Performance Analysis of Multiple Virtualized Servers

Server virtualization is considered as one of the most significant changes in IT operations in the past decade, making it possible to manage groups of servers with a greater degree of reliability at a lower cost. It is driven by the goal of reducing the total number of physical servers in an organization by consolidating multiple applications on shared servers. In this paper we construct several x86_64 servers based on VMware vSphere, and then analyze their performances using open source analyzing tools Pylot and Curl-loader. The results show that despite the enormous potential benefits of virtualization techniques, the efficiency decreased by increasing the number of virtual machines. So, a trade-off is needed between number of virtual machines and expected efficiency of servers

Dynamical Analysis of Yeast Cell Cycle Using a Stochastic Markov Model

Introduction: The cell cycle network is responsible of control, growth and proliferation of cells. The relationship between the cell cycle network and cancer emergence, and the complex reciprocal interactions between genes/proteins calls for computational models to analyze this regulatory network. Ample experimental data confirm the existence of random behaviors in the interactions between genes and proteins in gene regulatory networks. Genetic factors, regulatory dynamics at the microscopic level, transcription rates of genes, and many other factors that depend on variable environmental conditions cause random behaviors in the cell cycle network. Method: The aim of this study was to present a stochastic Markov model to simulate interactions between proteins in a complex network of fission yeast cell cycle and to predict the dynamics of proteins. We used local sensitivity analysis to investigate the relationship between the weight of protein / gene interactions with the probabilities of phase transition in the cell cycle. Results: Using this model, the probability of transition between different phases of the cell cycle in the presence of different levels of noise was investigated and it was proved that the cell cycle path has the highest probability among all possible pathways for the cell. By performing sensitivity analysis, the correlation between the weight of interactions between proteins and the probability of transition between different phases of the cell cycle was calculated. Conclusion: Our local sensitivity analysis revealed that how perturbation on parameters affect the transition probabilities between subsequent cell cycle phases, so it suggests testable hypotheses in the experimental environments. Also, the model of this study proves the stability of the cell cycle in the presence of moderate levels of noise