mecRT: Mobile Edge Computing Simulator for Real-Time Applications

(previously known as VecSim - Vehicular Edge Computing Simulator)

mecRT is an open-source simulator for Mobile Edge Computing (MEC) scenarios. It provides a comprehensive framework for modeling task offloading and resource allocation in heterogeneous 5G-enabled environments. By integrating realistic wireless communication (via Simu5G) with edge computing models, mecRT enables researchers to evaluate scheduling strategies for latency-sensitive applications under resource and deadline constraints.

demo.mp4

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🔍 Why mecRT?

Limitations of existing MEC simulators for real-time applications:

• iFogSim / EdgeCloudSim: Good for resource modeling, but lack 5G network support and cannot capture real-time channel quality feedback.
• Simu5G / Fogbed: Offer fine-grained 5G network modeling, but do not support integrated offloading frameworks that jointly optimize bandwidth and computational resources under deadline constraints.

mecRT bridges this gap by combining realistic 5G-based communication with a deadline-aware scheduling framework. This enables evaluation of various resource management strategies and adaptive offloading control strategies in dynamic MEC environments for real-time applications.

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✨ Key Features

• Task Life Cycle Management

  • Service request registration when UEs enter the MEC coverage area.
  • Service deployment on Edge Servers (ESs).
  • Task offloading, execution, and result retrieval with deadline monitoring.
  • Request deregistration upon UE exit.

• Real-World Data Integration

  • Support real-world application executiuon profiling (e.g., execution time, energy consumption).
  • Configurable application profiles: task type, period, deadline, data size.
  • Support for real traffic GPS traces or synthetic trajectories (e.g., via SUMO).

• Joint Bandwidth and Computational Resource Optimization

• Adaptive Offloading Control

  • Enable/Terminate offloading based on service deployment and resource management.
  • Suspend/Resume offloading based on real-time channel quality feedback (i.e., via SRS).
  • Support customizable offloading control policies.

• 5G-Based Communication

  • Built on Simu5G for realistic 5G-URLLC network modeling.

• Customizable Scheduling Policies

  • Easily implement, evaluate, and benchmark different scheduling algorithms.
  • Support various scheduling modes:
    • Schedule all requests periodically or only schedule pending requests.
    • Enable/Disable Task Forwarding in wired backhaul network after task being offloaded to ES.
    • Consider/Ignore scheduling overhead.

• Easy Data Collection and Analysis

  • Configurable experiment parameters via one .ini file.
  • Easy parameter data logging.
  • Provides scripts for simulation result fetching and analysis.

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🏗️ System Architecture

A typical mecRT environment consists of:

• User Equipments (UEs) that offload tasks to nearby Edge Servers (ESs) via 5G links.
• Edge Servers providing both wireless bandwidth and computational resources.
• A centralized Scheduler that monitors system state and periodically decides offloading and allocation.

mecRT extends Simu5G with:

• Online control logic for bandwidth reallocation and suspension under poor channel conditions.
• Deadline-aware resource allocation for real-time MEC applications.

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🚀 Installation

Installation Instruction

If using WSL2 on windows 10/11, consider additional GUI setting.

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⚡ Quick Start

Coming Soon...

1. Run an example simulation:

1.1 Build mecRT:

```bash
$ cd mecRT
$ make cleanall
$ make makefiles
$ make -j8
```

1.2 Run the example scenario:

```bash
$ cd mecRT/simulations/decentralized/
$ ./examples.sh
```

2. Modify the provided omnetpp.ini configs to set:

   • Number of UEs and ESs
   • Task periods, deadlines, and data sizes
   • Scheduling policies

3. Visualize Result

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📊 Example Use Cases

• Evaluate deadline-aware task offloading algorithms.
• Benchmark adaptive resource allocation under variable 5G channel quality.
• Study trade-offs between latency, throughput, and computational load.
• Integrate real-world traces and application profiles for realistic workloads.

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📚 Citation

If you use mecRT in your research, please cite:

@INPROCEEDINGS{gao2025real,
  author={Gao, Chuanchao and Easwaran, Arvind},
  booktitle={2025 IEEE Real-Time Systems Symposium (RTSS)}, 
  title={Real-Time Service Subscription and Adaptive Offloading Control in Vehicular Edge Computing}, 
  year={2025},
  volume={},
  number={},
  pages={175-188},
  doi={10.1109/RTSS66672.2025.00023}}

or

[1] C. Gao and A. Easwaran, “Real-Time Service Subscription and Adaptive Offloading Control in Vehicular Edge Computing,” in 2025 IEEE Real-Time Systems Symposium (RTSS)., IEEE Computer Society, 2025, pp. 1591–188. doi: 10.1109/RTSS66672.2025.00023.

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🙏 Acknowledgements

• This project is supported by the MoE Tier-2 grant MOE-T2EP20221-0006 (Singapore) under the NTU Cyber-Physical Systems Research Group.
• Built on OMNeT++, INET, and Simu5G.