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.

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Abstract-Data Grid provides resources for data-intensive scientific applications that need to access a huge amount of data around the world. Since data grid is built on a wide-area network, its latency prohibits efficient access to data. This latency can be decreased by data replication in the vicinity of users who request data. Data replication can also improve data availability and decreases network bandwidth usage. It can be influenced by two imperative constraints: Quality of Service (QoS) that is locally owned by a user and bandwidth constraint that globally affects on link that might be shared by multiple users. Guaranteeing both constraints and also minimizing replication cost consisting communication and storage costs is a challenging task. To address this problem, the authors propose to use a dynamic algorithm called Optimal Placement of Replicas to minimize replication cost and coupled with meeting both mentioned constraints. It is also designed as heuristic algorithms that are competitive with optimal algorithm in performance metrics such as replication cost, network bandwidth usage and data availability. Extensive simulations show that the Optimal algorithm saves 10% cost compared to heuristic algorithms and provides local responsiveness for half of the user requests.

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.