移动边缘计算和网络工程中的网络资源分配和任务卸载机制

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhixu Shu, Kewang Zhang
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引用次数: 0

摘要

目前,移动边缘计算中的大多数资源分配方法都是根据任务请求计算和卸载的时间顺序来分配计算资源,没有考虑实际应用中任务的优先级。根据这种情况下的计算需求,提出了一种面向任务优先级的资源分配方法。根据任务执行的平均处理时间,给出相应的任务优先级。通过对不同优先级的任务进行加权来分配计算资源,既能保证高优先级任务获得充足的计算资源,又能减少完成所有任务计算的总时间和能耗,从而提高服务质量。实验结果表明,所提出的方法可以实现更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A mechanism for network resource allocation and task offloading in mobile edge computing and network engineering

At present, most of the resource allocation methods in mobile edge computing allocate computing resources according to the time order in which task requests are calculated and unloaded, without considering the priority of tasks in practical applications. According to the computing requirements in such cases, a priority task-oriented resource allocation method is proposed. According to the average processing time of the task execution, the corresponding priority for task is given. The tasks with different priorities are weighted to allocate computing resources, which not only ensures that the high-priority tasks obtain sufficient computing resources, but also reduces the total time and energy consumption to complete the calculation of all tasks, thus improving the quality of service. The experimental results show that the proposed method can achieve better performance.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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