Dynamic Resource Allocation for Network Slicing with Multi-Tenants in 5G Two-Tier Networks

Area of Science:

• Telecommunications Engineering
• Computer Networking
• Resource Management

Background:

• Virtualization is key to 5G networks, enabling cost savings through shared hardware.
• Multi-tenant service diversity presents challenges in guaranteeing Quality of Service (QoS).
• Network slicing isolates resources for different tenants but optimizing allocation remains difficult.

Purpose of the Study:

• To propose heuristic algorithms for dynamic resource allocation in multi-tenant 5G network slices.
• To address the critical challenge of optimizing network and computation resource allocation across diverse network slices.

Main Methods:

• Development of two heuristic algorithms: Minimum Cost Resource Allocation (MCRA) and Fast Latency Decrease Resource Allocation (FLDRA).
• Implementation within a two-tier architecture for dynamic path routing and resource allocation.
• Comparative simulation against the UFLOP algorithm.

Main Results:

• Both MCRA and FLDRA significantly outperform the UFLOP algorithm.
• MCRA demonstrates higher resource utilization compared to FLDRA.
• The proposed algorithms effectively manage resources for multi-tenant network slices.

Conclusions:

• MCRA and FLDRA offer effective solutions for resource allocation in 5G network slicing.
• The proposed algorithms enhance efficiency and performance in virtualised multi-tenant environments.
• Further research can explore advanced optimization strategies for 5G network resource management.