不确定情况下的边缘重复数据删除:稳健的优化方法

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Ruikun Luo;Qiang He;Mengxi Xu;Feifei Chen;Song Wu;Jing Yang;Yuan Gao;Hai Jin
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引用次数: 0

摘要

分布式系统中出现的移动边缘计算(MEC)引发了人们对边缘数据管理的更多关注。有限的边缘资源和不断扩大的数据存储需求之间的矛盾使降低数据存储成本成为一个关键目标。尽管对作为数据削减技术的边缘重复数据删除进行了广泛研究,但现有的重复数据删除方法在 MEC 环境中遇到了许多挑战。这些挑战源于边缘服务器和云数据中心边缘服务器之间的差异,以及用户移动性等不确定性,导致重复数据删除决策的鲁棒性不足。因此,本文针对边缘重复数据删除问题提出了一种基于稳健优化的方法。通过考虑数据需求数量和边缘服务器故障等不确定因素,我们提出了两种不同的求解算法:uEDDE-C(一种基于列和约束生成的两阶段算法)和 uEDDE-A(一种近似算法,用于解决 uEDDE-C 的高计算开销问题)。我们的方法有助于在不稳定的边缘网络环境中实现高效的重复数据删除,并在各种不确定场景中保持稳健性。我们通过理论分析和实验评估验证了 uEDDE-C 和 uEDDE-A 的性能和鲁棒性。大量实验结果表明,我们的方法显著降低了数据存储成本和数据检索延迟,同时确保了真实世界 MEC 环境中的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge Data Deduplication Under Uncertainties: A Robust Optimization Approach
The emergence of mobile edge computing (MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data requests for data storage, making the reduction of data storage costs a critical objective. Despite the extensive studies of edge data deduplication as a data reduction technique, existing deduplication methods encounter numerous challenges in MEC environments. These challenges stem from disparities between edge servers and cloud data center edge servers, as well as uncertainties such as user mobility, leading to insufficient robustness in deduplication decision-making. Consequently, this paper presents a robust optimization-based approach for the edge data deduplication problem. By accounting for uncertainties including the number of data requirements and edge server failures, we propose two distinct solving algorithms: uEDDE-C, a two-stage algorithm based on column-and-constraint generation, and uEDDE-A, an approximation algorithm to address the high computation overhead of uEDDE-C. Our method facilitates efficient data deduplication in volatile edge network environments and maintains robustness across various uncertain scenarios. We validate the performance and robustness of uEDDE-C and uEDDE-A through theoretical analysis and experimental evaluations. The extensive experimental results demonstrate that our approach significantly reduces data storage cost and data retrieval latency while ensuring reliability in real-world MEC environments.
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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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