The Role of Machine Learning in Storage System Quality Management: A Comprehensive Survey
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I3P105Keywords:
Machine Learning, Storage System, Quality Management, Data Integrity, Predictive Maintenance, Cloud Computing, Federated LearningAbstract
The fast growth of cloud computing, big data analytics, and distributed applications has made the modern storage systems remarkably more complex, and quality management has become a serious issue. The Quality Management of Storage Systems (SQM) is expected to guarantee efficient, reliable, secure, and scalable data storage services in accordance with the Quality of Service (QoS) and Service Level Agreement (SLA) conditions. Although useful, traditional quality management methods tend to be ineffective in managing the dynamic, heterogeneous, and large size storage infrastructures of the modern world. The paper provides an overview of the literature on utilizing machine learning (ML) to improve the quality management of storage system. It talks about the main ML paradigms such as supervised, unsupervised, and reinforcement learning and examines how all the three can be applied to monitoring storage, performance optimization, fault detection, and automated decision-making. In addition to that, the paper discusses ML-enabled quality improvement activities as testing, monitoring, debugging, and data evaluation, and security and data integrity features to deal with intrusion detection, data corruption, ransomware attacks, and adaptive access control. The survey identifies the benefits, issues, and research directions of the ML-based SQM and the potential of the system to facilitate intelligent, proactive and autonomous quality control in the next-generation storage systems.
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