The advent of the function virtualization concept, especially that of network functions, leads to important benefits for future networks. Although the orchestration of virtualized functions presents gains for network operators and clients alike, the overhead for moving functions has not been thoroughly explored so far, especially considering functions virtualized by using container technologies. In this work, we investigate orchestration costs associated with the migration of containerized virtual functions. To this end, we first perform a systematic literature review on state-of-the-art virtual function migration costs, electing time and data transferred as so. We then use a well-known container platform (LXD) to perform several orchestration experiments in a controlled environment. By analyzing the container migration process in smaller complementary steps, and designing experiments to evaluate them individually, a pattern for migration costs is observed. Linear regression is then used to derive a prediction model for the necessary time and data transferring for performing a container migration. To assess the predictor’s accuracy, we present a cloud computing use case where the predictor is deployed. Results indicate that predictions can be accurate within reasonable range, and therefore orchestration algorithms may be improved by accounting for similar prediction models when determining the migration of one or more virtualized functions.

功能虚拟化概念（尤其是网络功能的概念）的出现为未来的网络带来了重要的好处。尽管虚拟化功能的编排为网络运营商和客户端都带来了好处，但到目前为止，尚未充分探讨移动功能的开销，尤其是考虑到使用容器技术实现虚拟化的功能。在这项工作中，我们调查与容器化虚拟功能的迁移相关的编排成本。为此，我们首先对最新的虚拟功能迁移成本，选择时间和传输数据进行系统的文献综述。然后，我们使用众所周知的容器平台（LXD）在受控环境中执行多个业务流程实验。通过在较小的互补步骤中分析容器迁移过程，并设计实验进行单独评估，可以观察到迁移成本的模式。然后，使用线性回归来导出用于执行容器迁移所需的时间和数据传输的预测模型。为了评估预测变量的准确性，我们提出了部署预测变量的云计算用例。结果表明预测可以在合理范围内准确，因此在确定一个或多个虚拟化功能的迁移时，可以通过考虑类似的预测模型来改进编排算法。然后，使用线性回归来导出用于执行容器迁移所需的时间和数据传输的预测模型。为了评估预测变量的准确性，我们提出了部署预测变量的云计算用例。结果表明预测可以在合理范围内准确，因此在确定一个或多个虚拟化功能的迁移时，可以通过考虑类似的预测模型来改进编排算法。然后，使用线性回归来导出用于执行容器迁移所需的时间和数据传输的预测模型。为了评估预测变量的准确性，我们提出了部署预测变量的云计算用例。结果表明预测可以在合理范围内准确，因此在确定一个或多个虚拟化功能的迁移时，可以通过考虑类似的预测模型来改进编排算法。