  Predicting future resource demand in Cloud Computing is essential for
optimizing the trade-off between serving customers' requests efficiently and
minimizing the provisioning cost. Modelling prediction uncertainty is also
desirable to better inform the resource decision-making process, but research
in this field is under-investigated. In this paper, we propose univariate and
bivariate Bayesian deep learning models that provide predictions of future
workload demand and its uncertainty. We run extensive experiments on Google and
Alibaba clusters, where we first train our models with datasets from different
cloud providers and compare them with LSTM-based baselines. Results show that
modelling the uncertainty of predictions has a positive impact on performance,
especially on service level metrics, because uncertainty quantification can be
tailored to desired target service levels that are critical in cloud
applications. Moreover, we investigate whether our models benefit transfer
learning capabilities across different domains, i.e. dataset distributions.
Experiments on the same workload datasets reveal that acceptable transfer
learning performance can be achieved within the same provider (because
distributions are more similar). Also, domain knowledge does not transfer when
the source and target domains are very different (e.g. from different
providers), but this performance degradation can be mitigated by increasing the
training set size of the source domain.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Machine Learning