Virtual machine placement is a crucial challenge in cloud computing for efficiently utilizing physical machine resources in data centers. Virtual machine placement can be formulated as a MinUsageTime Dynamic Vector Bin Packing (DVBP) problem, aiming to minimize the total usage time of the physical machines. This paper evaluates state-of-the-art MinUsageTime DVBP algorithms in non-clairvoyant, clairvoyant and learning-augmented online settings, where item durations (virtual machine lifetimes) are unknown, known and predicted, respectively. Besides the algorithms taken from the literature, we also develop several new algorithms or enhancements. Empirical experimentation is carried out with real-world datasets of Microsoft Azure. The insights from the experimental results are discussed to explore the structures of algorithms and promising design elements that work well in practice.

翻译：虚拟机放置是云计算中高效利用数据中心物理机资源的关键挑战。虚拟机放置可建模为最小使用时间动态向量装箱（DVBP）问题，其目标是最小化物理机的总使用时间。本文在非预知、预知及学习增强的在线设置下评估了最先进的最小使用时间DVBP算法，其中项目持续时间（虚拟机生命周期）分别为未知、已知和预测状态。除了文献中的现有算法外，我们还开发了若干新算法及改进方案。实验采用微软Azure的真实数据集进行实证研究，通过分析实验结果探讨了算法的内在结构及在实践中表现优异的设计要素。