边缘计算中关键任务应用的资源管理:近期研究和开放问题的系统综述

IF 28 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Nayereh Rasouli, Cristian Klein, Erik Elmroth
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引用次数: 0

摘要

在边缘计算领域,延迟、能量、带宽和本地计算的优化至关重要,特别是对于灾难管理和医疗保健等领域的关键任务应用程序。此类应用,例如部署无人机和自主机器人,需要即时数据处理。考虑到边缘服务器的固有约束(以有限的容量为特征),细致的资源管理变得至关重要。这需要明智的资源分配、精明的供应、战略性任务卸载和明智的应用程序放置,所有这些对于固定和移动资源服务交付都至关重要。本调查深入研究了在边缘环境中部署关键任务应用程序的细微差别,剖析了它们的技术先决条件。我们的研究采用了基于传统综述方法的系统文献综述。我们分析了边缘环境中这些关键应用程序的基础服务质量指标,旨在实现高效的服务交付。此外,我们确定了当前资源管理战略中的一些主要差距。我们的首要目标是为为关键任务应用量身定制的强大边缘计算范例铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Resource Management for Mission-critical Applications in Edge Computing: Systematic Review on Recent Research and Open Issues
In the realm of edge computing, the optimization of latency, energy, bandwidth, and local computation is critical, especially for mission-critical applications in sectors like disaster management and healthcare. Such applications, exemplified by deploying UAVs and autonomous robots, demand instantaneous data processing. Given the inherent constraints of edge servers—characterized by their limited capacity—meticulous resource management becomes paramount. This entails judicious resource allocation, astute provisioning, strategic task offloading, and judicious application placement, all pivotal for both fixed and mobile resource service delivery. This survey delves deep into the nuances of deploying mission-critical applications in an edge environment, dissecting their technological prerequisites. Our exploration employs a systematic literature review grounded in a conventional review methodology. We analyze the cornerstone quality of service metrics pivotal for such critical applications in edge contexts, aiming for efficient service delivery. Moreover, we identified some major gaps in current resource management strategies. Our overarching ambition is to pave the way for robust edge computing paradigms tailored for mission-critical applications.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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