Multi-Target Classification Based Automatic Virtual Resource Allocation Scheme

• DOI: 10.1587/transinf.2018ntp0016
• Publication Date: 2019-04-30T18:22:51Z
• Authors: Abu Hena Al MUKTADIR; Takaya MIYAZAWA; Pedro MARTINEZ-JULIA; Hiroaki HARAI; Ved P. KAFLE
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

In this paper, we propose a method for automatic virtual resource allocation by using multi-target classification-based scheme (MTCAS). our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used the InP optimally allocate set of NEs Virtual Operator (VNO). Such will be subject some constraints, such avoidance over-allocation satisfaction multiple Quality Service (QoS) metrics. order achieve comparable or higher prediction accuracy less training time than available ensemble-based classification (MTC) algorithms, majority-voting based ensemble algorithm (MVEN) MTCAS. We numerically evaluate performance MVEN MTC algorithms with synthetic datasets. The results indicate that requires 70% but achieves same related algorithms. also demonstrate increasing amount data increases efficacy ofMTCAS, thus reducing CPU memory about 33% 51%, respectively.