Cost-efficient virtual machine management in data centers

Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.

Irritable bowel syndrome in the general population : epidemiology, comorbidity and societal costs

Background: Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) disorder characterised by abdominal pain and abnormal bowel function. It is associated with a high rate of healthcare consumption and significant health care costs. The prevalence and economic burden of IBS in Finland has not been studied before. The aims of this study were to assess the prevalence of IBS according to various diagnostic criteria and to study the rates of psychiatric and somatic comorbidity in IBS. In addition, health care consumption and societal costs of IBS were to be evaluated. Methods: The study was a two-phase postal survey. Questionnaire I identifying IBS by Manning 2 (at least two of the six Manning symptoms), Manning 3 (at least three Manning symptoms), Rome I, and Rome II criteria, was mailed to a random sample of 5 000 working age subjects. It also covered extra-GI symptoms such as headache, back pain, and depression. Questionnaire II, covering rates of physician visits, and use of GI medication, was sent to subjects fulfilling Manning 2 or Rome II IBS criteria in Questionnaire I. Results: The response rate was 73% and 86% for questionnaires I and II. The prevalence of IBS was 15.9%, 9.6%, 5.6%, and 5.1% according to Manning 2, Manning 3, Rome I, and Rome II criteria. Of those meeting Rome II criteria, 97% also met Manning 2 criteria. Presence of severe abdominal pain was more often reported by subjects meeting either of the Rome criteria than those meeting either of the Manning criteria. Presence of depression, anxiety, and several somatic symptoms was more common among subjects meeting any IBS criterion than by controls. Of subjects with depressive symptoms, 11.6% met Rome II IBS criteria compared to 3.7% of those with no depressiveness. Subjects meeting any IBS criteria made more physician visits than controls. Intensity of GI symptoms and presence of dyspeptic symptoms were the strongest predictors of GI consultations. Presence of dyspeptic symptoms and a history of abdominal pain in childhood also predicted non-GI visits. Annual GI related individual costs were higher in the Rome II group (497 ) than in the Manning 2 group (295 ). Direct expenses of GI symptoms and non GI physician visits ranged between 98M for Rome II and 230M for Manning 2 criteria. Conclusions: The prevalence of IBS varies substantially depending on the criteria applied. Rome II criteria are more restrictive than Manning 2, and they identify an IBS population with more severe GI symptoms, more frequent health care use, and higher individual health care costs. Subjects with IBS demonstrate high rates of psychiatric and somatic comorbidity regardless of health care seeking status. Perceived symptom severity rather than psychiatric comorbidity predicts health care seeking for GI symptoms. IBS incurs considerable medical costs. The direct GI and non-GI costs are equivalent to up to 5% of outpatient health care and medicine costs in Finland. A more integral approach to IBS by physicians, accounting also for comorbid conditions, may produce a more favourable course in IBS patients and reduce health care expenditures.

Do inventory management practices affect economic performance? An empirical evaluation of the machine tool SMEs in Bangalore

Inventory management (IM) has a decisive role in the enhancement of manufacturing industry's competitiveness. Therefore, major manufacturing industries are following IM practices with the intention of improving their performance. However, the effort to introduce IM in SMEs is very limited due to lack of initiation, expertise, and financial constraints. This paper aims to provide a guideline for entrepreneurs in enhancing their IM performance, as it presents the results of a survey based study carried out for machine tool Small and Medium Enterprises (SMEs) in Bangalore. Having established the significance of inventory as an input, we probed the relationship between IM performance and economic performance of these SMEs. To the extent possible all the factors of production and performance indicators were deliberately considered in pure economic terms. All economic performance indicators adopted seem to have a positive and significant association with IM performance in SMEs. On the whole, we found that SMEs which are IM efficient are likely to perform better on the economic front also and experience higher returns to scale.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Mixed pixel analysis for flood mapping using extended support vector machine

This paper addresses the challenges of flood mapping using multispectral images. Quantitative flood mapping is critical for flood damage assessment and management. Remote sensing images obtained from various satellite or airborne sensors provide valuable data for this application, from which the information on the extent of flood can be extracted. However the great challenge involved in the data interpretation is to achieve more reliable flood extent mapping including both the fully inundated areas and the 'wet' areas where trees and houses are partly covered by water. This is a typical combined pure pixel and mixed pixel problem. In this paper, an extended Support Vector Machines method for spectral unmixing developed recently has been applied to generate an integrated map showing both pure pixels (fully inundated areas) and mixed pixels (trees and houses partly covered by water). The outputs were compared with the conventional mean based linear spectral mixture model, and better performance was demonstrated with a subset of Landsat ETM+ data recorded at the Daly River Basin, NT, Australia, on 3rd March, 2008, after a flood event.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.

Direct torque control schemes for split-phase induction machine

In this paper, direct torque control (DTC) algorithms for a split-phase induction machine (SPIM) are established. An SPIM has two sets of three-phase stator windings, with a shift of thirty electrical degrees between them. The significant contributions of this paper are: 1) two new methods of DTC technique for an SPIM are developed, called Resultant Flux Control Method and Individual Flux Control Method and 2) advantages and disadvantages of both methods are discussed. High torque ripple is a disadvantage for three-phase DTC. It is found that torque ripple in an SPIM can be significantly reduced without increasing the switching frequency.

Support vector machine approach for the switching transient peak overvoltages during line energization

At the time of restoration transmission line switching is one of the major causes, which creates transient overvoltages. Though detailed Electro Magnetic Transient studies are carried out extensively for the planning and design of transmission systems, such studies are not common in a day-today operation of power systems. However it is important for the operator to ensure during restoration of supply that peak overvoltages resulting from the switching operations are well within safe limits. This paper presents a support vector machine approach to classify the various cases of line energization in the category of safe or unsafe based upon the peak value of overvoltage at the receiving end of line. Operator can define the threshold value of voltage to assign the data pattern in either of the class. For illustration of proposed approach the power system used for switching transient peak overvoltages tests is a 400 kV equivalent system of an Indian southern gri

Machine recognition of online handwritten Devanagari characters

In this paper, we describe a system for the automatic recognition of isolated handwritten Devanagari characters obtained by linearizing consonant conjuncts. Owing to the large number of characters and resulting demands on data acquisition, we use structural recognition techniques to reduce some characters to others. The residual characters are then classified using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system Ifs evaluated for the writer dependent scenario.

Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Minimizing total weighted tardiness on a batch-processing machine with non-agreeable release times and due dates

This study considers the scheduling problem observed in the burn-in operation of semiconductor final testing, where jobs are associated with release times, due dates, processing times, sizes, and non-agreeable release times and due dates. The burn-in oven is modeled as a batch-processing machine which can process a batch of several jobs as long as the total sizes of the jobs do not exceed the machine capacity and the processing time of a batch is equal to the longest time among all the jobs in the batch. Due to the importance of on-time delivery in semiconductor manufacturing, the objective measure of this problem is to minimize total weighted tardiness. We have formulated the scheduling problem into an integer linear programming model and empirically show its computational intractability. Due to the computational intractability, we propose a few simple greedy heuristic algorithms and meta-heuristic algorithm, simulated annealing (SA). A series of computational experiments are conducted to evaluate the performance of the proposed heuristic algorithms in comparison with exact solution on various small-size problem instances and in comparison with estimated optimal solution on various real-life large size problem instances. The computational results show that the SA algorithm, with initial solution obtained using our own proposed greedy heuristic algorithm, consistently finds a robust solution in a reasonable amount of computation time.

Adhesion,Presence and Antifouling of Deinococcus geothermalis in Paper Machine Environment

This thesis has two items: biofouling and antifouling in paper industry. Biofouling means unwanted microbial accumulation on surfaces causing e.g. disturbances in industrial processes, contamination of medical devices or of water distribution networks. Antifouling focuses on preventing accumulation of the biofilms in undesired places. Deinococcus geothermalis is a pink-pigmented, thermophilic bacterium, and extremely resistant towards radiation, UV-light and desiccation and known as a biofouler of paper machines forming firm and biocide resistant biofilms on the stainless steel surfaces. The compact structure of biofilm microcolonies of D. geothermalis E50051 and the adhesion into abiotic surfaces were investigated by confocal laser scanning microscope combined with carbohydrate specific fluorescently labelled lectins. The extracellular polymeric substance in D. geothermalis microcolonies was found to be a composite of at least five different glycoconjugates contributing to adhesion, functioning as structural elements, putative storages for water, gliding motility and likely also to protection. The adhesion threads that D. geothermalis seems to use to adhere on an abiotic surface and to anchor itself to the neighbouring cells were shown to be protein. Four protein components of type IV pilin were identified. In addition, the lectin staining showed that the adhesion threads were covered with galactose containing glycoconjugates. The threads were not exposed on planktic cells indicating their primary role in adhesion and in biofilm formation. I investigated by quantitative real-time PCR the presence of D. geothermalis in biofilms, deposits, process waters and paper end products from 24 paper and board mills. The primers designed for doing this were targeted to the 16S rRNA gene of D. geothermalis. We found D. geothermalis DNA from 9 machines, in total 16 samples of the 120 mill samples searched for. The total bacterial content varied in those samples between 107 to 3 ×1010 16S rRNA gene copies g-1. The proportion of D. geothermalis in those same samples was minor, 0.03 1.3 % of the total bacterial content. Nevertheless D. geothermalis may endanger paper quality as its DNA was shown in an end product. As an antifouling method towards biofilms we studied the electrochemical polarization. Two novel instruments were designed for this work. The double biofilm analyzer was designed for search for a polarization program that would eradicate D. geothermalis biofilm or from stainless steel under conditions simulating paper mill environment. The Radbox instrument was designed to study the generation of reactive oxygen species during the polarization that was effective in antifouling of D. geothermalis. We found that cathodic character and a pulsed mode of polarization were required to achieve detaching D. geothermalis biofilm from stainless steel. We also found that the efficiency of polarization was good on submerged, and poor on splash area biofilms. By adding oxidative biocides, bromochloro-5,5-dimethylhydantoin, 2,2-dibromo-2-cyanodiacetamide or peracetic acid gave additive value with polarization, being active on splash area biofilms. We showed that the cathodically weighted pulsed polarization that was active in removing D. geothermalis was also effective in generation of reactive oxygen species. It is possible that the antifouling effect relied on the generation of ROS on the polarized steel surfaces. Antifouling method successful towards D. geothermalis that is a tenacious biofouler and possesses a high tolerance to oxidative stressors could be functional also towards other biofoulers and applicable in wet industrial processes elsewhere.