边缘云中基于层冗余的DNN模型库规划

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hongmin Geng;Yuepeng Li;Sheng Wang;Lin Gu;Deze Zeng
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引用次数: 0

摘要

人工智能应用的蓬勃发展极大地推动了边缘智能技术的发展。为了支持基于延迟敏感的深度神经网络(DNN)应用,将无服务器推理范式集成到边缘智能中已成为一种广泛认可的解决方案。然而,从中心云到边缘服务器的DNN模型下载时间长,影响了推理性能,并要求在边缘云中建立模型存储库。本文首先识别了DNN模型中固有的层冗余,这可能有利于提高边缘云模型库的存储效率。然而,如何利用层冗余特性并在具有容量存储资源的不同边缘服务器上分配DNN层以减少模型下载时间仍然是一个挑战。为了解决这个问题,我们首先用二次整数规划(Quadratic Integer Programming, QIP)的形式来表述这个问题,并在此基础上提出了一种随机舍入层冗余感知的DNN模型存储规划策略。与最先进的方法相比,我们的方法显著减少了高达63%的模型下载时间,正如通过广泛的跟踪驱动实验所证明的那样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Layer Redundancy Aware DNN Model Repository Planning for Fast Model Download in Edge Cloud
The booming development of artificial intelligence (AI) applications has greatly promoted edge intelligence technology. To support latency-sensitive Deep Neural Network (DNN) based applications, the integration of serverless inference paradigm into edge intelligence has become a widely recognized solution. However, the long DNN model downloading time from central clouds to edge servers hinders inference performance, and asks for establishing model repository within the edge cloud. This paper first identifies the inherent layer redundancy in DNN models, which is potentially beneficial to improve the storage efficiency of the model repository in the edge cloud. However, how to exploit the layer redundancy feature and allocate the DNN layers across different edge servers with capacitated storage resources to reduce the model downloading time remains challenging. To address this issue, we first formulate this problem in Quadratic Integer Programming (QIP) form, based on which a randomized rounding layer redundancy aware DNN model storage planning strategy is proposed. Our approach significantly reduces model downloading time by up to 63% compared to state-of-the-art methods, as demonstrated through extensive trace-driven experiments.
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来源期刊
IEEE Transactions on Cloud Computing
IEEE Transactions on Cloud Computing Computer Science-Software
CiteScore
9.40
自引率
6.20%
发文量
167
期刊介绍: The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.
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