DNN Application Oriented Migration Scheduling Strategy based on Genetic Algorithm

Migration Scheduling Strategy for Deep Neural Network (DNN) is a considerably popular method to migrate some complex neural network layers to the edge nodes having the rich resources for computation. In spite of this, this migration process causes additional time overhead and energy overhead and thu...

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Bibliographic Details
Published in:Chinese Control Conference pp. 4296 - 4301
Main Authors: Yang, Hong, Guo, Xiong, Li, Mengliang
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 01.07.2020
Subjects:
Cloud computing
Energy consumption
Mobile handsets
Neural networks
Processor scheduling
Task analysis
Time factors
ISSN:1934-1768
Online Access:Get full text
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Summary:Migration Scheduling Strategy for Deep Neural Network (DNN) is a considerably popular method to migrate some complex neural network layers to the edge nodes having the rich resources for computation. In spite of this, this migration process causes additional time overhead and energy overhead and thus impacts the quality of experience for users. To this end, with the consideration of the DNN applications, this paper proposes a Genetic Algorithm (GA) based migration strategy to minimize response time and energy consumption for the multiple task scheduling condition. To be specific, the migration scheduling problem for DNN applications is formulated firstly. Then, the concrete strategy based on GA is devised. In addition, the simulation is implemented over the designated edge environment, and the experimental results demonstrate that the proposed strategy outperforms two baselines in terms of response time and energy consumption.
ISSN:1934-1768
DOI:10.23919/CCC50068.2020.9188543