The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Virtualisasi fungsi rangkaian (NFV) membolehkan pengendali rangkaian menyediakan pelbagai fungsi maya secara fleksibel untuk perkhidmatan seperti Internet of things (IoT) dan aplikasi mudah alih. Untuk memenuhi pelbagai keperluan kualiti perkhidmatan (QoS) terhadap persekitaran rangkaian yang berubah-ubah masa, penyedia infrastruktur mesti melaraskan jumlah sumber pengiraan secara dinamik, seperti CPU, yang diperuntukkan kepada fungsi rangkaian maya (VNF). Untuk menyediakan kawalan sumber yang tangkas dan penyesuaian, meramalkan beban pelayan maya melalui teknologi pembelajaran mesin ialah pendekatan yang berkesan untuk kawalan proaktif sistem rangkaian. Dalam kertas ini, kami mencadangkan mekanisme pelarasan untuk regressor berdasarkan lupaan dan ensembel dinamik yang dilaksanakan dalam masa yang lebih singkat daripada kerja kami sebelum ini. Rangka kerja termasuk kaedah data latihan mengurangkan berdasarkan regresi model jarang. Dengan membuat senarai pendek data latihan yang diperoleh daripada model regresi jarang, masa pembelajaran semula boleh dikurangkan kepada kira-kira 57% tanpa merendahkan ketepatan peruntukan.
Takahiro HIRAYAMA
National Institute of Information and Communications Technology (NICT)
Takaya MIYAZAWA
National Institute of Information and Communications Technology (NICT)
Masahiro JIBIKI
National Institute of Information and Communications Technology (NICT)
Ved P. KAFLE
National Institute of Information and Communications Technology (NICT)
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Salinan
Takahiro HIRAYAMA, Takaya MIYAZAWA, Masahiro JIBIKI, Ved P. KAFLE, "Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 5, pp. 606-616, May 2021, doi: 10.1587/transinf.2020NTP0010.
Abstract: Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020NTP0010/_p
Salinan
@ARTICLE{e104-d_5_606,
author={Takahiro HIRAYAMA, Takaya MIYAZAWA, Masahiro JIBIKI, Ved P. KAFLE, },
journal={IEICE TRANSACTIONS on Information},
title={Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction},
year={2021},
volume={E104-D},
number={5},
pages={606-616},
abstract={Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.},
keywords={},
doi={10.1587/transinf.2020NTP0010},
ISSN={1745-1361},
month={May},}
Salinan
TY - JOUR
TI - Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 606
EP - 616
AU - Takahiro HIRAYAMA
AU - Takaya MIYAZAWA
AU - Masahiro JIBIKI
AU - Ved P. KAFLE
PY - 2021
DO - 10.1587/transinf.2020NTP0010
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2021
AB - Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.
ER -