Energy-Efficient Per-Core DVFS for Virtual Machine Management in Cloud Data Centers
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Abstract
Energy efficiency and thermal management are critical challenges in virtualized cloud data centers, particularly for optimizing parallel workloads. Dynamic Voltage and Frequency Scaling (DVFS) is widely used to balance power consumption and computational performance. This study proposes an Adaptive Threshold Per-Core DVFS Governor that dynamically adjusts CPU core frequencies based on per-core utilization, improving energy efficiency and workload performance. The proposed algorithm is evaluated using Charm++ parallel workloads and is benchmarked against existing Linux governors, including the Conservative, OnDemand, and Performance governors. Experimental results demonstrate that the proposed approach achieves superior energy efficiency per Giga-instructions compared to the Conservative and OnDemand governors while maintaining performance levels comparable to the Performance governor. Furthermore, the proposed method reduces average CPU temperature by approximately 5% (2.5◦C lower) compared to the Performance governor, contributing to enhanced thermal management in cloud computing environments. These findings highlight the potential of the adaptive per-core DVFS mechanism for improving energy efficiency and performance in virtualized data centers.
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