Efficient management of storage resources in big data and cloud computing environments requires accurate identification of data's "cold" and "hot" states. Traditional methods, such as rule-based algorithms and early AI techniques, often struggle with dynamic workloads, leading to low accuracy, poor adaptability, and high operational overhead. To address these issues, we propose a novel solution based on online learning strategies. Our approach dynamically adapts to changing data access patterns, achieving higher accuracy and lower operational costs. Rigorous testing with both synthetic and real-world datasets demonstrates a significant improvement, achieving a 90% accuracy rate in hot-cold classification. Additionally, the computational and storage overheads are considerably reduced.
@article{arxiv.2411.14759,
title = {Hammer: Towards Efficient Hot-Cold Data Identification via Online Learning},
author = {Kai Lu and Siqi Zhao and Jiguang Wan},
journal= {arXiv preprint arXiv:2411.14759},
year = {2024}
}