In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualization and containerization technologies are usually used to execute customers’ workloads and applications — as part of their cloud computing services offering. The computational resources are provided through large-scale datacentres, which consume substantial amount of energy and, consequently, affect our environment with global warming. Cloud datacentres have become a backbone for today’s business and economy, which are the fastest-growing electricity consumers, globally. Numerous studies suggest that $\sim$30% of the US datacentres are comatose and the others are grossly less-utilized, which make it possible to save energy through technologies like virtualization and containerization. These technologies provide support for allocation and consolidation of workloads on appropriate resources. However, consolidation comprises migrations of virtual machines (VMs), containers and/or applications, depending on the underlying virtualization method; that are expensive in terms of energy consumption, performance degradation, and therefore, costs which is mostly not accounted for in many existing models, and, possibly, it could be more energy and performance efficient not to consolidate. This paper describes energy consumption and performance, therefore, cost issues of large-scale datacentres. Besides, we cover various methods for energy and performance efficient distributed systems, clouds and datacentres. We elaborate energy efficiency methods at three different levels: hardware; resource management; and applications. Besides these, different performance management techniques are mapped onto taxonomies and described in details. In last, energy, performance and cost management techniques, at geographically distributed and multi-access edge computing platforms, are described along with critical discussion.

在主要的信息技术（IT）公司（例如Google，Rackspace和Amazon Web Services（AWS））中，虚拟化和容器化技术通常用于执行客户的工作负载和应用程序-作为其云计算服务产品的一部分。计算资源是通过大型数据中心提供的，这些数据中心消耗大量的能源，因此，随着全球变暖，我们的环境受到影响。云数据中心已成为当今业务和经济的骨干，而这些业务和经济是全球增长最快的电力消费者。大量研究表明$〜$美国30％的数据中心是昏迷的，其他数据中心的利用率很低，这使得可以通过虚拟化和容器化等技术来节省能源。这些技术为在适当资源上分配和合并工作负载提供支持。但是，合并包括虚拟机（VM），容器和/或应用程序的迁移，具体取决于底层的虚拟化方法。它们在能耗，性能下降以及因此在许多现有模型中通常未考虑到的成本方面很昂贵，并且可能在不整合的情况下具有更高的能源和性能效率。本文描述了能耗和性能，因此，描述了大型数据中心的成本问题。除了，我们涵盖了各种用于能源和性能高效的分布式系统，云和数据中心的方法。我们在三个不同的层面上详细阐述了能效方法：硬件；资源管理; 和应用程序。除此之外，将不同的绩效管理技术映射到分类法中并进行详细描述。最后，介绍了地理分布和多访问边缘计算平台上的能源，性能和成本管理技术，并进行了重要讨论。