Determining the superiority of a robust cloud fault tolerance mechanism using a spherical cubic fuzzy set-based decision approach

Abstract

Ensuring the availability of robust fault tolerance mechanisms is crucial for providing reliable cloud computing services. The complexity of cloud system components, combined with the wide range of fault tolerance frameworks proposed in numerous studies, makes identifying the optimal cloud fault tolerance framework a significant challenge. These frameworks, typically based on either reactive fault tolerance (RFT) or proactive fault tolerance (PFT) mechanisms, can be evaluated using distinct attributes. However, determining the superiority of one framework over another is not straightforward due to several factors: the multiplicity of performance attributes, trade-offs among these attributes, decisions regarding their relative importance, observed variations in attribute data across different frameworks, and nature of the subjective evaluation. To address this challenge, this paper proposes a decision-making approach using multiple attributes decision-making (MADM) methods, including the Fuzzy Decision by Opinion Score Method (FDOSM) and the Fuzzy Weighted with Zero Inconsistency Criterion (FWZIC) method, extended and formulated within Spherical Cubic Fuzzy Sets (SCFS) and integrated with the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE). The developed SCFS–FWZIC method prioritizes the performance attributes of cloud fault tolerance frameworks, while the SCFS–FDOSM method is developed to transform the evaluation values of each framework into scores. PROMETHEE is then employed to rank 19 frameworks under the RFT category and 7 frameworks under the PFT category to identify the optimal one. The results indicate that AFTRC under RFT and ASSURE under PFT ranked highest due to their essential attributes, while SAFTP under RFT and PFHC₂ under PFT received the lowest ranks. Sensitivity, correlation, and comparison analyses were conducted to validate and assess the stability and robustness of the proposed methods. The implications of this study are likely to benefit a variety of stakeholders, including organizations and managers.