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Because of the continuous promotion of cloud computing applications, the demand for data processing in cloud computing is increasing. Users have higher requirements for the service quality of cloud computing, the high efficiency of cloud computing task scheduling algorithm plays a key role in the cloud computing. How to scheduling the computing resources efficiently, all tasks can be completed in the least time and cost is an important issue in cloud computing research. In this paper, a method of initial optimization on the crossover mutation probability of adaptive genetic algorithm (AGA) using binary coded chromosomes is proposed. Through experiments, the improved adaptive genetic algorithm is compared with the adaptive genetic algorithm (AGA) and the standard genetic algorithm (SGA). The experimental results show that the improved algorithm is an effective cloud computing task scheduling algorithm.

Cloud computing resource scheduling is a complex NP problem and difficult to solve. In order to shorten task completion time in cloud resource scheduling, an improved LDW-PSO (linearly decreasing weight-particle swarm optimization) algorithm is proposed. Firstly, for the fact that PSO algorithm is easy to fall into local convergence, based on the linearly decreasing weight strategy, the constant disturbance is added to increase the inertia weight, for the purpose of getting rid of local search and begining global search. Secondly, in order to avoid the situation that particles highly gather around the optimal particles, resulting in being similar and damaging the diversity of particle swarm, thus, by changing inertia weight mixed with random individuals Adaptively in a certain probability, it could better maintain the diversity of population. Finally, through different simulation tests on Matlab2010a platform, proving that the improved LDW-PSO algorithm can get a more accurate solution and optimize completion time in cloud computing resource scheduling.

The Component Library(CL)is the key to realize software reuse, the CL available on the network is decentralized, independent, not easy to access at present and existing component libraries face infrastructure supply and maintenance challenges. The authors use cloud computing model, which contribute to resolve the issues of visiting different component libraries and the scalability of component library resources. In this paper, we discuss the plan that placed the CL in the cloud infrastructure and put forward a hierarchical organizational model of component resources based on cloud storage to achieve component integration services. We also explore the component library retrieval framework in the cloud and verify the cloud model applied to component library retrieval based on the Hadoop platform can improve the system performance and achieve the scalability of the computing power.