在多接入边缘计算中提供经济高效且稳健的服务

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-07-30 DOI:10.1109/TPDS.2024.3435929
Zhengzhe Xiang;Yuhang Zheng;Dongjing Wang;Javid Taheri;Zengwei Zheng;Minyi Guo
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引用次数: 0

摘要

随着多访问边缘计算(MEC)技术的发展,越来越多的研究人员和开发人员正在边缘设备上部署计算密集型和 IO 密集型服务(尤其是人工智能服务)。这些设备靠近终端用户,能在移动环境中提供更好的性能。通过在网络边缘构建服务供应系统,与边缘服务器的短距离通信可显著降低延迟。然而,由于基于 MEC 的服务供应系统对资源敏感,而且网络可能不稳定,因此必须采取谨慎的资源分配和流量调度策略。本文通过应用资源分配和流量调度策略,研究并量化了基于 MEC 的服务供应系统的成本效益和稳健性。在此分析基础上,提出了一种成本效益高且稳健的服务供应算法(称为 CERA),以最大限度地降低部署成本,同时保持系统的稳健性。我们进行了广泛的实验,将所提出的方法与著名的基线算法进行比较,并对影响结果的因素进行评估。实验结果表明,在各种实例中,CERA 比其他基线算法至少提高了 15.9% 的性能。
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Cost-Effective and Robust Service Provisioning in Multi-Access Edge Computing
With the development of multiaccess edge computing (MEC) technology, an increasing number of researchers and developers are deploying their computation-intensive and IO-intensive services (especially AI services) on edge devices. These devices, being close to end users, provide better performance in mobile environments. By constructing a service provisioning system at the network edge, latency is significantly reduced due to short-distance communication with edge servers. However, since the MEC-based service provisioning system is resource-sensitive and the network may be unstable, careful resource allocation and traffic scheduling strategies are essential. This paper investigates and quantifies the cost-effectiveness and robustness of the MEC-based service provisioning system with the applied resource allocation and traffic scheduling strategies. Based on this analysis, a c ost- e ffective and r obust service provisioning a lgorithm, termed CERA , is proposed to minimize deployment costs while maintaining system robustness. Extensive experiments are conducted to compare the proposed approach with well-known baseline algorithms and evaluate factors impacting the results. The findings demonstrate that CERA achieves at least 15.9% better performance than other baseline algorithms across various instances.
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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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