Slice-aware 5G network orchestration framework based on dual-slice isolation and management strategy (D-SIMS).

Area of Science:

• Computer Science
• Telecommunications Engineering

Background:

• Network slicing is a key 5G feature, virtualizing resources via SDN and NFV.
• Effective resource allocation is vital for VNF managers to meet VSR demands.

Purpose of the Study:

• To investigate and compare two network slicing frameworks: D-SIMS.
• To recommend the optimal framework based on performance metrics.

Main Methods:

• Phase 1: Deep Neural Network (DNN) for slice creation (PI and VSR).
• Phase 2: D-SIMS for resource allocation using fuzzy-PROMETHEE (node mapping) and Dijkstra's algorithm (link mapping).
• Evaluation using accuracy, precision, recall, F1 score, acceptance rate, and resource effectiveness.

Main Results:

• DNN classification performance evaluated during slice creation.
• D-SIMS demonstrates effective resource allocation strategies (SSRR and MSRR).
• Performance benefits investigated under diverse network conditions and VSRs.

Conclusions:

• D-SIMS provides a flexible and efficient approach to network slicing resource management.
• The proposed methods enhance VNF manager strategies for optimal PI resource allocation.
• Comparative analysis validates the proposed work against existing literature.