Cost-aware routing for computation offloading in knowledge-defined AIoT

IF 6.2 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Future Generation Computer Systems-The International Journal of Escience Pub Date : 2025-07-19 DOI:10.1016/j.future.2025.108013

Peichen Li , Xingwei Wang , Bo Yi , Tingting Yuan , Jiahao Chen , Jiaxin Zhang , Min Huang

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引用次数: 0

Abstract

Edge computing plays a crucial role in supporting high-bandwidth and latency-sensitive applications in the Artificial Intelligence of Things (AIoT). These applications often demand both computing and network resources within strict time constraints, yet existing approaches often fall short in jointly considering dynamic destination-path combinations, pricing incentives, and differentiated computation costs. In this paper, we propose a Knowledge-Defined AIoT-based framework that incorporates a cost-aware routing algorithm called CompuRoute for computation offloading. This framework enables collaborative data collection and centralized data aggregation and analysis, supporting efficient cost estimation. Based on the estimated cost, CompuRoute integrates a reverse auction mechanism for selecting candidate edge servers. Next, CompuRoute considers networking states and introduces a multipath routing algorithm based on network flow theory to determine the destination edge servers and routing paths. Experimental results demonstrate that CompuRoute can improve the task success rate and reduce task completion time compared to baseline algorithms, exhibiting scalability across various network topologies.

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知识定义AIoT中计算卸载的成本感知路由

边缘计算在支持物联网人工智能（AIoT）中的高带宽和延迟敏感应用方面发挥着至关重要的作用。这些应用程序通常在严格的时间限制内同时需要计算和网络资源，然而现有的方法在联合考虑动态目标路径组合、定价激励和差异化计算成本方面往往存在不足。在本文中，我们提出了一种基于知识定义的ai框架，该框架结合了一种称为CompuRoute的成本感知路由算法，用于计算卸载。该框架支持协作数据收集和集中数据聚合和分析，支持有效的成本估算。基于估计的成本，CompuRoute集成了一个反向拍卖机制来选择候选边缘服务器。其次，CompuRoute考虑网络状态，引入基于网络流理论的多路径路由算法，确定目标边缘服务器和路由路径。实验结果表明，与基准算法相比，CompuRoute可以提高任务成功率，减少任务完成时间，并表现出跨各种网络拓扑的可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Future Generation Computer Systems-The International Journal of Escience 工程技术-计算机：理论方法

CiteScore

19.90

自引率

2.70%

发文量

376

审稿时长

10.6 months

期刊介绍： Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.