In the recent years, emergence huge Edge-Cloud environments faces great challenges like the ever-increasing energy demand, the extensive Internet of Things (IoT) devices adaptation, and the goals of efficiency and reliability. Containers has become increasingly popular to encapsulate various services and container migration among Edge-Cloud nodes may enable new use cases in various IoT domains. In this study, an efficient joint VM and container consolidation solution is proposed for Edge-Cloud environment. The proposed method uses the Auto-Encoder (AE) and TOPSIS modules for two stages of consolidation subproblems, namely, Joint VM and Container Multi-criteria Migration Decision (AE-TOPSIS-JVCMMD) and Edge-Cloud Power SLA Aware (AE-TOPSIS-ECPSA) for VM placement. The module extracts the contribution of different criteria and computes the scores of all the alternatives. Combining the non-linear contribution learning ability of the AE algorithm and the intelligent ranking of the TOPSIS algorithm, the proposed method successfully avoids the bias of conventional multi-criteria approaches toward alternatives that have good evaluations in two or more dependent criteria. The simulations conducted using the Cloudsim simulator confirm the effectiveness of the proposed policies, demonstrating to 41.5%, 30.13%, 12.9%, 10.3%, 58.2% and 56.1% reductions in energy consumption, SLA violation, response time, running cost, number of VM migrations, and number of container migrations, respectively in comparison with state of the arts.

中文翻译：

---

在 Edge-Cloud 环境中，通过基于自动编码器的贡献提取来联合 VM 和容器


近年来，新兴的巨大边缘云环境面临着巨大的挑战，例如不断增长的能源需求、广泛的物联网 （IoT） 设备适应以及效率和可靠性的目标。容器封装各种服务越来越流行，边缘云节点之间的容器迁移可能会在各种物联网领域实现新的用例。在本研究中，针对边缘云环境提出了一种高效的 VM 和容器联合整合解决方案。所提出的方法使用自动编码器 （AE） 和 TOPSIS 模块来处理整合子问题的两个阶段，即 VM 和容器联合多标准迁移决策 （AE-TOPSIS-JVCMMD） 和边缘云电源 SLA 感知 （AE-TOPSIS-ECPSA） 用于 VM 放置。该模块提取不同标准的贡献并计算所有备选方案的分数。结合AE算法的非线性贡献学习能力和TOPSIS算法的智能排序，所提方法成功地避免了传统多标准方法对在两个或多个依赖标准中具有良好评价的替代方案的偏见。使用 Cloudsim 模拟器进行的模拟证实了拟议策略的有效性，与最先进的技术相比，能耗、SLA 违规、响应时间、运行成本、VM 迁移数量和容器迁移数量分别降低了 41.5%、30.13%、12.9%、10.3%、58.2% 和 56.1%。