Abstract
Nowadays, the amount of data coming from terminal devices is increasing on a vast scale. Data centers in different geographical locations store vast amounts of data. In this paper, to optimize the data deployment problem, a data layout algorithm that can be satisfied with the load balancing of the cluster is introduced. Under the three constraints of cost, capacity, and load balance, this paper presents the idea of ant colony optimization and uses the Lagrangian relaxation method to verify the value of the optimal solution. For improving the execution efficiency of various cloud data centers, a task scheduling method is proposed. This method solves the problems of delay and transmission cost and then uses the gray Markov-based prediction method to predict the dynamic changes of resources of different cluster nodes and select the most suitable task scheduling node. As shown in the experiment, the completion time of the application can be shortened by this algorithm. Meanwhile, in this paper, the consumption of cluster resources is reduced, and the throughput of the cluster is improved.
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