CAMS: A Cost-Aware Migration Scheme for Cloud Object Storage Systems

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Man Luo, Ke Zhou, Hua Wang, Zhibin Jiang, Han Kong, Yongguang Ji

Published: 2024, Last Modified: 15 Jan 2026NAS 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Cloud object storage systems provide massive data storage capabilities where data is stored in different storage clusters. Storing data according to access characteristics efficiently in different clusters is a challenging task. Methods considering past data access frequency bring the problem of low storage utilization and load imbalance. We propose a Cost-Aware Migration Scheme for cloud object storage systems(CAMS) based on object hotness and life cycle to improve the utilization of cloud object storage systems and reduce the Total Cost of Ownership(TCO). CAMS establishes an accurate object hotness standard, it uses object hotness and lifecycle prediction to guide data migration. CAMS was tested using real-world datasets from production cloud object storage system, the results show that CAMS strategies outperform Cold, CoinFlip and RejectX strategies with gains of up to 19.79% on the estimated TCO.