Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers

Cluster Computing - Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs),...     

Self-adaptive architecture for virtual machines consolidation based on probabilistic model evaluation of data centers in Cloud computing

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.     

Iterative algorithms for processing experimental data

The need to solve linear and nonlinear integral equations arise, e.g., in recovering plasma parameters from the data of multichannel diagnostics. The paper presents an iterative method for solving integral equations with a singularity at the upper limit of integration. The method consists in constructing successive approximations and calculating the integral by quadrature formulas in each integration interval. An example of application of the iterative algorithm to numerically solve an integral equation similar to those arising in recovering the plasma density profile from reflectometry data is presented.     

Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm

Cluster Computing - Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services...     

RHAS: robust hybrid auto-scaling for web applications in cloud computing

Cluster Computing - The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives...     

Managing overloaded hosts for energy-efficiency in cloud data centers

Cluster Computing - Traditional data centers are shifted toward the cloud computing paradigm. These data centers support the increasing demand for computational and data storage that consumes a...     

Biclustering algorithms for biological data analysis: a survey

A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results from the application of standard clustering methods to genes are limited. This limitation is imposed by the existence of a number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the data matrix has been proposed. The goal is to find submatrices, that is, subgroups of genes and subgroups of conditions, where the genes exhibit highly correlated activities for every condition. In this paper, we refer to this class of algorithms as biclustering. Biclustering is also referred in the literature as coclustering and direct clustering, among others names, and has also been used in fields such as information retrieval and data mining. In this comprehensive survey, we analyze a large number of existing approaches to biclustering, and classify them in accordance with the type of biclusters they can find, the patterns of biclusters that are discovered, the methods used to perform the search, the approaches used to evaluate the solution, and the target applications.     

Classification and feature selection algorithms for multi-class CGH data

Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses and gains. In this article, we develop novel SVM-based methods for classification and feature selection of CGH data. For classification, we developed a novel similarity kernel that is shown to be more effective than the standard linear kernel used in SVM. For feature selection, we propose a novel method based on the new kernel that iteratively selects features that provides the maximum benefit for classification. We compared our methods against the best wrapper-based and filter-based approaches that have been used for feature selection of large dimensional biological data. Our results on datasets generated from the Progenetix database, suggests that our methods are considerably superior to existing methods. AVAILABILITY: All software developed in this article can be downloaded from http://plaza.ufl.edu/junliu/feature.tar.gz.     

Segmentation algorithms for ear image data towards biomechanical studies

In recent years, the segmentation, i.e. the identification, of ear structures in video-otoscopy, computerised tomography (CT) and magnetic resonance (MR) image data, has gained significant importance in the medical imaging area, particularly those in CT and MR imaging. Segmentation is the fundamental step of any automated technique for supporting the medical diagnosis and, in particular, in biomechanics studies, for building realistic geometric models of ear structures. In this paper, a review of the algorithms used in ear segmentation is presented. The review includes an introduction to the usually biomechanical modelling approaches and also to the common imaging modalities. Afterwards, several segmentation algorithms for ear image data are described, and their specificities and difficulties as well as their advantages and disadvantages are identified and analysed using experimental examples. Finally, the conclusions are presented as well as a discussion about possible trends for future research concerning the ear segmentation.     

Infrastructures and services for remote sensing data production management across multiple satellite data centers

With the number of satellite sensors and date centers being increased continuously, it is becoming a trend to manage and process massive remote sensing data from multiple distributed sources. However, the combination of multiple satellite data centers for massive remote sensing (RS) data collaborative processing still faces many challenges. In order to reduce the huge amounts of data migration and improve the efficiency of multi-datacenter collaborative process, this paper presents the infrastructures and services of the data management as well as workflow management for massive remote sensing data production. A dynamic data scheduling strategy was employed to reduce the duplication of data request and data processing. And by combining the remote sensing spatial metadata repositories and Gfarm grid file system, the unified management of the raw data, intermediate products and final products were achieved in the co-processing. In addition, multi-level task order repositories and workflow templates were used to construct the production workflow automatically. With the help of specific heuristic scheduling rules, the production tasks were executed quickly. Ultimately, the Multi-datacenter Collaborative Process System (MDCPS) were implemented for large-scale remote sensing data production based on the effective management of data and workflow. As a consequence, the performance of MDCPS in experiments environment showed that those strategies could significantly enhance the efficiency of co-processing across multiple data centers.     

Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data

MOTIVATION: Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order to locate the peaks and quantify their intensities, several pre-processing steps are required. Though different approaches to perform pre-processing have been proposed, there is no systematic study that compares their performance. RESULTS: In this article, we present the results of a systematic comparison of various popular packages for pre-processing of SELDI-TOF data. We evaluate their performance in terms of two of their primary functions: peak detection and peak quantification. Regarding peak quantification, the performance of the algorithms is measured in terms of reproducibility. For peak detection, the comparison is based on sensitivity and false discovery rate. Our results show that for spectra generated with low laser intensity, the software developed by Ciphergen Biosystems (ProteinChip Software 3.1 with the additional tool Biomarker Wizard) produces relatively good results for both peak quantification and detection. On the other hand, for the data produced with either medium or high laser intensity, none of the methods show uniformly better performances under both criteria. Our analysis suggests that an advantageous combination is the use of the packages MassSpecWavelet and PROcess, the former for peak detection and the latter for peak quantification.     

Bayesian automatic relevance determination algorithms for classifying gene expression data

MOTIVATION: We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays. RESULTS: We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important.     

Monte carlo simulation-based algorithms for analysis of shotgun proteomic data

Two new statistical models based on Monte Carlo Simulation (MCS) have been developed to score peptide matches in shotgun proteomic data and incorporated in a database search program, MassMatrix (www.massmatrix.net). The first model evaluates peptide matches based on the total abundance of matched peaks in the experimental spectra. The second model evaluates amino acid residue tags within MS/MS spectra. The two models provide complementary scores for peptide matches that result in higher confidence in peptide identification when significant scores are returned from both models. The MCS-based models use a variance reduction technique that improves estimation precision. Due to the high computational expense of MCS-based models, peptide matches were prefiltered by other statistical models before further evaluation by the MCS-based models. Receiver operating characteristic analysis of the data sets confirmed that MCS-based models improved the overall performance of the MassMatrix search software, especially for low-mass accuracy data sets.     

Genetic algorithms applied to multi-class clustering for gene expression data

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance