Clustering-Based Numerosity Reduction for Cloud Workload Forecasting

Rossi, Andrea; Visentin, Andrea; Prestwich, Steven; Brown, Kenneth N.

doi:10.1007/978-3-031-49361-4_7

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2024

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Cited by 2 publications

References 42 publications

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Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation

Lackinger,

Morichetta,

Dustdar

2024

2024 IEEE International Conference on Service-Oriented System Engineering (SOSE)

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Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation

Lackinger,

Morichetta,

Dustdar

2024

2024 IEEE International Conference on Service-Oriented System Engineering (SOSE)

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Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Domain Generalization

Rossi,

Visentin,

Carraro

et al. 2024

Preprint

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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.

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Clustering-Based Numerosity Reduction for Cloud Workload Forecasting

Cited by 2 publications

References 42 publications

Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation

Time Series Predictions for Cloud Workloads: A Comprehensive Evaluation

Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Domain Generalization

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