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Summary Cloud computing is distributed computing on a large scale driven by practical and effective operations, in which a pay‐per‐use framework provides dynamic scaling in response to the needs of workflow applications. Many existing cloud computing environments do not effectively employ security measures to counter security threats in task scheduling. To improve the scheduling system, we include security service to the scheduling process. However, adding security services to applications inevitably causes overhead in terms of computation time. The tradeoff between achieving high computing performance and providing the desired level of security protection imposes a big challenge for task scheduling. To solve this problem, we propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. Our proposed algorithm is based on the hybrid optimization approach, which combines Firefly and Bat algorithms. The coding strategy is to minimize the total execution cost while meeting the deadline and risk rate constraints. The proposed system uses a multi‐objective function, and the results indicate that our algorithm always outperforms the traditional algorithms.

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Cloud databases have been used in a spate of web-based applications in recent years owing to their capacity to store big data efficiently. In such a scenario, access control techniques implemented in relational databases are so modified as to suit cloud databases. The querying features of cloud databases are designed with facilities to retrieve encrypted data. The performance with respect to retrieval and security needs further improvements to ensure a secured retrieval process. In order to provide an efficient secured retrieval mechanism, a rule- and agent-based intelligent secured retrieval model has been proposed in this paper that analyzes the user, query and contents to be retrieved so as to effect rapid retrieval with decryption from the cloud databases. The major advantage of this retrieval model is in terms of its improved query response time and enhanced security of the storage and retrieval system. From the experiments conducted in this work, proposed model increased storage and access time and, in addition, intensified the security of the data stored in cloud databases.