An Efficient Cloud Computing System for E-Health | ||
| Scientific Journal for Damietta Faculty of Science | ||
| Volume 13, Issue 1, June 2023, Pages 48-56 PDF (638.52 K) | ||
| Document Type: Review article. | ||
| DOI: 10.21608/sjdfs.2023.198673.1092 | ||
| Authors | ||
| H. H. El Hadidi1; A. A. El-harby2; G. M. Behery2; R. Abedel Hady Omar* 3 | ||
| 1Department of Computer Science, Faculty of Computers and Artifical Int ellgence, Damietta University, New Damietta | ||
| 2Department of computer science, Faculty of computers and artificial intelligence, Damietta university, Damietta | ||
| 3Department of Computer Science, Faculty of Science, Damietta University, New Damietta | ||
| Abstract | ||
| Cloud computing has become an indispensable component of the E-health services sector. Based upon, the data of E-Health cloud is increasing day by day as a result of increasing patient data and medical team diagnoses. This leads to a huge amount of data being stored that is easy to lose during disaster occurred. In order to that, an optimization system named The Particle Swarm Multi-Objective Optimization (PSMOO) was proposed for serving and recovering data of the E-Health cloud during system failure. The Particle Swarm Optimization (PSO) algorithm was used for the E-Health data scheduling procedure. The proposed system has the ability to play an important role in achieving the reliability of the cloud computing E-Health environment by considering the available resources to prevent data loss. The system compared PSO and genetic algorithms to show that the proposed system is robust, integrated and reliable for the disaster recovery. The experimental results illustrated the proposed system spent a shorter time and the lowest possible cost to complete the disaster recovery process. | ||
| Keywords | ||
| Cloud Computing; E-Health; Disaster Recover; Particle Swarm Optimization Algorithm | ||
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