Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic...

Area of Science:

• Computer Science
• Cloud Computing
• Artificial Intelligence

Background:

• Cloud computing platforms face challenges in efficiently managing resources due to rapid expansion.
• Optimizing system performance and resource utilization is crucial for large-scale cloud environments.

Purpose of the Study:

• To propose an efficient method for evaluating server node load states in cloud computing.
• To develop a dynamic load balancing scheduling algorithm that improves upon existing methods.

Main Methods:

• Utilized Analytic Hierarchy Process Group Decision (AHPGD) to assess server node load states.
• Employed a Hybrid Hierarchical Genetic Algorithm (HHGA) to optimize a Radial Basis Function Neural Network (RBFNN) for load prediction.
• Developed a dynamic load balancing algorithm integrating AHPGD, HHGA-optimized RBFNN, and weighted round-robin principles.

Main Results:

• The proposed method effectively evaluates the load state of server nodes.
• The dynamic load balancing algorithm successfully predicts node load values and updates weights.
• The new algorithm retains the benefits of static weighted round-robin while mitigating its drawbacks.

Conclusions:

• The AHPGD and HHGA-optimized RBFNN provide an effective approach for predicting cloud server load.
• The proposed dynamic load balancing algorithm enhances cloud computing performance through efficient resource allocation.