## 2 Related Works
[26] suggested the heuristic task migration computation offloading (TMCO) scheme to address these challenges. Unlike old approaches, TMCO dynamically identifies the best spots for unloading jobs from moving vehicles, ensuring that activities are performed within tight timeframes. This approach considered both vehicle mobility and strict delay requirements. In this technique, a hash table was used to track the number of tasks given to each server, while a random function mimics the likelihood of job offloading. According to experimental results, the TMCO scheme outperformed standard full-offloading systems in latency by 10% on average. The reliability, availability, and security of exchanged data are among the numerous challenges of vehicular networks. [PERSON] et al. [27] resolved the scientific challenge of creating a vehicle network structure that guarantees secure and correct data transmission in the V2X. They aimed to improve the safety of people using the roads by utilizing blockchain technology and MEC. The authors implemented an offloading model to manage traffic dispatching to the MEC efficiently. The system and the implemented subsystems and algorithms were evaluated in a dependable simulation environment across several scenarios. The simulations' results proved the system's efficiency. [PERSON] et al. [28] developed a seamless handover mechanism for MEC-based V2X systems. The suggested approach successfully handles the handover as vehicles travel between adjacent RSUs on multilane bidirectional roadways. The system was implemented with MECs linked to the RSUs, considerably reducing installation time and speeding up the handover procedure. The MEC platform uses a MEC controller, which runs under the control scheme of an SDN controller in charge of network management. The implementation of the SDN paradigm ensures a smooth handover process. The MEC controller speeds up the handover process, increasing overall efficiency. The evaluation results confirmed the effectiveness and efficiency of the suggested plan. [PERSON] et al. [29] offered a new methodology for estimating connectivity time in autonomous vehicular networks. The authors investigated the most accurate way to estimate the communication lifetime between automobiles and infrastructure units by using a variety of machine learning algorithms. They based their assessments on realistic mobility traces from the Cologne city dataset to ensure practical relevance and applicability. This dataset contains detailed and authentic vehicle movement patterns required for creating and testing the predictive algorithm in real-world scenarios. The suggested model combined a variety of machine learning methodologies, and each technique was tested for its accuracy and dependability in forecasting connectivity duration. The model considered various parameters influencing connectivity length, including vehicle speed, density, and traffic circumstances. Simulations and experiments demonstrated the efficiency of the proposed paradigm. The approach improved communication reliability in autonomous vehicle networks by precisely forecasting connectivity length, allowing for smoother and more efficient interactions between vehicles and infrastructure. Table 1 summarizes the features of the previously introduced related studies and compares these works with the proposed work to provide the novelty of the work. Overall, 5G high-availability testing is an important step in ensuring that next-generation wireless networks satisfy the demanding needs of modern applications. This evaluation is based on robust modeling stages that include advanced mathematical frameworks [30]. Table 2 presents some of the most common methodologies that are particularly useful in addressing performance and availability challenges in designing 5G infrastructures. SRNs and MUGFs have been widely employed in the literature to simulate and examine the robustness of telecommunication networks, specifically those related to 5G technology.