Recent telecommunication paradigms, such as big data, Internet of Things (IoT), ubiquitous edge computing (UEC), and machine learning, are encountering with a tremendous number of complex applications that require different priorities and resource demands. These applications usually consist of a set of virtual machines (VMs) with some predefined traffic load between them. The efficiency of a cloud data center (CDC) as prominent component in UEC significantly depends on the efficiency of its VM placement algorithm applied. However, VM placement is an NP-hard problem and thus there exist practically no optimal solution for this problem. In this paper, motivated by this, we propose a priority, power and traffic-aware approach for efficiently solving the VM placement problem in a CDC. Our approach aims to jointly minimize power consumption, network consumption and resource wastage in a multi-dimensional and heterogeneous CDC. To evaluate the performance of the proposed method, we compared it to the state-of-the-art on a fat-tree topology under various experiments. Results demonstrate that the proposed method is capable of reducing the total network consumption up to 29%, the consumption of power up to 18%, and the wastage of resources up to 68%, compared to the second-best results.

大数据，物联网（IoT），无处不在的边缘计算（UEC）和机器学习等最新的电信范例正在遇到大量需要不同优先级和资源需求的复杂应用程序。这些应用程序通常由一组虚拟机（VM）组成，它们之间具有一些预定义的流量负载。作为UEC中重要组成部分的云数据中心（CDC）的效率在很大程度上取决于所应用的VM放置算法的效率。但是，VM的放置是一个NP难题，因此实际上没有针对此问题的最佳解决方案。为此，本文提出了一种优先级，功耗和流量感知方法，以有效解决CDC中的VM放置问题。我们的方法旨在共同降低功耗，多维和异构CDC中的网络消耗和资源浪费。为了评估所提出方法的性能，我们将其与各种实验下的胖树拓扑结构上的最新技术进行了比较。结果表明，与第二好的结果相比，该方法能够减少高达29％的总网络消耗，高达18％的功耗和68％的资源浪费。