Comparative Analysis of Cloud Simulators and Authentication Techniques in Cloud Computing

Cloud computing is the concern of computer hardware and software resources above the internet so that anyone who is connected to the internet can access it as a service or provision in a seamless way. As we are moving more and more towards the application of this newly emerging technology, it is essential to study, evaluate and analyze the performance, security and other related problems that might be encountered in cloud computing. Since, it is not a practicable way to directly examine the behavior of cloud on such problems using the real hardware and software resources due to its high costs, modeling and simulation has become an essential tool to withstand with these issues. In this paper, we retrospect, analyse and compare features of the existing cloud computing simulators and various location based authentication and simulation tools

H-Function and a problem related to a String

The aim of this paper is to obtain the solution of a problem related to a String with the help of H–function of one variable

Limiting Laws of Linear Eigenvalue Statistics for Unitary Invariant Matrix Models

We study the variance and the Laplace transform of the probability law of linear eigenvalue statistics of unitary invariant Matrix Models of n-dimentional Hermitian matrices as n tends to infinity. Assuming that the test function of statistics is smooth enough and using the asymptotic formulas by Deift et al for orthogonal polynomials with varying weights, we show first that if the support of the Density of States of the model consists of two or more intervals, then in the global regime the variance of statistics is a quasiperiodic function of n generically in the potential, determining the model. We show next that the exponent of the Laplace transform of the probability law is not in general 1/2variance, as it should be if the Central Limit Theorem would be valid, and we find the asymptotic form of the Laplace transform of the probability law in certain cases

Level density and level-spacing distributions of random, self-adjoint, non-Hermitian matrices

We investigate the level-density $\sigma(x)$ and level-spacing distribution $p(s)$ of random matrices $M=AF\neq M^{\dagger}$ where $F$ is a (diagonal) inner-product and $A$ is a random, real symmetric or complex Hermitian matrix with independent entries drawn from a probability distribution $q(x)$ with zero mean and finite higher moments. Although not Hermitian, the matrix $M$ is self-adjoint with respect to $F$ and thus has purely real eigenvalues. We find that the level density $\sigma_F(x)$ is independent of the underlying distribution $q(x)$, is solely characterized by $F$, and therefore generalizes Wigner's semicircle distribution $\sigma_W(x)$. We find that the level-spacing distributions $p(s)$ are independent of $q(x)$, are dependent upon the inner-product $F$ and whether $A$ is real or complex, and therefore generalize the Wigner's surmise for level spacing. Our results suggest $F$-dependent generalizations of the well-known Gaussian Orthogonal Ensemble (GOE) and Gaussian Unitary Ensemble (GUE) classes.Comment: 5 pages, 5 figures, revised tex