Research On The Resource Allocation And Job Scheduling In Cloud Computing Environment

Cloud computing is an emerging model of business computing emerging in the context of the increase demand of businesses and individuals, and it is the hot area of research of many IT companies and university research institutes. The cloud computing resource are abundant and dynamically changed, and the service demand of cloud client differ from each other in a thousand ways, which made the job scheduling of cloud computing more complex. The job scheduling of cloud computing is a NP-complete problem.because of it’s commercial, Cloud computing pay more attention on the user’s service quality, try to meet the demand of every user as possible. However the virtualization technology implementing cloud computing is very different from grid computing in the aspect of resource allocate and job scheduling. Thus, scheduling the computing resource with a good schedule strategy, improving the resource utilization rate, building a flexible scheduling model that can meet the performance requirement is meaningful.The research content of this paper includes the followings:First, in this paper, after analysis the present some intelligent algorithm applied to cloud computing tasks scheduling, we use an optimization algorithm-quantum genetic algorithm to solve the cloud computing job scheduling problem. Quantum genetic algorithm combined the characteristics of quantum computing with traditional genetic algorithm. Quantum bits code chromosome can make a variety of state present at the same chromosome, which enrich the diversity of population. And quantum logic gate update and evolution the chromosome, and ultimately the problem’ solving the optimization. Experiments results show that Quantum genetic algorithm out performs the traditional genetic algorithm. Quantum genetic algorithm-based cloud computing job scheduling methods are as follows:First, encoded chromosome based on the characteristics of cloud computing job scheduling. Then design the process and steps of quantum genetic algorithm-based cloud computing job scheduling algorithm. Second, the exiting some job scheduling strategy only considerd the whole finish time of job, neglected the average finish time of every user. Aim to solve this problem, this paper propose a double fitness function quantum genetic algorithm-based cloud computing job scheduling. This method introduce a fitness function of user average job finish time, and adjust the algorithm flow.Finally, a cloud computing simulation tool "CloudSim" were used to implement the job scheduling methods proposed in this paper. Compared with round robin algorithm and genetic algorithm-based job scheduling algorithm, the results showed that our methods are more effective and correct.