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A dataset for mobile edge computing network topologies.

Bin Xiang1, Jocelyne Elias2, Fabio Martignon3

  • 1Politecnico di Milano, Italy.

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Researchers introduce synthetic Mobile Edge Computing (MEC) topologies for 5G networks. These datasets enable performance comparisons for resource allocation and network planning.

Keywords:
5G NetworkBase stationsGeographic locationMobile edge computingNetwork parametersNetwork topologyRandom graphs

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Area of Science:

  • Computer Science
  • Telecommunications Engineering

Background:

  • Mobile Edge Computing (MEC) is crucial for 5G and future applications requiring low latency and high bandwidth.
  • Existing research is hindered by the lack of public, realistic MEC network topologies for experimentation.

Purpose of the Study:

  • To address the scarcity of public MEC network data.
  • To provide researchers with realistic synthetic MEC topologies and scenarios for performance evaluation.

Main Methods:

  • Generated 3 synthetic MEC topologies with 25-100 nodes based on prior experimental data.
  • Created a MEC topology using OpenCellID data for 234 Vodafone LTE cells in Milan.
  • Included realistic parameters like bandwidth, capacity, and traffic derived from real MEC services.

Main Results:

  • Publicly released 3 synthetic MEC topologies and 1 real-world derived topology.
  • Provided realistic network parameters essential for simulating MEC environments.
  • Enabled comparative analysis of resource allocation, planning, and routing strategies.

Conclusions:

  • The released datasets facilitate extensive experimentation and benchmarking of MEC solutions.
  • This initiative aims to accelerate research and development in optimized resource allocation for 5G and beyond networks.