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

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  • 1Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi 62102, Taiwan.

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Summary
This summary is machine-generated.

This study introduces two new algorithms, MCRA and FLDRA, for optimizing network slice resource allocation in 5G virtualization. These methods improve upon existing solutions, with MCRA offering superior resource utilization for multi-tenant services.

Keywords:
dynamic offloadingmulti-access edge computingnetwork function virtualizationnetwork slicingoptimal resource allocation

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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.