On Improving The Performance And Resource Utilization of Consolidated Virtual Machines: Measurement, Modeling, Analysis, and Prediction

This dissertation addresses the performance related issues of consolidated \emph{Virtual Machines} (VMs). \emph{Virtualization} is an important technology for the \emph{Cloud} and data centers. Essential features of a data center like the fault tolerance, high-availability, and \emph{pay-as-you-go} model of services are implemented with the help of VMs. Cloud had become one of the significant innovations over the past decade. Research has been going on the deployment of newer and diverse set of applications like the \emph{High-Performance Computing} (HPC), and parallel applications on the Cloud. The primary method to increase the server resource utilization is VM consolidation, running as many VMs as possible on a server is the key to improving the resource utilization. On the other hand, consolidating too many VMs on a server can degrade the performance of all VMs. Therefore, it is necessary to measure, analyze and find ways to predict the performance variation of consolidated VMs. This dissertation investigates the causes of performance variation of consolidated VMs; the relationship between the resource contention and consolidation performance, and ways to predict the performance variation. Experiments have been conducted with real virtualized servers without using any simulation. All the results presented here are real system data. In this dissertation, a methodology is introduced to do the experiments with a large number of tasks and VMs; it is called the \emph{Incremental Consolidation Benchmarking Method} (ICBM). The experiments have been done with different types of resource-intensive tasks, parallel workflow, and VMs. Furthermore, to experiment with a large number of VMs and collect the data; a scheduling framework is also designed and implemented. Experimental results are presented to demonstrate the efficiency of the ICBM and framework

Compare the out-of-sample performance of mean-variance optimization relative to equally weighted or naîve 1/N portfolio

Masteroppgave i finansiering og investering - Nord universitet 202

Counterfactual shock in energy commodities affects stock market dynamics : Evidence from the United States

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Graphics Performance Benchmarks: Summary Report

Report on study of seven different benchmark software systems viewing the parameters and configurations of graphics systems

How COVID‐19 pandemic may hamper sustainable economic development

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COVID-19 pandemic and economic policy uncertainty regimes affect commodity market volatility

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An Analysis of Understanding of Traffic Signs among Drivers and Pedestrians in Dhaka, Bangladesh

This research demonstrates the percentage of drivers and pedestrians understanding the traffic sign in Dhaka, Bangladesh. The survey was conducted among 634 drivers inside and outside of Dhaka city. Moreover, 508 pedestrians participated in the survey within Dhaka city. In comparison, there were 863 male respondents and 279 female respondents among 1142 respondents. The survey took the form of multiple-choice questions that included the picture attached to each sign. However, the survey questionnaires included a few questions regarding driver gender, age, educational qualification, and driving experience. Similarly, the survey questionnaires for pedestrians had also been discussed questions regarding pedestrian gender, age, educational qualification, and job status. The overall traffic sign understanding of drivers was 68.68%. Moreover, the comprehensive traffic signs understanding of pedestrians was 64.5%. The findings showed that the drivers had a medium degree of understanding of the traffic sign’s meaning. However, the study results showed that efforts are needed to educate the drivers and pedestrians about the proper interpretation and reaction to traffic signals

Trade volume affects bitcoin energy consumption and carbon footprint

The environmental sustainability of bitcoin is making waves in the empirical literature, yet, no study has thus far examined the financial determinants of bitcoin energy consumption and carbon footprint. Here, we use novel estimation methods comprising dynamic ARDL simulations and general-to-specific VAR to examine steady-state effects, cumulative impulse-response, and counterfactual shocks of bitcoin trade volume on bitcoin energy bitcoin carbon footprint to ensure genuine causal inferences. We observed an increase in bitcoin trade volume spur both carbon and energy footprint by 24% in the long-run, whereas a dynamic shock in trade volume escalates bitcoin energy and carbon footprint by 46.54%

A Review on Effects of Pseudo Cereals Flour on Quality Properties of Biscuit, Cookies and Cake

Gluten free products are currently highly demandable by those with different gluten intolerances. Pseudo cereals are a category of non-grass seeds used to manufacture various gluten free products, including bread, biscuits, cakes, and cookies. Pseudo cereal seeds contain high-quality proteins, rich quantities and unique characteristics of starch, vast amounts of micronutrients such as minerals, vitamins along with diverse bioactive compounds. This chapter is focused on other resentful research work on the characteristics of pseudo cereals seeds and pseudo cereals flour. It also reveals different effects of pseudo cereal flour on physical-chemicals properties of biscuit, cake, and cookie. We think that this study will have a significant influence on product developers and customers on the use of pseudo cereal seeds and pseudo cereals flour