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      Optimization Model of Reliable Data Storage in Cloud Environment Using Genetic Algorithm

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      https://www.riss.kr/link?id=A100248623

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      다국어 초록 (Multilingual Abstract)

      Massive data storage is one of the great challenges for cloud computing service, and reliable storage of sensitive data directly affects quality of storage service. In this paper, based on analysis of data storage process in cloud environment, the cos...

      Massive data storage is one of the great challenges for cloud computing service, and reliable storage of sensitive data directly affects quality of storage service. In this paper, based on analysis of data storage process in cloud environment, the cost of massive data storage is considered to be comprised of data storage price, data migration and communication; and the storage reliability consists of data transmission reliability and hardware dependability. A multi-objective optimization model for reliable massive storage is proposed, in which storage cost and reliability are the objectives. Then, a genetic algorithm for solving the model is designed. Finally, experimental results indicate that the proposed model is positive and effective.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Problem Analysis
      • 2.1. Risk Analysis of Data Resources in Cloud Storage Environment
      • 2.2. DC (Data Center)
      • Abstract
      • 1. Introduction
      • 2. Problem Analysis
      • 2.1. Risk Analysis of Data Resources in Cloud Storage Environment
      • 2.2. DC (Data Center)
      • 3. Model Establishment
      • 3.1. Assumptions
      • 3.2. Storage Cost
      • 3.3. Migration Cost
      • 3.4. Communication Cost
      • 3.5. Total Cost for Storage
      • 3.6. Reliability of Data Storage
      • 4. Mathematical Model
      • 4.1. Objective Analysis
      • 4.2. Constraints
      • 4.3. Multi-objective Optimization Model
      • 5. GA for Solving Multi-Objective Optimization
      • 5.1. Flow of GA
      • 5.2. Solution Algorithm
      • 6. Simulation Experiment
      • 6.1. Experiment Setup
      • 6.2. Analysis of Experimental Results
      • 7. Conclusion
      • Acknowledgement
      • References
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