In this paper, we address the challenge of optimizing task scheduling in cloud environments by systematically evaluating and comparing various methodologies, namely heuristics, meta-heuristics, and deep reinforcement learning (DeepRL). The context of our study is driven by the need for efficient resource allocation and management in dynamic and large-scale cloud systems to minimize servers’ power consumption. We employ the framework of Hegelian dialectics to dissect the strengths, weaknesses, and practical limitations of each approach. Our experiments, conducted using Alibaba Trace Data, provide empirical evidence that underscores the effectiveness of different methods in specific cloud scenarios. The key contributions of this study include a comprehensive analysis of each methodology’s performance, findings regarding their scalability, and new perspectives for optimizing both energy consumption and maximum processing time while scheduling tasks in a cloud system.