Intelligent Solution System for Cloud Security Based on Equity Distribution:Model and Algorithms

In the cloud environment,ensuring a high level of data security is in high demand.Data planning storage optimization is part of the whole security process in the cloud environment.It enables data security by avoiding the risk of data loss and data overlapping.The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient.In our work,we propose a data scheduling model for the cloud environment.Themodel is made up of three parts that together help dispatch user data flow to the appropriate cloudVMs.The first component is the Collector Agent whichmust periodically collect information on the state of the network links.The second one is the monitoring agent which must then analyze,classify,and make a decision on the state of the link and finally transmit this information to the scheduler.The third one is the scheduler who must consider previous information to transfer user data,including fair distribution and reliable paths.It should be noted that each part of the proposedmodel requires the development of its algorithms.In this article,we are interested in the development of data transfer algorithms,including fairness distribution with the consideration of a stable link state.These algorithms are based on the grouping of transmitted files and the iterative method.The proposed algorithms showthe performances to obtain an approximate solution to the studied problem which is an NP-hard(Non-Polynomial solution)problem.The experimental results show that the best algorithm is the half-grouped minimum excluding(HME),with a percentage of 91.3%,an average deviation of 0.042,and an execution time of 0.001 s.

Hybrid Flow Shop with Setup Times Scheduling Problem

The two-stage hybridflow shop problem under setup times is addressed in this paper.This problem is NP-Hard.on the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high performance-computing.Tackling this kind of problem requires the development of adapted algorithms.In this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this paper.These approximate solutions are using the optimal solution of the parallel machines under release and delivery times.Indeed,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be solved.The general solution is then a concatenation of all the solutions in each stage.In addition,three lower bounds based on the relaxation method are provided.These lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative gap.An experimental result is discussed to evaluate the performance of the developed algorithms.In total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and heuristics.Several indicators are given to compare between algorithms.The results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.