Research Article
A New Collaborative Scheduling Mechanism Based on Grading Mapping for Resource Balance in Distributed Object Cloud Storage System
@INPROCEEDINGS{10.1007/978-3-030-67540-0_35, author={Yu Lu and Ningjiang Chen and Wenjuan Pu and Ruifeng Wang}, title={A New Collaborative Scheduling Mechanism Based on Grading Mapping for Resource Balance in Distributed Object Cloud Storage System}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2021}, month={1}, keywords={Cloud storage Storage balance Grading mapping Ceph Collaborative scheduling}, doi={10.1007/978-3-030-67540-0_35} }- Yu Lu
Ningjiang Chen
Wenjuan Pu
Ruifeng Wang
Year: 2021
A New Collaborative Scheduling Mechanism Based on Grading Mapping for Resource Balance in Distributed Object Cloud Storage System
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-67540-0_35
Abstract
An algorithmic mapping of storage locations brings high storage efficiency to the storage system, but the loss of efficient scheduling makes systems prone to crashing at low usage. This paper uses the Ceph storage system as a research sample to analyze these issues and proposes a grading mapping adaptive storage resource collaborative optimization mechanism. This approach grading both the storage device and the storage content, and introduced random factors and influence factors as two-factors to quantify the grading mapping relationship between the two of them. This relation coordinates the storage systems’ performance and reliability. In addition, a collaborative storage algorithm is proposed to realize balanced storage efficiency and control data migration. The experimental results show that in comparison with the inherent mechanism in the traditional Ceph system, the proposed cooperative storage adaptation mechanism for data balancing has increased the average system usage by 17% and reduces data migration by 50% compared to the traditional research approach.
