Policy Generation Framework for Large-Scale Storage Infrastructures
Cloud computing is gaining acceptance among mainstream technology users. Storage cloud providers often employ Storage Area Networks (SANs) to provide elasticity, rapid adaptability to changing demands, and policy based automation. As storage capacity grows, the storage environment becomes heterogeneous, increasingly complex, harder to manage, and more expensive to operate. This paper presents PGML (Policy Generation for large-scale storage infrastructure configuration using Machine Learning), an automated, supervised machine learning framework for generation of best practices for SAN configuration that can potentially reduce configuration errors by up to 70% in a data center.