An Adaptive Erasure-Coded Data Access Method for Cross-Cloud Collaborative Scheduling of Storage and Computation

Zhang Kaixin; Wang Yijie; Bao Han; Kan Junhui

doi:10.7544/issn1000-1239.202330541

Journal of Computer Research and Development > 2024 > 61(3): 571-588. > DOI: 10.7544/issn1000-1239.202330541 CSTR: 32373.14.issn1000-1239.202330541

Zhang Kaixin, Wang Yijie, Bao Han, Kan Junhui. An Adaptive Erasure-Coded Data Access Method for Cross-Cloud Collaborative Scheduling of Storage and Computation[J]. Journal of Computer Research and Development, 2024, 61(3): 571-588. DOI: 10.7544/issn1000-1239.202330541

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An Adaptive Erasure-Coded Data Access Method for Cross-Cloud Collaborative Scheduling of Storage and Computation

1.
National Key Laboratory of Parallel and Distributed Computing (National University of Defense Technology), Changsha 410073
2.
College of Computer Science and Technology, National University of Defense Technology, Changsha 410073

Funds: This work was supported by the National Key Research and Development Program of China (2022ZD0115302), the National Natural Science Foundation of China (61379052), the Science Foundation of Ministry of Education of China (2018A02002), and the Natural Science Foundation for Distinguished Young Scholars of Hunan Province (14JJ1026).

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Author Bio:
Zhang Kaixin: born in 2002. Master candidate. His main research interests include cloud storage and erasure coding

Wang Yijie: born in 1971. PhD. professor, PhD supervisor. Distinguished member of CCF. Her main research interests include distributed storage, big data analysis, and cloud computing

Bao Han: born in 1992. PhD. His main research interests include cloud storage and erasure coding

Kan Junhui: born in 2000. Master candidate. His main research interests include cloud storage and collaborative scheduling of storage and computation
Received Date: June 20, 2023
Revised Date: December 03, 2023
Available Online: December 19, 2023

Graphical Abstract

Abstract

Abstract

Nowadays, the increasing demand for cross-cloud collaborative scheduling of storage and computation puts high demands on cross-cloud data access speed. Therefore, cross-cloud data access methods based on data redundancy techniques (erasure coding and multiple-duplicate) with high cross-cloud data access speed are gaining attention. Among them, the cross-cloud data access method based on erasure coding has become a hot research topic because of its low storage overhead and high fault tolerance. In order to improve the data access speed by shortening the transmission time of coded blocks, existing cross-cloud data access methods based on erasure coding introduce caching techniques and optimize the coded data access scheme. However, due to the coarse granularity of cache management and the lack of coordinated optimization of cache management and coded data access scheme, the existing methods suffer from low cache hits, low cache hit efficiency, and high access volume of coded blocks with low transmission speed, which prolong the coded block transmission time. To this end, we first propose an IPFS-based cross-cloud storage system framework (IBCS) that can realize fine-grained cache management based on IPFS data slice management mechanism, and thus can improve cache hits. Then, we propose an adaptive erasure-coded data access method for cross-cloud collaborative scheduling of storage and computation (AECAM) that evaluates the transmission speed of each coded block during data access based on the distribution of coded blocks (including cached coded blocks) and data access nodes, and accordingly formulates a coded data access scheme that can avoid accessing low transmission speed coded blocks. In addition, AECAM identifies coded blocks that are easily selected in the coded data access scheme and have low transmission speed, and caches them near the data access nodes, thus improving both cache hits and hit efficiency. We build a cross-cloud storage system for collaborative scheduling of storage and computation (C2S2) based on IBCS and AECAM. Compared with existing erasure-coded storage systems that introduce caching, experiments in a cross-cloud environment show that C2S2 can improve data access speed by 75.22%−81.29%.
- cross-cloud collaborative scheduling of storage and computation,
- erasure coding,
- data access technology,
- IPFS,
- cache

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References (25)

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