  A computational workflow, also known as workflow, consists of tasks that are
executed in a certain order to attain a specific computational campaign.
Computational workflows are commonly employed in science domains, such as
physics, chemistry, genomics, to complete large-scale experiments in
distributed and heterogeneous computing environments. However, running
computations at such a large scale makes the workflow applications prone to
failures and performance degradation, which can slowdown, stall, and ultimately
lead to workflow failure. Learning how these workflows behave under normal and
anomalous conditions can help us identify the causes of degraded performance
and subsequently trigger appropriate actions to resolve them. However, learning
in such circumstances is a challenging task because of the large volume of
high-quality historical data needed to train accurate and reliable models.
Generating such datasets not only takes a lot of time and effort but it also
requires a lot of resources to be devoted to data generation for training
purposes. Active learning is a promising approach to this problem. It is an
approach where the data is generated as required by the machine learning model
and thus it can potentially reduce the training data needed to derive accurate
models. In this work, we present an active learning approach that is supported
by an experimental framework, Poseidon-X, that utilizes a modern workflow
management system and two cloud testbeds. We evaluate our approach using three
computational workflows. For one workflow we run an end-to-end live active
learning experiment, for the other two we evaluate our active learning
algorithms using pre-captured data traces provided by the Flow-Bench benchmark.
Our findings indicate that active learning not only saves resources, but it
also improves the accuracy of the detection of anomalies.
