Julius Dichter

Cloud computing is reserving its position in the market as the next disruptive utility paradigm. It is found on the pay-as-you-use model. Cloud computing is changing the way information technology (IT) operates for individuals as well as for companies. Cloud computing comes with different offerings to accommodate diverse applications. It comes with many successful adoption stories and a few unfortunate ones that are related to security breaches. Security concerns are what is making many companies reluctant to fully embrace the cloud realm. To enhance trust and entice adoption between cloud clients (CC) and cloud service providers (CSP), a new paradigm of depending on involving a third-party auditor (TPA) has been introduced. Hence, implementing a solution with a TPA comes with its toll in terms of trust and processing overhead. A lightweight security protocol to give the CC extra control with tools to audit the TPA and the CSP is paramount to the solution. In this paper, we are introducing a novel protocol: the lightweight accountable privacy-preserving (LAPP) protocol. Our proposed protocol is lightweight in terms of processing and communication costs. It is based on a newly introduced mathematical model along with two algorithms. We have conducted simulation experiments to measure the impact of our method. We have compared LAPP to the most eminent privacy-preserving methods in the cloud research field, using the open source cloud computing simulator GreenCloud. Our simulation results showed superiority in performance for LAPP in regard to time complexity, accuracy, and computation time on auditing. The aim of the time complexity and computation time on auditing simulations is to measure the lightweight aspect of our proposed protocol as well as to improve the quality of service.

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.