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Towards Automated Configuration of Cloud Storage Gateways: A Data Driven Approach

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11513)

Abstract

Cloud storage gateways (CSGs) are an essential part of enterprises to take advantage of the scale and flexibility of cloud object store. A CSG provides clients the impression of a locally configured large size block-based storage device, which needs to be mapped to remote cloud storage which is invariably object based. Proper configuration of the cloud storage gateway is extremely challenging because of numerous parameters involved and interactions among them. In this paper, we study this problem for a commercial CSG product that is typical of offerings in the market. We explore how machine learning techniques can be exploited both for the forward problem (i.e. predicting performance from the configuration parameters) and backward problem (i.e. predicting configuration parameter values from the target performance). Based on extensive testing with real world customer workloads, we show that it is possible to achieve excellent prediction accuracy while ensuring that the model is not overfitted to the data.

Keywords

Cloud storage gateway Object store Performance Configuration management Machine learning 

Notes

Acknowledgements

This research was supported by NSF grant IIP-330295. Discussions with Dr. S. Vucetic of Temple University were highly valuable in devising the extended validation techniques presented in the paper.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Temple UniversityPhiladelphiaUSA

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