Unsupervised learning for anomaly detection based on memory and cpu usage

Recently got into Data Science, I've been working on a data science project, I have built a system that collects real-time logs on virtual machines in the cloud, the logs include memory consumption and cpu usage data.

I started researching online and discovered endless amount of information on these topics, however I couldn't find an answer or figure out which unsupervised model would best suit my needs (time series analysis, isolation forest, statistical model, etc...); a model that could detect anomalies (or at least determine a confidence level that a given data point is anomalous) that combines memory and cpu usage data.

Should I analyze each metric by itself or together, and if so- what's the correct way to do that? sum? weighted sum? quotient?

However those are the closest references that I've found:

Unsupervised Anomaly Detection on system metrics like memory, cpu, io, net, etc

https://uu.diva-portal.org/smash/get/diva2:1334370/FULLTEXT01.pdf

I would really appreciate any type of resource that might be helpful or any other guidance...

Thanks!