Exploring Analytical Models for Performability Evaluation of Virtualized Servers using Dynamic Resource

Virtualization of resources is a widely accepted technique to optimize resources in recent technologies. Virtualization allows users to execute their services on the same physical machine, keeping these services isolated from each other. This paper proposes the analytical models for performability evaluation of virtualized servers with dynamic resource utilization. The performance and avalability models are considered separately due to the behaviour of the proposed system. The well-known Markov Reward Model (MRM) is used for the solution of the analytical model considered together with an exact spectral expansion and product form solution. The dynamic resource utilization is employed to enhance the QoS of the proposed model which is another major issue in the performance characterization of virtulazilation. In this paper, the performability output parameters, such as mean queue length, mean response time and blocking probability are computed and presented for the proposed model. In addition, the performability results obtained from the analytical models are validated by the simulation (DES) results to show the accuracy and effectiveness of the proposed work. The results indicate the proposed modelling results show good agreement with DES and understand the factors are very important to improve the QoS

Factors Influencing Repurchase Intention of Facemasks from Online Platforms

At the end of the year 2019, a viral and deadly pandemic hit the world, referred to as the COVID-19 crisis. Healthcare professionals recommend that wearing face masks diminishes the prevalence of COVID-19 at the population level, resulting in a slower pandemic spread. In the COVID 19 situation there is everyday requirement of face mask and other precautionary products in order to prevent the spread of virus in Bangkok, Thailand. The study examined the factors influencing the Repurchase intention of face mask from 401 valid respondents. Inferential analysis to analyse the strength of influence of the independent variables and intervening variable on Repurchase intention of facemask was also performed through multiple linear regression and simple linear regression. The findings showed that other than Perceived Ease of use, all other variables Perceived usefulness, Satisfaction, Perceived website Quality, Privacy, System Availability, Trust has a statistically significant influence on Repurchase intention.</em