Summary One of the major concerns in security and storage improvement of data centers is due to the fact that there is a possibility of access to cloud information, and the distance between data centers for data transfer causes cloud storage problems. In this article, an encryption method using artificial immune system (AIS) is leveraged to secure sensitive data in data centers and particle swarm optimization (PSO) algorithm is exploited to improve the selection of storage services in distributed cloud data centers to store and transfer particular a data set between data centers. To obtain satisfied performance considering the transmitting cost, and smallest transmitting distance, we exploited one discrete PSO algorithm and AIS method and called PSO_AIS. To evaluate the proposed PSO_AIS algorithm, we designed the experiments and simulated it in MATLAB software. In the simulation, we conducted experiments on 13 data centers located in different locations in Iran. The experimental results expose that the proposed algorithm reduces 30% the distance and cost to select a particular data center for a particular data set. Experiments have been carried‐out to demonstrate the effectiveness of the proposed encryption method, which is based on AIS. The results showed that the encoding performance of the proposed method is better than other methods.

Storing extensive data in cloud environments affects service quality, transmission speed, and access to information in systems, which is becoming a growing challenge. In storage improvement, reducing various costs and reducing the shortest path in the storage of distributed cloud data centers are among the important issues in the field of cloud computing. In this paper, particle swarm optimization (PSO) algorithm and learning automaton (LA) are used to minimize the cost of a data center, which includes communication, data transfer, and storage and optimization of communication between data centers. To improve storage in distributed data centers, a new model called LAPSO is proposed by combining LA and PSO, in which LA improves particle control by searching for particle speed and position. In this method, LA moves each particle in the direction where it has the best individual and group experiences. In multipeak problems, it does not fall into local optimums. Results of the experiments are shown on the dataset of spatial information and cadastre of country lands, which includes 13 data centers. The proposed method evaluates and improves the optimal position parameters, minimum route cost, distance, data transfer cost, storage cost, data communication cost, load balance, and access performance better than other methods.