Cloud computing is an opened and distributed network that guarantees access to a large amount of data and IT infrastructure at several levels (software, hardware...). With the increase demand, handling clients' needs is getting increasingly challenging. Responding to all requesting clients could lead to security breaches, and since it is the provider's responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients reliability. Although filtering clients in the cloud is not so common, it is required to assure cloud safety. In this paper, by implementing multi agent systems in the cloud to handle interactions for the providers, trust is introduced at agent level to filtrate the clients asking for services by using Particle Swarm Optimization and acquaintance knowledge to determine malicious and untrustworthy clients. The selection depends on previous knowledge and overall rating of trusted peers. The conducted experiments show that the model outputs relevant results, and even with a small number of peers, the framework is able to converge to the best solution. The model presented in this paper is a part of ongoing work to adapt interactions in the cloud.

翻译：云计算是一个开放和分布式的网络,可以保证获得多个级别(软件、硬件......)的大量数据和信息技术基础设施。随着需求的增加,处理客户需求变得日益具有挑战性。回应所有请求客户可能会导致安全违约,而且由于提供方不仅负责确保所提供的云服务,而且确保数据可靠,因此必须确保客户的可靠性。虽然云中过滤客户并不常见,但必须确保云层安全。在本文中,通过在云层中安装多剂系统,处理供应商的互动,在代理人一级引入信任,通过利用Partle Swarm Opptimization和熟识知识过滤客户要求服务的请求客户,以确定恶意和不可信的客户。选择取决于先前的知识以及信任同行的总体评级。进行的实验表明,模型输出的结果相关结果,即使有少量同行,框架也能够与最佳解决方案汇合。本文中介绍的模式是当前在云层中调整互动工作的一部分。