Estimating power consumption in modern Cloud environments is essential for
carbon quantification toward green computing. Specifically, it is important to
properly account for the power consumed by each of the running applications,
which are packaged as containers. This paper examines multiple challenges
associated with this goal. The first challenge is that multiple customers are
sharing the same hardware platform (multi-tenancy), where information on the
physical servers is mostly obscured. The second challenge is the overhead in
power consumption that the Cloud platform control plane induces. This paper
addresses these challenges and introduces a novel pipeline framework for power
model training. This allows versatile power consumption approximation of
individual containers on the basis of available performance counters and other
metrics. The proposed model utilizes machine learning techniques to predict the
power consumed by the control plane and associated processes, and uses it for
isolating the power consumed by the user containers, from the server power
consumption. To determine how well the prediction results in an isolation, we
introduce a metric termed isolation goodness. Applying the proposed power model
does not require online power measurements, nor does it need information on the
physical servers, configuration, or information on other tenants sharing the
same machine. The results of cross-workload, cross-platform experiments
demonstrated the higher accuracy of the proposed model when predicting power
consumption of unseen containers on unknown platforms, including on virtual
machines.

本文研究了现代云环境中估计功耗的重要性，提出了一个新的管道框架用于训练功耗模型，通过机器学习技术预测云平台控制平面和相关进程所消耗的功耗，并利用该模型来隔离用户容器消耗的功耗。实验结果表明该模型在预测未知平台和虚拟机上的未知容器功耗时具有更高的准确性。

云原生运行时能耗指标导出的强大功率模型训练框架

A Robust Power Model Training Framework for Cloud Native Runtime Energy  Metric Exporter

Organizations struggle to handle sheer number of vulnerabilities in their
cloud environments. The de facto methodology used for prioritizing
vulnerabilities is to use Common Vulnerability Scoring System (CVSS). However,
CVSS has inherent limitations that makes it not ideal for prioritization. In
this work, we propose a new way of prioritizing vulnerabilities. Our approach
is inspired by how offensive security practitioners perform penetration
testing. We evaluate our approach with a real world case study for a large
client, and the accuracy of machine learning to automate the process end to
end.

本文介绍了一种新的漏洞优先级排序方法，受渗透测试的启发，通过一个真实的案例来验证了机器学习自动化流程的准确性。