Multi-tenancy in public clouds may lead to co-location interference on shared
resources, which possibly results in performance degradation of cloud
applications. Cloud providers want to know when such events happen and how
serious the degradation is, to perform interference-aware migrations and
alleviate the problem. However, virtual machines (VM) in
Infrastructure-as-a-Service public clouds are black-boxes to providers, where
application-level performance information cannot be acquired. This makes
performance monitoring intensely challenging as cloud providers can only rely
on low-level metrics such as CPU usage and hardware counters.
We propose a novel machine learning framework, Alioth, to monitor the
performance degradation of cloud applications. To feed the data-hungry models,
we first elaborate interference generators and conduct comprehensive
co-location experiments on a testbed to build Alioth-dataset which reflects the
complexity and dynamicity in real-world scenarios. Then we construct Alioth by
(1) augmenting features via recovering low-level metrics under no interference
using denoising auto-encoders, (2) devising a transfer learning model based on
domain adaptation neural network to make models generalize on test cases unseen
in offline training, and (3) developing a SHAP explainer to automate feature
selection and enhance model interpretability. Experiments show that Alioth
achieves an average mean absolute error of 5.29% offline and 10.8% when testing
on applications unseen in the training stage, outperforming the baseline
methods. Alioth is also robust in signaling quality-of-service violation under
dynamicity. Finally, we demonstrate a possible application of Alioth's
interpretability, providing insights to benefit the decision-making of cloud
operators. The dataset and code of Alioth have been released on GitHub.

通过构建一个新颖的机器学习框架 Alioth 来监控云应用性能降低，并提供对决策的洞察力。