OLMA: Lyapunov-Driven Online Hierarchical Control for Stochastic Edge Computing in 6G Vehicular Networks_datasets

This dataset supports the performance evaluation of the Lyapunov-driven OLMA solver for Service Function Chain (SFC) placement in 6G vehicular networks. It consists of two primary components: (1) Real-world Vehicular Dataset: Derived from the Next Generation Simulation (NGSIM) program, where raw trajectory data (velocity and acceleration) are mapped into computational tasks. It includes parameters such as input\/output data size (D_{in}, D_{out}) and CPU cycle requirements (C_{in}), as well as wireless channel gains modeled with log-normal shadowing. (2) Synthetic Simulation Dataset: A pool of 800 diverse task samples generated using uniform distributions to stress-test the solver's robustness across extreme scenarios. This dataset enables researchers to reproduce experiments on task offloading, resource allocation, and SFC orchestration, providing a standardized benchmark for evaluating energy-delay trade-offs in dynamic VEC environments.