基于ConvLSTM的移动边缘计算服务器能耗模型

doi:10.3969/j.issn.0255-8297.2024.01.005

Abstract

Abstract: To address the issue of low sensitivity and accuracy of existing energy consumption models in accommodating dynamic workload fluctuations, this paper proposes an intelligence server energy consumption model (IECM) based on the convolutional long short-term memory (ConvLSTM) neural network in mobile edge computing, which is used to predict and optimize energy consumption in servers. By collecting server runtime parameters and using the entropy method to filter and retain parameters significantly affecting server energy consumption, a deep network for training the server energy consumption model is constructed based on the selected parameters using the ConvLSTM neural network. Compared with existing energy consumption models, IECM exhibits superior adaptability to dynamic changes in server workload in CPU-intensive, I/O-intensive, memoryintensive, and mixed tasks, offering enhanced accuracy in energy consumption prediction.

Key words: convolutional long short-term memory (ConvLSTM), energy consumption prediction, intelligence power model, power modeling

CLC Number:

TP311

LI Xiaolong, LI Xi, YANG Lingfeng, HUANG Hua. Server Energy Consumption Model Based on ConvLSTM in Mobile Edge Computing[J]. Journal of Applied Sciences, 2024, 42(1): 53-66.

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Server Energy Consumption Model Based on ConvLSTM in Mobile Edge Computing

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