Paper Info
Title
Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme
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 of MTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.
Year
DOI
Venue
2019
10.1587/transinf.2018NTP0016
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
multi-target classification, virtual resource allocation scheme, multiple QoS
Computer vision,Computer science,Resource allocation,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
E102D
5
1745-1361
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Abu Hena Al Muktadir1195.58
Takaya Miyazawa23913.31
Pedro Martinez-Julia310920.06
Hiroaki Harai425151.96
Ved P. Kafle520935.01