Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

• Published in: IEICE Transactions on Information and Systems Vol. E102.D; no. 5; pp. 898 - 909
• Main Authors: MARTINEZ-JULIA, Pedro, MUKTADIR, Abu Hena Al, MIYAZAWA, Takaya, HARAI, Hiroaki, KAFLE, Ved P.
• Format: Journal Article
• Language: English
• Published: Tokyo The Institute of Electronics, Information and Communication Engineers 01.05.2019; Japan Science and Technology Agency
• Subjects: Algorithms; Classification; Energy consumption; Memory management; multi-target classification; multiple QoS; Resource allocation; Training; Virtual networks; virtual resource allocation scheme
• ISSN: 0916-8532; 1745-1361; 1745-1361
• DOI: 10.1587/transinf.2018NTP0016

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.