Grouping-based consistency protocol design for end-edge-cloud hierarchical storage system

Simple item page
dc.contributor.authorGu, Shushi
dc.contributor.authorWang, Yizhen
dc.contributor.authorWang, Ye
dc.contributor.authorZhang, Qinyu
dc.contributor.authorQin, Xue
dc.creator.orcidhttps://orcid.org/0000-0003-2736-7649en_US
dc.creator.orcidhttps://orcid.org/0000-0003-2736-7649
dc.date.accessioned2022-10-05T18:26:34Z
dc.date.available2022-10-05T18:26:34Z
dc.date.issued2020-01-15
dc.description.abstractWith the increasing of the number of edge-devices and the demand of real-time experience, the end-edge-cloud hierarchical storage system (EECHSS) is emerged recently for reliable caching and fast offloading of massive amounts of data. EECHSS can accommodate various services, save computing power and improve storage capacity, due to transformation from central-cloud to edge-cloud, considerable reducing service delay and communication overhead. One of the main challenges brought by edge-cloud architecture is consistency problem. It is difficult to guarantee that the data from the two distributed clusters is consistent by existing consistency protocols. In addition, as the variety of applications increases, the existing fixed consistency level of classical protocols can no longer satisfy the system dynamic requirements. In this paper, we focus on designing grouping-based consistency protocols with adaptively selecting consistency level in EECHSS. At first, we analyze the internal structure and the workflow of EECHSS, and devise two modified adaptive grouping-based consistency protocols (GM-Paxos and GEPaxos) with efficient grouping algorithms. Then, for the characteristics that data is offloaded frequently, we design two synchronization strategies to ensure the consistency of the data cached in the edge-cloud and the central-cloud, respectively. Experiments show that, our proposed grouping-based consistency protocols of EECHSS can improve the availability as much as possible while ensuring data consistent.en_US
dc.description.abstractWith the increasing of the number of edge-devices and the demand of real-time experience, the end-edge-cloud hierarchical storage system (EECHSS) is emerged recently for reliable caching and fast offloading of massive amounts of data. EECHSS can accommodate various services, save computing power and improve storage capacity, due to transformation from central-cloud to edge-cloud, considerable reducing service delay and communication overhead. One of the main challenges brought by edge-cloud architecture is consistency problem. It is difficult to guarantee that the data from the two distributed clusters is consistent by existing consistency protocols. In addition, as the variety of applications increases, the existing fixed consistency level of classical protocols can no longer satisfy the system dynamic requirements. In this paper, we focus on designing grouping-based consistency protocols with adaptively selecting consistency level in EECHSS. At first, we analyze the internal structure and the workflow of EECHSS, and devise two modified adaptive grouping-based consistency protocols (GM-Paxos and GEPaxos) with efficient grouping algorithms. Then, for the characteristics that data is offloaded frequently, we design two synchronization strategies to ensure the consistency of the data cached in the edge-cloud and the central-cloud, respectively. Experiments show that, our proposed grouping-based consistency protocols of EECHSS can improve the availability as much as possible while ensuring data consistent.
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61701136, Grant 61831008, and Grant 61525103, in part by the Guangdong Science and Technology Planning Project under Grant 2018B030322004, in part by The Verification Platform of Multi-Tier Coverage Communication Network for Oceans under Grant PCL2018KP002, and in part by the Shenzhen Basic Research Program under Grant JCYJ20170811154233370 and Grant ZDSYS201707280903305.en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grant 61701136, Grant 61831008, and Grant 61525103, in part by the Guangdong Science and Technology Planning Project under Grant 2018B030322004, in part by The Verification Platform of Multi-Tier Coverage Communication Network for Oceans under Grant PCL2018KP002, and in part by the Shenzhen Basic Research Program under Grant JCYJ20170811154233370 and Grant ZDSYS201707280903305.
dc.identifier.citationS. Gu, Y. Wang, Y. Wang, Q. Zhang and X. Qin, "Grouping-Based Consistency Protocol Design for End-Edge-Cloud Hierarchical Storage System," in IEEE Access, vol. 8, pp. 8959-8973, 2020, doi: 10.1109/ACCESS.2020.2964626.en_US
dc.identifier.citationS. Gu, Y. Wang, Y. Wang, Q. Zhang and X. Qin, "Grouping-Based Consistency Protocol Design for End-Edge-Cloud Hierarchical Storage System," in IEEE Access, vol. 8, pp. 8959-8973, 2020, doi: 10.1109/ACCESS.2020.2964626.
dc.identifier.doihttps://doi.org/10.1109/ACCESS.2020.2964626
dc.identifier.urihttps://hdl.handle.net/1969.6/94064
dc.language.isoen_USen_US
dc.language.isoen_US
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectend-edge-cloud hierarchical storage systemen_US
dc.subjectadaptive consistencyen_US
dc.subjectgrouping algorithmen_US
dc.subjectsynchronization strategyen_US
dc.subjectend-edge-cloud hierarchical storage system
dc.subjectadaptive consistency
dc.subjectgrouping algorithm
dc.subjectsynchronization strategy
dc.titleGrouping-based consistency protocol design for end-edge-cloud hierarchical storage systemen_US
dc.titleGrouping-based consistency protocol design for end-edge-cloud hierarchical storage system
dc.typeArticleen_US
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Grouping-Based Consistency Protocol Design for End-Edge-Cloud Hierarchical Storage System.pdf
Size:
1.71 MB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

COE Faculty Works