Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684

Multi-Dimensional Customization Modelling Based On Metagraph For Saas Multi-Tenant Applications

Software as a Service (SaaS) is a new software delivery model in which pre-built applications are delivered to customers as a service. SaaS providers aim to attract a large number of tenants (users) with minimal system modifications to meet economics of scale. To achieve this aim, SaaS applications have to be customizable to meet requirements of each tenant. However, due to the rapid growing of the SaaS, SaaS applications could have thousands of tenants with a huge number of ways to customize applications. Modularizing such customizations still is a highly complex task. Additionally, due to the big variation of requirements for tenants, no single customization model is appropriate for all tenants. In this paper, we propose a multi-dimensional customization model based on metagraph. The proposed mode addresses the modelling variability among tenants, describes customizations and their relationships, and guarantees the correctness of SaaS customizations made by tenants.Comment: 10 pages, 8 figure

Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection

One of the main objectives of cloud computing providers is increasing the revenue of their cloud datacenters by accommodating virtual network requests as many as possible. However, arrival and departure of virtual network requests fragment physical network's resources and reduce the possibility of accepting more virtual network requests. To increase the number of virtual network requests accommodated by fragmented physical networks, we propose two virtual network embedding algorithms, which coarsen virtual networks using Heavy Edge Matching (HEM) technique and embed coarsened virtual networks on best-fit sub-substrate networks. The performance of the proposed algorithms are evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithms increase the acceptance ratio and the revenue.Comment: arXiv admin note: substantial text overlap with arXiv:1502.0235

Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance

This paper proposes a novel multi-objective optimisation approach to solving both the problem of finding good structural and parametric choices in an ANN and the problem of training a classifier with a heavily skewed data set. The state-of-the-art CMA-PAES-HAGA multi-objective evolutionary algorithm [41] is used to simultaneously optimise the structure, weights, and biases of a population of ANNs with respect to not only the overall classification accuracy, but the classification accuracies of each individual target class. The effectiveness of this approach is then demonstrated on a real-world multi-class problem in medical diagnosis (classification of fetal cardiotocograms) where more than 75% of the data belongs to the majority class and the rest to two other minority classes. The optimised ANN is shown to significantly outperform a standard feed-forward ANN with respect to minority class recognition at the cost of slightly worse performance in terms of overall classification accuracy

A Multi objective Approach to Evolving Artificial Neural Networks for Coronary Heart Disease Classification

The optimisation of the accuracy of classifiers in pattern recognition is a complex problem that is often poorly understood. Whilst numerous techniques exist for the optimisa- tion of weights in artificial neural networks (e.g. the Widrow-Hoff least mean squares algorithm and back propagation techniques), there do not exist any hard and fast rules for choosing the structure of an artificial neural network - in particular for choosing both the number of the hidden layers used in the network and the size (in terms of number of neurons) of those hidden layers. However, this internal structure is one of the key factors in determining the accuracy of the classification. This paper proposes taking a multi-objective approach to the evolutionary design of artificial neural networks using a powerful optimiser based around the state-of-the-art MOEA/D- DRA algorithm and a novel method of incorporating decision maker preferences. In contrast to previous approaches, the novel approach outlined in this paper allows the intuitive consideration of trade-offs between classification objectives that are frequently present in complex classification problems but are often ignored. The effectiveness of the proposed multi-objective approach to evolving artificial neural networks is then shown on a real-world medical classification problem frequently used to benchmark classification method