2020
DOI: 10.1109/access.2020.2964626
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Grouping-Based Consistency Protocol Design for End-Edge-Cloud Hierarchical Storage System

Abstract: With 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 con… Show more

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Cited by 4 publications
(2 citation statements)
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“…Although PBFT has Byzantine Fault Tolerance, high efficiency, and low power consumption [18][19][20], it also has shortcomings such as malicious nodes that cannot be eliminated in time and C/S response mode; since the number of nodes in the network cannot be dynamically sensed, the efficiency of consistency will decline when the number of nodes increases. To solve these problems, the PBFT consistency protocol and master node scheduling are optimized based on RPoS, and a dynamic Byzantine consensus algorithm (DPBFT) is proposed.…”
Section: Dynamic Byzantine Consensus Algorithm Based On Pbft-rposmentioning
confidence: 99%
“…Although PBFT has Byzantine Fault Tolerance, high efficiency, and low power consumption [18][19][20], it also has shortcomings such as malicious nodes that cannot be eliminated in time and C/S response mode; since the number of nodes in the network cannot be dynamically sensed, the efficiency of consistency will decline when the number of nodes increases. To solve these problems, the PBFT consistency protocol and master node scheduling are optimized based on RPoS, and a dynamic Byzantine consensus algorithm (DPBFT) is proposed.…”
Section: Dynamic Byzantine Consensus Algorithm Based On Pbft-rposmentioning
confidence: 99%
“…The second large group of recent research activities (see Table 2) puts forward (i) new data models suitable for efficient distributed across multiple nodes [29-30]; (ii) new consistency models [40-46], (iii) consistency control algorithms and consensus quorum protocols [31][32][33][34][35][36][37][38][39], and (iv) techniques for data replications and strategies for replica placement [47][48][49][50][51][52][53][54][55].…”
Section: A Survey Of Recent Research Work Studying Distributed Data S...mentioning
confidence: 99%