EdgeHydra: Fault-Tolerant Edge Data Distribution Based on Erasure Coding

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

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Publication Details

Journal
IEEE Transactions on Parallel and Distributed Systems
Published
2024-11-07
DOI
https://doi.org/10.1109/tpds.2024.3493034
Citations
2
Primary Topic
Big Data and Digital Economy
Type
article
Field-Weighted Citation Impact
1.56
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EdgeHydra: Fault-Tolerant Edge Data Distribution Based on Erasure Coding

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2 citations
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Big Data and Digital Economy
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article

EdgeHydra: Fault-Tolerant Edge Data Distribution Based on Erasure Coding

Hai Jin, Qiang He, Yun Yang, Xiaohai Dai, Guobiao Zhang, Yuchong Hu, Jiawei Wang, Feifei Chen, Ruikun Luo
article en
2 citations

Abstract

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

IEEE Transactions on Parallel and Distributed SystemsVol. 36(1)
Deakin University (AU), Huazhong University of Science and Technology (CN), Swinburne University of Technology (AU)
Openalex Percentile: Top 12%
Big Data and Digital Economy
1.56
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