Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended Abstract-Reference-Cited by-同舟云学术

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Storage System Trace Characterization, Compression, and Synthesis using Machine Learning – An Extended Abstract

Published:2023-06-21 Issue: Volume: Page:
ISSN:
Container-title:ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
language:
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Author:

Poudel Pratik¹^ORCID

Affiliation:

1. Florida International University, USA

Publisher

ACM

Reference6 articles.

1. Storage workload identification;Basak J.;ACM Transactions on Storage (TOS),2016

2. A principled approach for selecting block I/O traces

3. Workload characterization and synthesis for cloud using generative stochastic processes

4. Tarasov Vasily. 2012. Extracting flexible replayable models from large block traces. FAST. Tarasov Vasily. 2012. Extracting flexible replayable models from large block traces. FAST.

5. Akshat Verma , Ricardo Koller , Luis Useche , and Raju Rangaswami . 2010 . Energy proportional storage using dynamic consolidation . Proceedings of the File and Storage Systems (2010). Akshat Verma, Ricardo Koller, Luis Useche, and Raju Rangaswami. 2010. Energy proportional storage using dynamic consolidation. Proceedings of the File and Storage Systems (2010).

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