IEICE TRANSACTIONS on Information
Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme
Summary :
In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.5 pp.898-909
- Publication Date
- 2019/05/01
- Publicized
- 2019/02/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018NTP0016
- Type of Manuscript
- Special Section PAPER (Special Section on the Architectures, Protocols, and Applications for the Future Internet)
- Category
Authors
Abu Hena Al MUKTADIR
National Institute of Information and Communications Technology (NICT)
Takaya MIYAZAWA
National Institute of Information and Communications Technology (NICT)
Pedro MARTINEZ-JULIA
National Institute of Information and Communications Technology (NICT)
Hiroaki HARAI
National Institute of Information and Communications Technology (NICT)
Ved P. KAFLE
National Institute of Information and Communications Technology (NICT)
Keyword
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Abu Hena Al MUKTADIR, Takaya MIYAZAWA, Pedro MARTINEZ-JULIA, Hiroaki HARAI, Ved P. KAFLE, "Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 5, pp. 898-909, May 2019, doi: 10.1587/transinf.2018NTP0016.
Abstract: In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018NTP0016/_p
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@ARTICLE{e102-d_5_898,
author={Abu Hena Al MUKTADIR, Takaya MIYAZAWA, Pedro MARTINEZ-JULIA, Hiroaki HARAI, Ved P. KAFLE, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme},
year={2019},
volume={E102-D},
number={5},
pages={898-909},
abstract={In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.},
keywords={},
doi={10.1587/transinf.2018NTP0016},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme
T2 - IEICE TRANSACTIONS on Information
SP - 898
EP - 909
AU - Abu Hena Al MUKTADIR
AU - Takaya MIYAZAWA
AU - Pedro MARTINEZ-JULIA
AU - Hiroaki HARAI
AU - Ved P. KAFLE
PY - 2019
DO - 10.1587/transinf.2018NTP0016
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2019
AB - In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.
ER -
English
