In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualisation and containerisation technologies are usually used to execute customers' workloads and applications. The computational resources are provided through large-scale datacenters, which consume substantial amount of energy and have, therefore, ecological impacts. Since long, Google runs users' applications in containers, Rackspace offers bare-metal hardware, whereas AWS runs them either in VMs (EC2), containers (ECS) and/or containers inside VMs (Lambda); therefore, making resource management a tedious activity. The role of a resource management system is of the greatest importance, principally, if IT companies practice various kinds of sand-boxing technologies, for instance, bare-metal, VMs, containers, and/or nested containers in their datacenters (hybrid platforms). The absence of centralised, workload-aware resource managers and consolidation policies produces questions on datacenters energy efficiency, workloads performance, and users' costs. In this paper, we demonstrate, through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to four different kinds of workload, the likelihood of: (i) using workload-aware resource managers in hybrid clouds; (ii) achieving energy and cost savings, in heterogeneous hybrid datacenters such that the workload performance is not affected, negatively; and (iii) how various allocation policies, combined with different migration approaches, will impact on datacenter's energy and performance efficiencies. Using plausible assumptions for hybrid datacenters set-up, our empirical evaluation suggests that, for no migration, a single scheduler is at most 16.86% more energy efficient than distributed schedulers. Moreover, when migrations are considered, our resource manager can save up to 45.61% energy and can improve up to 17.9% workload performance.

在Google，Rackspace和Amazon Web Services（AWS）等主要信息技术（IT）公司中，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。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能源，因此具有生态影响。长期以来，Google在容器中运行用户的应用程序，Rackspace提供裸机硬件，而AWS在VM（EC2），容器（ECS）和/或VM内部的容器（Lambda）中运行它们；因此，使资源管理变得乏味。资源管理系统的作用至关重要，主要是，如果IT公司采用各种沙盒技术，例如裸机，虚拟机，容器，和/或嵌套容器在其数据中心（混合平台）中。由于缺乏集中的，可感知工作负载的资源管理器和整合策略，因此对数据中心的能效，工作负载性能和用户成本产生了疑问。在本文中，我们通过几次实验证明了使用Google的工作量数据获取12583个主机和大约一百万个任务的情况，这些任务属于四种不同的工作量：（i）在混合云中使用工作量感知的资源管理器；（ii）在异构混合数据中心中实现能源和成本节省，从而不会负面影响工作负载性能；（iii）各种分配策略结合不同的迁移方法将如何影响数据中心的能源和性能效率。根据混合数据中心设置的合理假设，我们的经验评估表明，对于不进行迁移的情况，单个调度程序的能源效率最多比分布式调度程序高16.86％。此外，考虑迁移时，我们的资源管理器可以节省多达45.61％的能源，并可以提高多达17.9％的工作负载性能。