网格环境下基于信任机制的资源调度研究

信任是网格资源调度中一个很重要的因素,也是影响网格计算有效性和性能的关键技术之一。将信任机制引入到网格资源调度中,提出了网格环境下的信任模型和基于信任机制的资源调度模型,在调度策略上对传统的Min-Min算法进行了改进,提出了基于信任机制的Trust-Min-Min算法。仿真结果表明,算法不仅可以缩短任务的总执行时间,而且可以有效地平衡负载,是网格环境下一种有效的资源调度方法。     

网格环境下一种基于Bayes信任模型的动态级调度算法

提出了一种基于信任机制的任务调度模型,该模型通过借鉴社会学中的信任机制,建立网格节点间的信任关系,利用Bayes方法对节点的可信度进行评估,将节点的可信度并入DLS算法得到可信动态级调度(Trust-DLS)算法.仿真结果证实,提出的Trust-DLS算法能有效提高应用任务在可信方面的服务质量需求.     

面向云数据安全自毁的分布式哈希表网络节点信任评估机制

在云环境下的数据自毁机制中,针对分布式哈希表（DHT）网络的恶意节点和不诚信节点容易造成密钥分量丢失或泄露等问题,提出面向云数据安全自毁的DHT网络节点信任评估机制。该机制首先为节点建立二维信任评估模型,对节点可信程度进行定性和定量的考察;接着改进节点直接信任值和推荐信任值的计算方法,充分考虑节点内外因素,从节点运行环境和交互经验两个维度出发并细化到不同层级计算节点信任值,得到信任评价子云;然后将各个信任评价子云加权合成得到综合信任云,采用云发生器描绘出综合信任云一维高斯云图形;最后结合信任决策算法选出最优可信节点。实验结果表明该机制能够帮助原有的数据自毁机制找到更适合存储密钥分量的DHT网络节点,提高了系统的容灾能力并降低了系统计算负载。     

P2P网络中基于云模型的信任机制研究

针对近年来对等网络中存在大量不安全服务的问题,提出一种基于云模型的信任机制。在符合查询操作的节点中,根据前若干周期的信任向量,利用灰色预测模型预测各个节点在当前周期的信任向量,设计云模型算法分别建立云模型,依据信任云决策算法和信任云相似度度量算法选出值得信任的节点为请求节点提供服务。仿真结果表明,该机制在选择目标服务节点时,不仅考虑节点信任值的平均值,而且还考虑信任值的离散性,可以从多个角度更全面、合理地选出最值得信任的目标服务节点,达到提高网络服务质量、增强网络安全性的目的。     

面向个性化云服务基于用户类型和隐私保护的信任模型

针对云用户难以获得个性化、高质量服务的问题,提出一种面向个性化云服务基于用户类型和隐私保护的信任模型。该模型先根据节点间的历史交易,将用户节点分为亲情节点、陌生节点及普通节点三种类型;其次,为了保护节点反馈的隐私信息,引入信任评估代理作为信任评估的主体,并且设计了基于用户类型的信任值评估方法;最后,鉴于信任的动态性,结合交易时间和交易额度提出一种新的基于服务质量的信任更新机制。实验结果表明,与AARep模型及PeerTrust模型相比,该模型不仅在恶意节点比例较低的场景中具有优势,而且在恶意节点比例超过70%的恶劣场景中,其交互成功率也分别提高了10%和16%,克服了云环境下用户节点和服务节点交互成功率低的缺点,具有较强的抵抗恶意行为的能力。     

基于云模型的协同过滤推荐算法

传统的协同过滤推荐算法面临严峻的数据稀疏性和推荐实时性困境,推荐质量明显不高。为提高推荐效果,首先对基于云模型的用户评分项和相似性度量方法展开研究。然后定义基于云模型的推荐系统信任约束,并改进主观信任云模型的约束函数、信任变化云模型的信任变化函数。最后提出一种基于云模型的协同过滤推荐算法。实验结果表明,相比传统算法,该算法在用户评分数据稀疏的状况下仍然可以取得良好的推荐效果,具有较高的实用价值。     

基于信任机制与遗传算法的渲染网格作业调度研究

信任关系是网格作业调度中一个很重要的因素,也是影响网格计算有效性和性能的关键技术之一。将信任机制引入到渲染网格作业调度中,建立渲染网格环境中基于信任机制的作业调度模型,在调度策略上对基本遗传算法进行了改进,提出了基于信任机制的遗传算法。实验结果表明,该算法可以提高任务完成率和平均信任效益,是适用于渲染网格的一种有效作业调度方法。     

P2P网络中信任评价研究

为了提高P2P网络节点间交互的安全性及成功率,客观地反映信任的模糊性与随机性本质,提出基于云模型的信任评价模型.通过信任云及其数字特征的定义,实现了信任定性描述与定量表示的统一.基于逆向信任云生成算法、信任云合并算法进行信任综合,以便获得更为完整的信任信息,并在推荐节点的选择上引进了信任的不确定度计算,提高了推荐的可靠性.通过信任云的相似度计算算法,生成相应的信任综合评价结果,作为节点交易的参考和依据,实现了信任的决策.通过仿真实验,表明该方法可较为客观、真实地反映P2P节点间的信任程度,从而为节点间成功交易提供了科学的、有效的决策依据.     

一种基于信誉QoS的网格资源调度算法

针对目前网格资源管理中存在的调度机制与信任机制分离、资源调度过多依赖于性能服务质量（QoS）的缺陷,讨论了基于信誉QoS的网格资源调度问题．分析了全局可信度模型,提出了基于信誉QoS的网格资源调度算法．该算法在保证服务安全性和可靠性的同时兼顾了网格负载平衡．实验结果表明,该算法在保证服务质量、维护系统性能和抵御恶意节点攻击等方面具有良好的综合性能．     

P2P环境下基于Vague集理论的主观信任模型研究

在诸如文件共享等无中心的P2P环境下,资源共享是用户自愿的行为,用户不承担任何责任,很难通过传统的信任机制来建立用户之间的信任.参考社会学的人际关系模型和Bayesian网络信任模型,在Vague集合理论基础上,提出了一种基于推荐的主观信任管理模型,给出了防止恶意行为和服务热点问题的方法.分析及仿真实验表明,针对网络中的恶意行为,本信任模型比已有的模型有更好的安全性.     

Cloud-DLS: Dynamic trusted scheduling for Cloud computing

Clouds are rapidly becoming an important platform for scientific applications. In the Cloud environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. In this paper, inspired by Bayesian cognitive model and referring to the trust relationship models of sociology, we first propose a novel Bayesian method based cognitive trust model, and then we proposed a trust dynamic level scheduling algorithm named Cloud-DLS by integrating the existing DLS algorithm. Moreover, a benchmark is structured to span a range of Cloud computing characteristics for evaluation of the proposed method. Theoretical analysis and simulations prove that the Cloud-DLS algorithm can efficiently meet the requirement of Cloud computing workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way.     

考虑信任可靠度的分布式动态信任管理模型

分布式动态信任模型作为适用于云计算环境下的访问管理机制已经得到广泛研究,然而现有的许多信任模型忽视了对信任数据可靠性的评估,导致推荐信任不可靠时出现模型失效.针对这一问题,本文提出了一种新的考虑信任可靠度的分布式动态信任管理模型DDTM-TR.DDTM-TR模型首先使用可靠度对信任进行评估,降低不可靠数据对直接信任、推荐信任、综合信任计算的影响.然后,选择多个待选节点计算它们的综合信任,并以计算出的综合信任为概率,随机选择待选节点进行交互.最后,在交互结束后,根据交互满意度反馈修正节点的可靠度.仿真实验表明,DDTM-TR模型在处理恶意服务、恶意推荐都优于对比模型并且能通过反馈算法进一步降低判断的失败率.     

移动agent系统安全问题的动态信任计算模型

在移动云计算中,数据存储和数据处理是在云端以远程方式进行的,因而信任是移动云计算安全中一个非常重要的因素.针对移动云计算环境中移动agent系统安全和信任管理问题,借鉴人类信任机制(human trust mechanism,HTM),研究主观信任形成、信任传播与信任进化规律,提出主观信任动态管理算法(MASTM);基...     

Mobile edge-enabled trust evaluation for the Internet of Things

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra’s algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things.     

基于信誉和风险评估的动态信任模型研究

网格环境中的信任问题是网格计算发展过程中必须解决的一个重要问题。网格节点间的行为信任具有动态性和不确定性。文章综合考虑了信任度评估的动态性和风险性,提出一种新的基于信誉和风险评估的动态信任模型。该模型引入惩罚机制、风险机制和推荐节点信任度更新机制,以评估信任关系的复杂性。仿真结果表明,该模型在有效抑制恶意节点上较已有模型有一定的改进。     

基于DS理论的多源证据融合云安全信任模型

云计算技术在带来信息更大范围共享、成本更低等便捷的同时,也面临着隐私泄露、信息丢失等新的安全问题。如何在海量分布式的云系统中识别和管理节点的可信度成为云安全问题研究的重要方向之一。本模型考虑云计算分布式体系架构,结合DS证据理论和信任机制,构建云环境下基于DS理论的信任模型,为云安全问题提供了新思路。该模型在主客观信任值的基础上合成综合信任值,进行可信度判断;并讨论模型中的关键问题,包括信任值初始化和更新、恶意节点惩罚、负载均衡;再通过仿真实验从有效性、均衡性和鲁棒性等方面对模型进行验证;最后进行总结并对下一步研究进行展望。     

志愿计算中基于贝叶斯定理的信任模型

志愿计算因其开放性、匿名性和动态性得到广泛应用,但同时也对系统的安全性带来挑战.传统认证方式无法满足志愿计算系统的安全性需求,而通过在系统中建立信任机制可以有效解决这一问题.为此,构建一种基于贝叶斯定理的志愿计算系统信任模型VC-trust.依据贝叶斯定理对节点的不确定性行为进行分析预测,根据节点历史交互记录并引入处罚因子和调节函数计算节点信任值,同时利用时间滑动窗口对其进行更新.实验结果表明,在节点行为变化的情况下,VC-trust模型较BTMS模型具有更高的交互成功率.     

An Uncertainty Enhanced Trust Evolution Strategy for e-Science

Resources shared in e-Science have critical requirements on security. Thus subjective trust management is essential to guarantee users’ collaborations and communications on such a promising infrastructure. As an important nature of subjective trust, uncertainty should be preserved and exhibited in trust definition, representation and evolution. Consider the drawbacks of existing mechanisms based on random mathematics and fuzzy theory, this paper designs an uncertainty enhanced trust evolution strategy based on cloud model theory. We define subjective trust as trust cloud. Then we propose new algorithms to propagate, aggregate and update trust. Furthermore, based on the concept of similar cloud, a method to assess trust level is put forward. The simulation results show the effectiveness, rationality and efficiency of our proposed strategy.     

Bayesian Cognitive Model in Scheduling Algorithm for Data Intensive Computing

Science is increasingly becoming more and more data-driven. The ability of a geographically distributed community of scientists to access and analyze large amounts of data has emerged as a significant requirement for furthering science. In data intensive computing environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. Novel algorithms are needed to schedule the jobs on the trusty nodes to execute, assure the high speed of communication, reduce the jobs execution time, lower the ratio of failure execution, and improve the security of execution environment of important data. In this paper, a kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among computing nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayesian cognitive method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling (DLS) algorithm, the Trust-Dynamic Level Scheduling (Trust-DLS) algorithm is proposed. Moreover, a benchmark is structured to span a range of data intensive computing characteristics for evaluation the proposed method. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of data intensive workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in large-scale data intensive computing environment.     

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.