Department of CSE, C V Raman Global University, Mahura, Jalna Bhubneshwar, Odisha, India.
Department of CSE, Netaji Subhas University of Technology, New Delhi, India.
Comput Intell Neurosci. 2022 May 25;2022:2019485. doi: 10.1155/2022/2019485. eCollection 2022.
Cloud computing has grown as a computing paradigm in the last few years. Due to the explosive increase in the number of cloud services, QoS (quality of service) becomes an important factor in service filtering. Moreover, it becomes a nontrivial problem when comparing the functionality of cloud services with different performance metrics. Therefore, optimal cloud service selection is quite challenging and extremely important for users. In the existing approaches of cloud service selection, the user's preferences are offered by the user in a quantitative form. With fuzziness and subjectivity, it is a hurdle task for users to express clear preferences. Moreover, many QoS attributes are not independent but interrelated; therefore, the existing weighted summation method cannot accommodate correlations among QoS attributes and produces inaccurate results. To resolve this problem, we propose a cloud service framework that takes the user's preferences and chooses the optimal cloud service based on the user's QoS constraints. We propose a cloud service selection algorithm, based on principal component analysis (PCA) and the best-worst method (BWM), which eliminates the correlations between QoS and provides the best cloud services with the best QoS values for users. In the end, a numerical example is shown to validate the effectiveness and feasibility of the proposed methodology.
云计算在过去几年中已经发展成为一种计算模式。由于云服务数量的爆炸式增长，服务质量（QoS）成为服务过滤的一个重要因素。此外，当比较具有不同性能指标的云服务的功能时，这就成为了一个非平凡的问题。因此，对于用户来说，最优的云服务选择是极具挑战性和至关重要的。在现有的云服务选择方法中，用户的偏好是由用户以定量的形式提供的。由于模糊性和主观性，用户很难清楚地表达自己的偏好。此外，许多 QoS 属性并不是相互独立的，而是相互关联的；因此，现有的加权求和方法无法适应 QoS 属性之间的相关性，从而产生不准确的结果。为了解决这个问题，我们提出了一个基于用户的 QoS 约束的云服务框架，该框架根据用户的偏好来选择最优的云服务。我们提出了一种基于主成分分析（PCA）和最佳最差方法（BWM）的云服务选择算法，该算法消除了 QoS 之间的相关性，并为用户提供了具有最佳 QoS 值的最佳云服务。最后，通过一个数值实例验证了所提出方法的有效性和可行性。
