Currently, many businesses are using cloud computing to obtain an entire IT infrastructure remotely while delegating its management to a third party. The provider of this architecture ensures the operation and maintenance of the services while offering management capabilities via web consoles.These providers offer solutions based on bare metal or virtualization platforms (mainly virtual machines). Recently, a new type of virtualization-based on containerization technology has emerged. Containers can be deployed on bare metal servers or in virtual machines. The migration of virtual machines (VMs) and Containers in Dynamic Resource Management (DRM) is a crucial factor in minimizing the operating costs of data centers by reducing their energy consumption and subsequently limiting their impact on climate change.In this article, live migration for both types of virtualization will be studied. for that, container placement and migration algorithms are proposed, which takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, a dynamic approach is suggested based on a threshold of RAM usage for host and virtual machines in the data center to avoid unnecessary power consumption. In this paper, the proposed work is compared with VM/Container placement and migration methods, the results of the experiment indicate that using container migration instead of VMs demonstrates a reduction in power consumption, and also reduces the migration time which impacts QoS and reduces SLA violation.

Currently, data centers energy consumption in the cloud is attracting a lot of interest. One of the most approaches to optimize energy and cost in data centers is virtualization. Recently, a new type of container-based virtualization has appeared, containers are considered very light and modular virtual machines, they offer great flexibility and the possibility of migration from one environment to another, which allows optimizing applications for the cloud. Another approach to saving energy is to consolidate the workload, which is the amount of processing that the computer has to perform at any given time. In this article, we will study the container placement algorithm that takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, we proposed a Hybrid approach for managing resources and workload based on ant colony optimization (ACO) and the first-fit decreasing (FFD) algorithm to avoid unnecessary power consumption. The results of the experiment indicate that using the first-fit decreasing algorithm (FFD) for container placement is better than ant colony optimization especially in a homogeneous systems. On the other hand the ant colony optimization shows very satisfying results in the case of workload management.

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