Hammer: Towards Efficient Hot-Cold Data Identification via Online Learning

• URL: http://arxiv.org/abs/2411.14759v1
• Date: Fri, 22 Nov 2024 06:46:16 GMT
• Title: Hammer: Towards Efficient Hot-Cold Data Identification via Online Learning
• Authors: Kai Lu, Siqi Zhao, Jiguang Wan,
• Abstract summary: 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. We propose a novel solution based on online learning strategies, achieving higher accuracy and lower operational costs.
• Score: 12.60117987781744
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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