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Tourism economic data is growing, making data scheduling crucial. This study enhances an artificial immune algorithm to optimize tourism data scheduling, reducing completion time and transmission delays for better cloud computing efficiency.

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Area of Science:

  • Computer Science
  • Data Science
  • Tourism Economics

Background:

  • The proliferation of image/video content and the expanding tourism economy are generating massive datasets, termed big data.
  • Efficient management and scheduling of this tourism economic data within cloud computing environments present a significant challenge.

Purpose of the Study:

  • To address the complexities of scheduling big data in the tourism sector.
  • To develop an optimized data scheduling algorithm for tourism economic data within cloud computing.

Main Methods:

  • A comprehensive review of existing research on image/video, cloud computing, tourism economics, and data scheduling algorithms.
  • Detailed exposition of cloud computing's origins, structure, development, and service models.
  • Development of a constraint model for tourism economic data scheduling, focusing on completion time and cross-node transmission delay.
  • Improvement of a fitness function using an artificial immune algorithm, incorporating gene recombination for optimal solution discovery.

Main Results:

  • The proposed enhanced artificial immune algorithm effectively optimizes tourism economic data scheduling.
  • The algorithm successfully incorporates completion time and cross-node transmission delay as key constraints.
  • Demonstrated efficiency with a response time of 107.92 seconds for a resource node scale of 100.

Conclusions:

  • The developed algorithm provides an effective solution for scheduling big data in the tourism economy.
  • The integration of cloud computing principles and advanced algorithms enhances data management efficiency.
  • The study offers a practical approach to optimizing data scheduling, crucial for the growing tourism data landscape.