Currently, enormous amounts of data are continuously processed to support our daily activities, such as managing bank accounts, streaming movies, or interacting on social networks. In recent years, cloud infrastructures have proven to be a reliable solution, not only for processing this data but also for enabling users worldwide to access it remotely. However, this processing demands vast computing resources, leading to significant energy consumption. In this paper, we present a strategy to address this problem by combining multi-objective optimization techniques with Metamorphic Testing (MT) and simulation tools to optimize cloud systems, focusing on both performance and energy consumption. To achieve this, several multi-objective genetic algorithms (MOGAs) have been integrated into the MT-EA4Cloud framework, a solution that previously applied single-objective evolutionary algorithms with MT. To determine the suitability of the proposed approach, an empirical study was conducted to analyze the behavior of the different MOGAs included in the framework. In this study, various test sets and two distinct workloads-inspired by big data analytics operations-were created to represent multiple cloud scenarios. The results clearly demonstrate that MOGAs can be effectively combined with MT to optimize cloud systems while considering multiple objectives-in this case, performance and energy consumption. A careful analysis of the results indicates that increasing the mutation rate leads to the best outcomes. In general, the NSGA-II algorithm has produced the best results in the experiments conducted in this study.
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