School of Computer Sciences, National College of Business Administration & Economics, Lahore 54700, Pakistan.
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Aljouf, Saudi Arabia.
Sensors (Basel). 2022 Jun 19;22(12):4627. doi: 10.3390/s22124627.
Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user's requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users' feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0-6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).
云计算 (CC) 提供了一系列技术的组合，使用户能够在最短的时间和最少的资金内使用最多的资源。CC 语义在通过使用不同云服务的属性对异构数据进行排序并实现最佳云服务方面起着关键作用。尽管为了实现对这一 CC 创新的简单访问付出了努力，但在存在各种组织以不同的成本和执行水平提供可比服务的情况下，根据用户的需求来识别理想的云服务要困难得多。在这项研究中，我们提出了一种基于本体映射和选择最佳服务的云服务排名代理 (CSRA)，用于分析使用最终用户反馈的云服务，包括平台即服务 (PaaS)、基础设施即服务 (IaaS) 和软件即服务 (SaaS)。所提出的 CSRA 拥有机器学习 (ML) 技术，可使用可用性、安全性、可靠性和成本等参数对云服务进行排名。这里使用了 Web 服务质量 (QWS) 数据集，其中包含七个主要的云服务类别，排名从 0-6，通过顺序最小优化回归 (SMOreg) 提取所需的有说服力的特征。SMOreg 的分类结果能够并且展示出通过所识别的参数识别最佳云服务的总体准确性约为 98.71%。SMOreg 的主要优点是 SMO 所需的内存量是线性的。研究结果表明，我们在精度方面的改进模型优于多层感知机 (MLP) 和线性回归 (LR) 等现有技术。
