Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy consumption of a cluster of server machines. Our solution is embodied through the use of sensors embedded inside physical server machines, enabling the introduction of new features for sensitive thermal awareness and proactive hot spot avoidance. Leveraging this significantly enhanced feature space, we implement data-driven predictive machine learning models along with a heuristic placement algorithm (CPP), enabling proactive VM placements that are both energy-aware and thermal-aware. Indeed, experiments carried out on a cluster of physical server machines demonstrate high performance by both the ML models and the placement algorithm (CPP). Compared with the best baseline algorithm, our solution reduced power consumption and temperature by 7% and 2%, respectively, while avoiding hot spots and maintaining efficient load distribution, thereby reducing the overhead of physical machines by 28%.

中文翻译：

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使用嵌入式传感器和机器学习实现最低能耗虚拟机放置


云数据中心 (DC) 消耗大量能源，严重影响环境问题。此外，随着5G和B5G网络的出现，越来越以软件为导向并高度依赖云计算，优化其能源消耗势在必行。因此，在本研究中，我们提出了一种虚拟机放置算法，可以最大限度地减少服务器集群的能耗。我们的解决方案通过使用嵌入物理服务器机器内的传感器来体现，从而能够引入敏感热感知和主动热点避免的新功能。利用这一显着增强的功能空间，我们实现了数据驱动的预测机器学习模型以及启发式布局算法（CPP），从而实现了能源感知和热感知的主动虚拟机布局。事实上，在物理服务器集群上进行的实验证明了 ML 模型和放置算法 (CPP) 的高性能。与最佳基线算法相比，我们的解决方案功耗和温度分别降低了7%和2%，同时避免了热点并保持了高效的负载分配，从而使物理机的开销降低了28%。