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Large-scale computing systems workload prediction using parallel improved LSTM neural network

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成果类型:
期刊论文
作者:
Tang, Xiaoyong*
通讯作者:
Tang, Xiaoyong
作者机构:
[Tang, Xiaoyong] Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
[Tang, Xiaoyong] Hunan Agr Univ, Southern Reg Collaborat Innovat Ctr Grain & Oil C, Changsha 410128, Hunan, Peoples R China.
通讯机构:
[Tang, Xiaoyong] H
Hunan Agr Univ, Coll Informat Sci & Technol, Changsha 410128, Hunan, Peoples R China.
Hunan Agr Univ, Southern Reg Collaborat Innovat Ctr Grain & Oil C, Changsha 410128, Hunan, Peoples R China.
语种:
英文
关键词:
computing systems;LSTM;neural network;parallel;Workload prediction
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2019
卷:
7
页码:
40525-40533
基金类别:
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0204004, in part by the Hunan Provincial Key Research and Development Program under Grant 2018GK2055, in part by the Double First-Class Construction Project of Hunan Agricultural University under Grant SYL201802029.
机构署名:
本校为第一且通讯机构
院系归属:
信息科学技术学院
摘要:
In recent years, large-scale computing systems have been widely used as an important part of the computing infrastructure. Resource management based on systems workload prediction is an effective way to improve application efficiency. However, accuracy and real-time functionalities are always the key challenges that perplex the systems workload prediction model. In this paper, we first investigate the dependence on historical workload in large-scale computing systems and build a day and time two-dimensional time-series workload model. We then d...

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