QoS-aware service composition based on context-free grammar and skyline in service function chaining using genetic...

Area of Science:

• Computer Science
• Network Engineering
• Software Engineering

Background:

• Service Function Chaining (SFC) enables dynamic service composition in virtual networks.
• Optimizing the selection of virtual services is crucial for meeting user demands and network constraints.
• Existing methods may not fully exploit available virtual infrastructure for efficient service chaining.

Purpose of the Study:

• To propose a novel approach for generating optimal service function compositions in SFC.
• To develop a genetic algorithm (GA) integrated with context-free grammar (CFG) for SFC.
• To ensure generated service chains adhere to Internet Engineering Task Force (IETF) standards.

Main Methods:

• A GA-based approach using CFG was developed for SFC.
• IETF use cases (data center, security, mobile) were employed to filter invalid service chains, reducing the search space.
• Genetic operations were defined to ensure standard service function chain generation.

Main Results:

• The proposed GA successfully identified the highest quality Skyline service chain instances.
• The method demonstrated superior performance over existing approaches in Quality of Service (QoS), running time, and time complexity.
• Experimental results validated the efficiency and effectiveness of the developed service composition method.

Conclusions:

• The novel GA-CFG approach provides an optimal solution for service function composition in SFC.
• The method significantly improves QoS, reduces running time, and enhances time complexity.
• The proposed CFG framework is adaptable and can be generalized for various SFC applications.