With the rapid development of cloud computing, various types of cloud services are available in the marketplace. However, it remains a significant challenge for cloud users to find suitable services for two major reasons: (1) Providers are unable to offer services in complete accordance with their declared Service Level Agreements, and (2) it is difficult for customers to describe their requirements accurately. To help users select cloud services efficiently, this article presents a Trust enabled Self-Learning Agent Model for service Matching (TSLAM). TSLAM is a multi-agent-based three-layered cloud service market model, in which different categories of agents represent the corresponding cloud entities to perform market behaviors. The unique feature of brokers is that they are not only the service recommenders but also the participants of market competition. We equip brokers with a learning module enabling them to capture implicit service demands and find user preferences. Moreover, a distributed and lightweight trust model is designed to help cloud entities make service decisions. Extensive experiments prove that TSLAM is able to optimize the cloud service matching process and compared to the state-of-the-art studies, TSLAM improves user satisfaction and the transaction success rate by at least 10%.

中文翻译：

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TSLAM

随着云计算的快速发展，市场上出现了各种类型的云服务。然而，对于云用户来说，找到合适的服务仍然是一个重大挑战，主要有两个原因：(1) 提供商无法完全按照其声明的服务水平协议提供服务，以及 (2) 客户难以描述他们的服务。要求准确。为了帮助用户有效地选择云服务，本文提出了一种基于信任的自学习代理模型，用于服务匹配（TSLAM）。TSLAM 是一种基于多智能体的三层云服务市场模型，其中不同类别的智能体代表相应的云实体来执行市场行为。经纪人的独特之处在于，他们不仅是服务推荐者，还是市场竞争的参与者。我们为经纪人配备了一个学习模块，使他们能够捕捉隐含的服务需求并找到用户偏好。此外，分布式轻量级信任模型旨在帮助云实体做出服务决策。大量实验证明，TSLAM 能够优化云服务匹配过程，与现有研究相比，TSLAM 将用户满意度和交易成功率提高了至少 10%。