基于b树的网格资源选择信任模型

P. Varalakshmi, S. Thamarai Selvi, A. Ashraf, K. Karthick
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引用次数: 16

摘要

在网格环境中,信任管理是一个重要问题,在网格环境中,消费者和服务提供者在地理上分布在自治管理域之间。在本文中,我们提出了基于信誉的信任管理架构,通过使用中介,经纪人。这种体系结构坚持在每个域中使用多个代理。实体(消费者和服务提供商)分布在这些代理中,其中每个实体都与多个代理相关联。这提高了在代理站点上维护的信息的冗余性,从而提高了可靠性。在处理消费者请求和反馈时,这也减轻了代理站点的网络流量。本文很好地解决了这种多个代理的安排所产生的问题,即代理之间的实体分布和跨代理的信息一致性维护。在每笔交易完成后,对服务提供商和消费者的信任指数进行动态评估和更新。与其他基于声誉的信任模型(在请求时计算信任索引)相比,这使得使用者能够更快地接收来自代理的响应。为了进一步提高每笔交易的响应时间,提出了一种b树索引方案。信任评估中考虑了交易的满意值、交易数量、交易成本和交易关键度等信任参数,以及过去和最近交易的不同权重。我们的模型显示,针对不同百分比的恶意实体,工作成功率有了显著提高。每个事务的选择查询成本降低了,从而提高了总体响应时间,如该模型所示。本文还讨论了经纪人反馈对信任指数计算的影响
本文章由计算机程序翻译,如有差异,请以英文原文为准。
B-Tree Based Trust Model for Resource Selection in Grid
Trust management is an important issue in a grid environment where consumers and service providers are distributed geographically across autonomous administrative domains. In this paper, we propose reputation-based trust management architecture through the use of intermediaries, brokers. This architecture insists on multiple brokers in each domain. The entities (Consumers and the SPs) are distributed across these brokers, with each of these entities being associated with more than one broker. This improves the redundancy of information maintained at the broker sites, thereby improving the reliability. This also eases the network traffic at the broker sites while handling consumer requests and feedbacks. The issues rising out of such an arrangement of multiple brokers, namely the distribution of entities among the brokers and maintenance of consistency of information across the brokers, are addressed well in this paper. Trust-indices of SPs and consumers are evaluated and updated dynamically after the completion of each transaction. This enables the consumer to receive the response from the broker significantly quicker compared to other reputation-based-trust models where the trust-indices are computed at the request-time. To further improve the response time of each transaction, a B-tree indexing scheme has been proposed. Trust parameters such as satisfaction-value, number, cost and criticality of transactions, and different weights for past and recent transactions are considered for the trust evaluation. Our model shows a marked improvement in job success rate for various percentages of malicious entities. The selection query cost for each transaction is reduced thereby improving the overall response time is show-cased in this model. The impact of broker's feedback on the computation of trust-indices is also presented
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