数据与推理结构的最优业务迁移

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE
Vahram Stepanyan, Stefan Schuet, Kalmanje Krishnakumar
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引用次数: 0

摘要

在本文中,我们考虑了支持数据和推理结构(DRF)的空域操作的迁移问题,假设具有分配的计算、存储和电源资源的固定云/边缘基础设施,其中云/边缘服务器和通信站位于有线连接网络中,而车辆使用无线网络进行通信。目标是为请求的服务执行自动选择最佳位置,在满足用户服务质量(QoS)和可用资源约束的同时实现最低成本。为此,为每个可能的计算位置定义响应时间、消耗的能量和总成本的估计。然后制定并求解了一个混合整数线性规划,以确定给定QoS约束和网络基础设施限制下最坏情况下车辆定位的最优计算位置。将该方法应用于轨迹重新规划用例,以实时避免与应急车辆的碰撞。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Service Migration for Data and Reasoning Fabric
In this paper we consider migration problem for Data and Reasoning Fabric (DRF)-enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage, and power resources, where cloud/edge servers and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, estimates of the response time, consumed energy, and total cost are defined for each potential compute location. A mixed-integer linear program is then formulated and solved to identify optimal compute locations given QoS constraints and network infrastructure limitations, with worst-case vehicle positioning. The approach is applied to trajectory replanning use case to avoid a collision with an emergency vehicle in real time.
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来源期刊
CiteScore
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
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