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This study introduces a new AI-driven task assignment method (TABAI) for cloud computing load balancing. TABAI enhances long-term resource utilization and reduces user waiting times compared to traditional methods.

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Area of Science:

  • Cloud Computing
  • Big Data Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Current cloud computing load balancing methods focus on immediate deployment, leading to inefficiencies and prolonged user wait times.
  • Optimizing load balancing is crucial for big data processing in cloud environments.

Purpose of the Study:

  • To propose a novel task assignment method for long-term resource load balancing in cloud platforms using artificial intelligence and big data.
  • To improve resource utilization and external service capabilities of cloud platforms.

Main Methods:

  • Calculated maximum posterior probability for each physical host using Bayesian theory.
  • Utilized Euler's formula to compute host similarity and establish a threshold for classification.
  • Applied cluster analysis combined with Bayes' theorem for global, time-dimension load balancing.

Main Results:

  • The proposed TABAI method demonstrated a smaller processing time compared to traditional multi-task assignment methods.
  • TABAI showed a significant decrease in standard deviation after 600 seconds, indicating improved stability.
  • The method enhanced the external service capabilities of the cloud platform.

Conclusions:

  • TABAI effectively achieves global load balancing in the time dimension by integrating cluster analysis and Bayes' theorem.
  • The approach leads to better resource utilization and external service capacity in cloud platforms.
  • TABAI offers a more efficient and stable load balancing solution for big data processing in the cloud.