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2 Computer Assisted Methods in Engineering and Science

2 2022

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Hybrid encryption algorithm for big data security in the Hadoop distributed file system

Mohanraj T., Santhosh R.

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

33--48

A large amount of structured and unstructured data is collectively termed big data. Therecent technological development streamlined several companies to handle massive dataand interpret future trends and requirements. The Hadoop distributed file system (HDFS)is an application introduced for efficient big data processing. However, HDFS does not have built-in data encryption methodologies, which leads to serious security threats. Encryption algorithms are introduced to enhance data security; however, conventional algorithmslag in performance while handling larger files. This research aims to secure big data usinga novel hybrid encryption algorithm combining cipher-text policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) algorithms. The performanceof the proposed model is compared with traditional encryption algorithms such as DES, 3DES, and Blowfish to validate superior performance in terms of throughput, encryptiontime, decryption time, and efficiency. Maximum efficiency of 96.5% with 7.12 min encryption time and 6.51 min decryption time of the proposed model outperforms conventionalencryption algorithms.

A hybrid optimized resource allocation modelfor multi-cloud environment using batand particle swarm optimization algorithms

Selvapandian D., Santhosh R.

Computer Assisted Methods in Engineering and Science

2022

Vol. 29, no. 1-2 spec.

87--103

In cloud computing, scheduling and resource allocation are the major factors that definethe overall quality of services. An efficient resource allocation module is required in cloudcomputing since resource allocation in a single cloud environment is a complex process.Whereas resource allocation in a multi-cloud environment further increases the complexityof allocation procedures. Earlier, resources from the multi-cloud environment were allocated based on task requirements. However, it is essential to analyze the present resourceavailability status and resource capability before allocating to the requested tasks. So, inthis research work, a hybrid optimized resource allocation model is presented using bat optimization algorithm and particle swarm optimization algorithm to allocate the resourceconsidering the resource status, distance, bandwidth, and task requirements. Proposedmodel performance is evaluated through simulation and compared with conventional optimization algorithms. For a set of 500 tasks, the proposed approach allocates resourcesin 47 s, with a minimum energy consumption of 200 kWh. Compared to conventionalapproaches, the performance of the proposed model is much better in terms of deadlinemissed tasks, resource requirement, energy consumption, and allocation time.