Edge-cloud services provide heterogeneous virtual machine types to run various IoT workloads. Choosing the appropriate VM configuration for each workload can effectively improve performance and reduce costs. This article proposes ARVMEC, Adaptive Recommendation of Virtual Machines for IoT in Edge-Cloud Environment, which can always provide users with the best VM recommendation according to their own budget or deadline constraints. ARVMEC uses a tree-based ensemble learning algorithm to make accurate predictions on workload performance for all VM types. It can abstract user purposes in a more flexible and general mode, thus offer reasonable recommendations accordingly. Compared to state-of-art methods, ARVMEC can make better predictions with a 15% improvement in accuracy.

边缘云服务提供异构虚拟机类型，以运行各种物联网工作负载。为每个工作负载选择适当的VM配置可以有效地提高性能并降低成本。本文提出ARVMEC，甲daptive ř的ecommendation V irtual中号achines为的IoT在ë dge- Ç嘈杂的环境，可以始终根据用户自己的预算或截止日期限制为用户提供最佳的VM建议。ARVMEC使用基于树的集成学习算法来针对所有VM类型的工作负载性能做出准确的预测。它可以更灵活，更通用的方式抽象用户目的，从而相应地提供合理的建议。与最先进的方法相比，ARVMEC可以做出更好的预测，准确度提高15％。