Research On Cloud Environment Resources Scheduling Basd On Improved QPSO-SFLA Algorithm

Cloud computing is one of the hottest topics in the field of information technology, widely concerned by public industry, academia, and government. Through continuous efforts of academia and industry, cloud of Computing Technology is gradually from theory to practice, each IT giants have added to the application of cloud computing in the past.Cloud resource scheduling policy has become an important research of cloud computing. Cloud computing scheduling strategy is the core technology of a large number of software and hardware devices on the network and resources through virtualization technology integrated into a flexible cloud processing system. Research of resource schedule algorithm has been a hot topic of Cloud Computing research. For the reasons given above, analyzing on some critical technologies about Cloud Computing, the paper focus on the study of cloud resource schedule algorithm. Details are as follows:(1) This paper use the resource control strategies, the deployment path and allocation algorithms to analysis the Supply strategy for cloud computing, cloud computing resource scheduling strategies and performance indicators resource scheduling, load balancing of cloud computing technology.(2) This paper explores the leapfrog algorithm(SFLA) in a cloud environment in the use of resource scheduling, inadequate analysis of shuffling on the basis of population improve leapfrog algorithm selection strategy. Improvement Shuffled Frog Leaping Algorithm(ISFLA) introduced roulette randomly selected during initialization population strategy, by strengthening the position of optimum fitness value of the individual to improve the convergence speed; the introduction of cellular sub-populations when re-shuffling automatic machine strategy, effectively avoid getting into local optimum algorithm.(3) The above improved SFLA algorithm and particle swarm search strategy combines quantum proposed shuffled frog leaping algorithm(QPSO-SFLA) based on quantum particle swarm new local search, thus algorithm improving the efficiency of local search algorithm to accelerate convergence.(4) Process simulation in the cloud computing platform Cloud Sim resource scheduling and task Improved algorithms for the above class experimental verification. The QPSO-SFLA algorithm, ISFLA algorithm and platform comes algorithm comparison, results show that QPSO-SFLA algorithm has significant efficiency and cost advantages on resource scheduling of cloud computing.