MEC设置的可公开验证分布式计算

IF 6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Qiang Wang;Zhicheng Li;Fucai Zhou;Jian Xu;Changsheng Zhang
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引用次数: 0

摘要

随着物联网(IoT)的快速扩展,从云计算到移动边缘计算(MEC)的转变已经成为解决实时应用的低延迟要求的必要条件。可验证计算(VC)使资源有限的客户能够将其计算密集型任务外包给强大的云,同时保证计算结果的正确性。然而,最初为云计算设计的传统VC方案在应用于MEC环境时面临挑战,例如可扩展性问题、健壮性和效率问题。为此,我们提出了一种可验证的MEC分布式计算方案,其中计算任务分布在云服务器集群(由$n$服务器组成)和边缘服务器之间。云通过并行子任务处理大部分计算,而边缘服务器验证中间结果并执行最少的计算以恢复最终结果。我们的方案保证,如果云服务器集群中总共$n$台服务器中至少有$t$台服务器诚实地执行计算,则可以恢复结果。通过利用批验证和矩阵优化多项式评估,我们的方案显著提高了可伸缩性、容错性和效率。大量的分析和仿真表明,我们提出的方案比现有的解决方案更可行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Publicly Verifiable Distributed Computation for MEC Setting
With the rapid expansion of the Internet of Things (IoT), the shift from cloud computing to Mobile Edge Computing (MEC) has become necessary to address the low-latency requirements of real-time applications. Verifiable computation (VC) enables resource-limited clients to outsource their computation-intensive tasks to a powerful cloud while ensuring the correctness of the computation result. However, traditional VC schemes, originally designed for cloud computing, face challenges when applied to MEC environments, such as scalability issues, robustness, and efficiency concerns. To this end, we propose a verifiable distributed computation scheme for MEC, where computation tasks are distributed between a cloud server cluster (consisting of $n$ servers) and an edge server. The cloud handles most of the computation through parallel sub-tasks, while the edge server verifies intermediate results and performs minimal computation to recover the final outcome. Our scheme guarantees that the result can be recovered if at least $t$ servers, out of a total of $n$ servers in the cloud server cluster, perform their computations honestly. By leveraging batch verification and matrix-optimized polynomial evaluations, our scheme significantly enhances scalability, fault tolerance, and efficiency. The extensive analysis and simulations demonstrate that our proposed scheme is more feasible than existing solutions.
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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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