Can We Recycle Our Old Models? An Empirical Evaluation of Model Selection Mechanisms for AIOps Solutions

TL;DR

This paper empirically evaluates model selection mechanisms for AIOps, demonstrating that temporal adjacency-based selection can outperform periodic retraining and identifying a performance gap that invites further research.

Contribution

It is the first to assess historical model selection mechanisms in AIOps, providing insights into their effectiveness and highlighting areas for future improvement.

Findings

Temporal adjacency-based mechanisms outperform periodic retraining.

Model selection mechanisms show a performance gap compared to the theoretical upper bound.

Evaluation conducted on large-scale datasets from Google, Alibaba, and BackBlaze.

Abstract

AIOps (Artificial Intelligence for IT Operations) solutions leverage the tremendous amount of data produced during the operation of large-scale systems and machine learning models to assist software practitioners in their system operations. Existing AIOps solutions usually maintain AIOps models against concept drift through periodical retraining, despite leaving a pile of discarded historical models that may perform well on specific future data. Other prior works propose dynamically selecting models for prediction tasks from a set of candidate models to optimize the model performance. However, there is no prior work in the AIOps area that assesses the use of model selection mechanisms on historical models to improve model performance or robustness. To fill the gap, we evaluate several model selection mechanisms by assessing their capabilities in selecting the optimal AIOps models that were built in the past to make predictions for the target data. We performed a case study on three large-scale public operation datasets: two trace datasets from the cloud computing platforms of Google and Alibaba, and one disk stats dataset from the BackBlaze cloud storage data center. We observe that the model selection mechnisms utilizing temporal adjacency tend to have a better performance and can prevail the periodical retraining approach. Our findings also highlight a performance gap between existing model selection mechnisms and the theoretical upper bound which may motivate future researchers and practitioners in investigating more efficient and effective model selection mechanisms that fit in the context of AIOps.