CONFIGURATION FILE RECOMMENDATIONS USING METADATA AND FUZZY TECHNIQUE

Abstract

In the real world installation of the software’s or upload software’s to the cloud requires a lot of understanding of the configuration files through which software’s understands how to run , where to run and what to run. Basically we call configuration files are the heart of the software. Discovery of configuration files is one of the prerequisite activities for a successful workload migration to the cloud. The complicated and super-sized file systems, the considerable variance of configuration files, and the multiple presences of configuration items make configuration file discovery very difficult. The Traditional approaches usually highly rely on experts to compose software specific scripts or rules to discover configuration files, which is very expensive and labor-intensive. Our proposed approach is a novel learning based approach named MetaConf to convert configuration file discovery to a supervised file classification task using the file metadata as learning features such that it can be conducted automatically, efficiently, and independently of domain expertise. I report our evaluation with extensive and realworld case studies, and the experimental results validate that our approach is effective and it outperforms our baseline method.