Fuzzy vs. Crisp in Uncertainty-Aware Service Selection: Enabling Sustainability on Multimedia Event Processing

The high energy consumption projections of Cloud/Edge applications urge the development of ecologically sustainable Multimedia Event Processing (MEP) systems. In these applications, a Multi-Criteria Decision Making (MCDM) problem must be solved when selecting the best service workers alternatives for processing user queries, according to the user-specific performance criteria requirements of energy, accuracy and speed. Moreover, fuzzy logic provides a well-established method, such as the fuzzy TOPSIS, for dealing with the uncertainties arising from real-world scenarios, where ambiguities of user requirement interpretations and imprecision of measurement of the computing devices may directly impact this decision-making process. However, this fuzzy method is more complex and computationally intensive than the original (crisp) TOPSIS. Therefore, it is crucial to understand to what degree the fuzzy and crisp methods may be used interchangeably and still get the same results to avoid unnecessary complexity in sustainable MEP solutions in a real-world context. In our work, we developed a fuzzy TOPSIS ranking method for handling the uncertainties of the user criteria weights and service worker alternative ratings. Contrary to a previous study, we provide evidence that replacing the fuzzy TOPSIS method with its crisp counterpart significantly affects the ranking results when applied to a real-world scenario, with contradiction rates higher than 60% in most scenarios explored, which suggests that it is not viable to interchange these methods without consequences to the sustainability efforts of an MEP application.