<p>Energy efficiency and effective resource utilization continue to be major challenges in cloud data centers, largely due to fluctuating workloads and uneven physical server utilization. Virtual machine (VM) scheduling is a key mechanism for addressing these issues, especially in large-scale environments where decisions must be taken quickly while accounting for multiple resource constraints. Such requirements are particularly evident in HPC-backed cloud infrastructures, where scalability and real-time responsiveness are essential. This work presents a detailed analytical and simulation-based evaluation of multi-criteria decision-making (MCDM) techniques for VM scheduling within the power and resource utilization-aware virtual machine scheduling (PRUVMS) framework. Unlike the original PRUVMS model, which relies on a VIKOR-based ranking strategy using only CPU utilization, this work extends the evaluation by jointly considering CPU, memory, and network bandwidth as decision criteria. In addition, three widely used MCDM methods AHP, VIKOR, and TOPSIS are comparatively analyzed. The proposed evaluation is conducted using CloudSim with large-scale workload traces. The results show that VIKOR and TOPSIS offer more consistent performance when multiple resource metrics are considered simultaneously, making them suitable for large-scale data centers with stringent scheduling deadlines. In contrast, AHP exhibits more conservative consolidation behavior, resulting in fewer host shutdowns and making it a viable option for smaller data centers with limited resource capacity.</p>