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NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers; improving I/O performance; and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.
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This content will become publicly available on March 5, 2026
Towards Agile and Judicious Metadata Load Balancing for Ceph File System via Matrix-based Modeling
To scale out the massive metadata access, the Ceph distributed file system (CephFS) adopts adynamic subtree partitioningmethod, splitting the hierarchical namespace and distributingsubtreesacross multiple metadata servers. However, this method suffers from a severe imbalance problem that may result in poor performance due to its inaccurate imbalance prediction, ignorance of workload characteristics, and unnecessary/invalid migration activities. To eliminate these inefficiencies, we propose Lunule, a novel CephFS metadata load balancer, which employs animbalance factor modelfor accurately determiningwhento trigger re-balance and tolerate unharmful imbalanced situations. Lunule further adopts aworkload-aware migration plannerto appropriately select subtree migration candidates. Finally, we extend Lunule to Lunule+, which models metadata accesses into matrices, and employs matrix-based formulas for more accurate load prediction and re-balance decision. Compared to baselines, Lunule achieves better load balance, increases the metadata throughput by up to 315.8%, and shortens the tail job completion time by up to 64.6% for five real-world workloads and their mixture, respectively. Besides, Lunule is capable of handling the metadata cluster expansion and the workload growth, and scales linearly on a 16-node cluster. Compared to Lunule, Lunule+achieves up to 64.96% better metadata load balance, and 13.53-86.09% higher throughput.
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- Award ID(s):
- 2305491
- PAR ID:
- 10577864
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Transactions on Storage
- ISSN:
- 1553-3077
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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