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.

Multi-dimensional Classification via Selective Feature Augmentation

“…For each figure, each row corresponds to the binary classification model of one class label. The first third of the columns correspond to the discrete version of kNN-augmented features in (10), the middle third of the columns to the continuous version of kNN-augmented features in (11), and the last third of the columns to the maximum margin-augmented features in (12). It is shown that, for each third of all columns, the diagonal element usually takes the largest value in its corresponding row.…”

Multi-dimensional classification via stacked dependency exploitation

“…Firstly, q 2 first-level classifiers are trained (steps 1-6). Then, training sets which will be used to train the second-level classifiers are constructed (steps [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. After that, the second-level classifier for each class space is induced one by one (steps [23][24][25].…”

“…Here, each class variable corresponds to one specific class space which characterizes the object's semantics from one dimension. Multi-dimensional classification (MDC) techniques have been widely utilized in real-world applications involving objects with rich semantics [4][5][6][7][8][9][10][11][12].…”

Multi-dimensional classification: paradigm, algorithms and beyond

“…), and from the scenario dimension (with possible classes wedding, memorial, saloon, etc.). Such kinds of applications widely exist in computer vision [3][4][5][6][7][8][9], text mining [10][11][12][13][14], bioinformatics [15][16][17][18][19][20][21], ecology [22,23], and beyond [24][25][26][27][28][29][30][31][32][33].…”