Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in...

Area of Science:

• Computer Science
• Telecommunications Engineering
• Operations Research

Background:

• 5G network slicing requires robust resource management in combined edge and core cloud environments.
• Client high availability demands pose challenges for admission control in edge cloud systems.

Purpose of the Study:

• To propose an orchestrator with a mathematical programming model for efficient resource management in 5G network slices.
• To address the challenge of high availability requirements in edge and core cloud computing.

Main Methods:

• Developed a global viewpoint mathematical programming model for resource management.
• Employed a Lagrangian relaxation-based approach for near-optimal problem-solving.
• Evaluated the approach through experimental cases and performance analysis.

Main Results:

• The proposed orchestrator effectively manages resources in combined edge and core cloud environments.
• The Lagrangian relaxation approach achieved near-optimal solutions, enhancing system revenue.
• Experimental results verified the approach's efficiency and effectiveness.

Conclusions:

• The novel orchestrator significantly enables 5G network slicing services.
• The approach efficiently enhances client satisfaction with high availability.
• This solution is suitable for edge and core cloud computing environments in 5G.