Enriched multi objective optimization model based cloud disaster recovery

In cloud computing massive data storage is one of the great challenging tasks in term of reliable storage of sensitive data and quality of storage service. Among various cloud safety issues, the data disaster recovery is the most significant issue which is required to be considered. Thus, in this paper, analysis of massive data storage process in the cloud environment is performed and the massive data storage cost is based on the data storage price, communication cost and data migration cost. The data storage reliability involves of data transmission, hardware dependability and reliability. Reliable massive storage is proposed by using Enriched Multi Objective Optimization Model (EMOOM). The main objective of this proposed optimization model is using Enriched Genetic Algorithm (EGA) for efficient Disaster Recovery in a cloud environment. Finally, the experimental results show that the proposed EMOOM model is effective and positive and reliable.