价模型.在该模型中,引入时间因子刻画信任的衰减特性,综合直接信任、推荐信任以及QoS反馈信任描述实体之间的信任关系;对于新加入节点以某先验初始概率参与服务交互,能在一定程度上有效地抵御漂白攻击;在推荐信任度量与直接信任度量计算中引入信息熵和相关比理论测度,能有效地减弱恶意评价对信任度量的影响.模拟实验表明,该模型在很大程度上满足用户选择服务的需求,并提高资源调度的成功率,有效地保障服务资源调度的安全性.
基金Supported by the Natural Science Foundation of Yunnan Province under Grant Nos.2005A0080m-2 and 08C0235the Key Subjects Fund for Condensed Physics of Qujing Normal University
文摘We present the logistic growth model to study the stochastic resonance （SR） in a bacterium growth system under the simultaneous action of two external multiplicative cross-correlation noises and periodic external forcing. The expression of the signal-to-noise ratio （SNR） for a bacterium growth system is derived by using the theory of SNR in the adiabatic limit. Based on SNR, we discuss the effects of self-correlation time τ1 and τ2, cross-correlation time 3-3 and cross-correlation strength λ on the SNR. It is found that the self-correlation time τ1 and τ2, and cross-correlation strength λ enhance the SR of the bacterium growth system, while cross-correlation time τ3 weakens the SR of the bacterium growth system.
