Cloud Workload Prediction and Generation Models

Cloud computing allows for elasticity as users can dynamically benefit from new virtual resources when their workload increases. Such a feature requires highly reactive resource provisioning mechanisms. In this paper, we propose two new workload prediction models, based on constraint programming and...

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Bibliographic Details
Published in2017 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) pp. 89 - 96
Main Authors Wamba, Gilles Madi, Yunbo Li, Orgerie, Anne-Cecile, Beldiceanu, Nicolas, Menaud, Jean-Marc
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2017
Subjects
Cloud computing
cloud workload
constraint programming
generation
machine learning
neural network
Neural networks
prediction
Predictive models
Programming
time series
Time series analysis
Virtual machining
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DOI10.1109/SBAC-PAD.2017.19

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Summary:Cloud computing allows for elasticity as users can dynamically benefit from new virtual resources when their workload increases. Such a feature requires highly reactive resource provisioning mechanisms. In this paper, we propose two new workload prediction models, based on constraint programming and neural networks, that can be used for dynamic resource provisioning in Cloud environments. We also present two workload trace generators that can help to extend an experimental dataset in order to test more widely resource optimization heuristics. Our models are validated using real traces from a small Cloud provider. Both approaches are shown to be complimentary as neural networks give better prediction results, while constraint programming is more suitable for trace generation.
DOI:10.1109/SBAC-PAD.2017.19