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Geographically distributed data management to support large-scale data analysis.

Tamer Z Emara1, Thanh Trinh2,3, Joshua Zhexue Huang4,5

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This study introduces a framework for managing massive data across geo-distributed data centers. It enables efficient big data analysis by intelligently distributing and replicating data blocks for improved performance and reliability.

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Area of Science:

  • Computer Science
  • Data Management
  • Distributed Systems

Background:

  • Companies increasingly use multiple data centers for faster response times and disaster recovery.
  • Rapid data growth presents significant storage, analysis, and processing challenges.
  • Existing solutions struggle with managing massive datasets across geographically dispersed locations.

Purpose of the Study:

  • To propose and design a novel geographically distributed data management framework.
  • To enable efficient utilization of distributed data blocks for big data analysis tasks.
  • To address the challenges of managing and analyzing massive datasets across geo-distributed data centers.

Main Methods:

  • Developed a framework with geo-distributed data centers connected to a central data controller (DCtrl).
  • Utilized the Big Data Management System (BDMS) to store big data files as sampled data blocks.
  • Implemented DCtrl for organizing and managing replicated data blocks across data centers.
  • Employed random sampling of replicated data blocks for big data analysis.

Main Results:

  • Simulation results demonstrate the framework's effectiveness in managing data across geo-distributed data centers.
  • The proposed system facilitates efficient big data analysis by leveraging distributed data blocks.
  • The architecture supports robust data replication and management for enhanced reliability.

Conclusions:

  • The proposed geographically distributed data management framework effectively handles massive datasets.
  • The system enhances big data analysis performance in geo-distributed environments.
  • This approach offers a scalable solution for modern data management challenges.