为人工智能进程提供边缘计算中的自适应反向任务卸载

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Petros Amanatidis , Dimitris Karampatzakis , Georgios Michailidis , Thomas Lagkas , George Iosifidis
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引用次数: 0

摘要

如今,从智能相机到自动驾驶汽车等边缘物联网设备层出不穷,它们的计算能力也在不断提高,用于在用户附近的边缘实施基于人工智能的服务。由于这些服务通常对计算要求较高,因此将其任务卸载到附近云服务器的流行模式已获得广泛关注和研究。在这项工作中,我们提出了一种不同于上述典型边缘计算卸载理念的新模式。也就是说,我们认为可以利用这些终端节点来协助大型节点(如小云)执行人工智能任务。事实上,随着越来越多的终端节点被部署,它们会产生大量的闲置计算能力,如果以系统化的方式对这些能力进行聚合和利用,就会被证明是有益的。我们引入了反向卸载的概念,并研究了这样一种场景:一个功能强大的节点将人工智能任务拆分成一组子任务,并将它们分配给附近的一组边缘物联网节点。每个节点的目标都是在遵守预定能耗和人工智能性能限制的同时,最大限度地减少受最慢子任务限制的整体执行时间。这是一个具有挑战性的 MINLP(混合整数非线性问题)优化问题,我们通过新推出的 EAI-ARO(边缘人工智能自适应反向卸载)算法,以一种新颖的方法解决了这一问题。此外,我们还利用边缘计算测试平台和具有代表性的人工智能服务演示了反向卸载建议的功效。研究结果表明,与贪婪算法和基线任务卸载算法相比,我们的方法能显著优化系统性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Adaptive reverse task offloading in edge computing for AI processes

Adaptive reverse task offloading in edge computing for AI processes
Nowadays, we witness the proliferation of edge IoT devices, ranging from smart cameras to autonomous vehicles, with increasing computing capabilities, used to implement AI-based services in users’ proximity, right at the edge. As these services are often computationally demanding, the popular paradigm of offloading their tasks to nearby cloud servers has gained much traction and been studied extensively. In this work, we propose a new paradigm that departs from the above typical edge computing offloading idea. Namely, we argue that it is possible to leverage these end nodes to assist larger nodes (e.g., cloudlets) in executing AI tasks. Indeed, as more and more end nodes are deployed, they create an abundance of idle computing capacity, which, when aggregated and exploited in a systematic fashion, can be proved beneficial. We introduce the idea of reverse offloading and study a scenario where a powerful node splits an AI task into a group of subtasks and assigns them to a set of nearby edge IoT nodes. The goal of each node is to minimize the overall execution time, which is constrained by the slowest subtask, while adhering to predetermined energy consumption and AI performance constraints. This is a challenging MINLP (Mixed Integer Non-Linear Problem) optimization problem that we tackle with a novel approach through our newly introduced EAI-ARO (Edge AI-Adaptive Reverse Offloading) algorithm. Furthermore, a demonstration of the efficacy of our reverse offloading proposal using an edge computing testbed and a representative AI service is performed. The findings suggest that our method optimizes the system’s performance significantly when compared with a greedy and a baseline task offloading algorithm.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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