EdgeHydra:基于消除编码的容错边缘数据分发

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-11-07 DOI:10.1109/TPDS.2024.3493034
Qiang He;Guobiao Zhang;Jiawei Wang;Ruikun Luo;Xiaohai Dai;Yuchong Hu;Feifei Chen;Hai Jin;Yun Yang
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引用次数: 0

摘要

在边缘计算环境中,应用程序供应商可以将流行数据从云端分发到边缘服务器,以提供低延迟数据检索。一个关键问题是如何经济高效地将这些数据从云端分发到边缘服务器。根据目前的方案,一个文件会被分成若干数据块,从云端并行传输到目标边缘服务器。然后,边缘服务器可以将接收到的数据块组合起来,重建文件。虽然这种方法加快了数据分发过程,但也存在潜在的缺点。它对网络波动和服务器故障等运行时异常情况造成的传输延迟和传输失败很敏感。本文介绍的 EdgeHydra 是首个边缘数据分发方案,它通过基于擦除编码的容错来应对这一挑战。在 EdgeHydra 中,文件被编码成数据块和奇偶校验块,从云并行传输到目标边缘服务器。边缘服务器在收到足够数量的数据块后就能重建文件,而无需等待传输中的所有数据块。它还创新性地采用了无领导块补充机制,以确保各个目标边缘服务器收到足够的块。这些都大大提高了数据分发过程的稳健性。大量实验表明,EdgeHydra 可以有效地容忍单个传输链路的延迟和故障,在分发时间上比最先进的方案最多可节省 50.54%。
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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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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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