Performance Prediction for Burstable Cloud Resources

Daniel J Dubois; Giuliano Casale

10th EAI International Conference on Performance Evaluation Methodologies and Tools

Research Article

Performance Prediction for Burstable Cloud Resources

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.25-10-2016.2266436,
    author={Daniel J Dubois and Giuliano Casale},
    title={Performance Prediction for Burstable Cloud Resources},
    proceedings={10th EAI International Conference on Performance Evaluation Methodologies and Tools},
    publisher={ACM},
    proceedings_a={VALUETOOLS},
    year={2017},
    month={5},
    keywords={burstable clouds performance prediction cloud simulator},
    doi={10.4108/eai.25-10-2016.2266436}
}

Daniel J Dubois
Giuliano Casale
Year: 2017
Performance Prediction for Burstable Cloud Resources
VALUETOOLS
ACM
DOI: 10.4108/eai.25-10-2016.2266436

Daniel J Dubois¹^,*, Giuliano Casale¹

1: Imperial College London

*Contact email: daniel.dubois@imperial.ac.uk

Abstract

We propose ForeBurst, an open source tool for performance prediction for complex cloud-based applications. ForeBurst leverages queueing network models for predicting performance metrics such as resource utilizations, request response times, and credit usage in burstable resources, such as the Amazon EC2 T-family instances.

Keywords: burstable clouds performance prediction cloud simulator

Published: 2017-05-03
Publisher: ACM

: http://dx.doi.org/10.4108/eai.25-10-2016.2266436

Performance Prediction for Burstable Cloud Resources

Abstract

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