This paper presents test data generation using artificialfirefly (AF) algorithm in software testing based on Markov model. This concept initially focus on flow graph by connecting decision-to-decision (DD-graph) from...
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Since the cloud servers are far away from the medical detection terminal and user terminal, the communication overhead such as the delay caused by data transmission is large. At the same time, a large number of medica...
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Since the cloud servers are far away from the medical detection terminal and user terminal, the communication overhead such as the delay caused by data transmission is large. At the same time, a large number of medical terminals and user terminals access the cloud servers, which makes the cloud servers overloaded, the overall robustness of the network is poor, and the network is prone to failure, which may lead to the work efficiency of doctors cannot be guaranteed, and the waiting time of patients will also increase. To solve the above problems, according to the characteristics of dynamic resource allocation in the medical big data environment, a new cloud network architecture is proposed. To solve the resource scheduling problem, a chaotic algorithm is introduced into the artificial firefly algorithm, and a load balancing optimisation strategy based on a chaotic fireflyalgorithm is proposed. The simulation results show that the convergence rate of the proposed algorithm is accelerated by adding chaos factor, to avoid the algorithm falling into the local optimal solution. Compared with other load balancing algorithms, the proposed algorithm is more suitable for solving the resource scheduling problem of large-scale tasks in cloud-fog networks.
The problem of resources under the environment of cloud computing has always been the focus of research. In this paper;the artificial firefly algorithm is studied, on the basis of which the chaos algorithm is introduc...
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The problem of resources under the environment of cloud computing has always been the focus of research. In this paper;the artificial firefly algorithm is studied, on the basis of which the chaos algorithm is introduced to improve the algorithm with regard to the problem of the subsequent search and optimization precision deficiency cause by the lack of initialization of firefly position in the fireflyalgorithm. Meanwhile, the updating of luciferin is improved. The improved algorithm is enhanced greatly in accuracy and performance. Through the simulation platform Cloudsim, it is found that under the cloud computing model, the algorithm presented in this paper can effectively reduce the average time spent by subtasks in processing request tasks, and thus improve the efficiency of task processing and achieve a rational allocation of resources.
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