## 6 Conclusions  
This study provides an in-depth evaluation of various resource-management methods in edge cloud systems, showing their unique performance across crucial criteria. Our findings show that the location-based method is the most effective at optimizing CPU and RAM utilization thanks to its ability to allocate resources based on users' geographic proximity. This geographic-aware strategy results in a significant decrease in processing overhead and increased overall system efficiency. In contrast, the best-fit method outperforms in terms of disk use, demonstrating its ability to manage storage resources and reduce excessive disk consumption. In terms of energy efficiency, the worst-fit and power-saving algorithms beat the others, indicating that they can contribute to energy-efficient computing in resource-constrained edge infrastructures. Additionally, the location-based algorithm proved to be the best in terms of latency, offering the lowest response times and enhancing user experience. Our research advances the subject by improving our understanding of how alternative resource-management strategies perform in simulated edge environments, bridging the gap between theoretical design and practical implementation. We used the EdgeSimPy simulation framework to conduct a detailed investigation of the performance of four algorithms--location-based, best-fit, worst-fit, and power-saving--using a number of measures, such as CPU, memory, disk utilization, power consumption, and latency. This study sheds light on how edge computing situations affect the effectiveness of these algorithms in real-world environments with dynamic network circumstances, variable workloads, and user mobility. The findings emphasize the significance of tailoring resource management solutions to specific goals, such as lowering latency or maximizing energy efficiency, based on the context and application requirements. For example, the location-based algorithm succeeds at latency reduction but exhibits inconsistencies in power and disk management, indicating the need for fine-tuning and hybrid approaches to achieve balanced performance across all parameters. Similarly, while the best-fit method is efficient in storage management, it requires additional refinement to more evenly allocate CPU and memory resources. This study serves as a reference point for future research aimed at optimizing resource management in edge cloud systems by illustrating the merits and shortcomings of each method in various circumstances. Our experimental results point to potential areas for development, such as the implementation of dynamic adaption mechanisms, parameter-optimization techniques, and context-aware resource scheduling. Furthermore, real-world validation and the implementation of scalability analysis will be required to translate these simulated insights into practical, implementable solutions. In conclusion, this study has contributed to a more nuanced understanding of resource management in edge cloud systems by providing a thorough experimental comparison of well-established methods in a simulated context. These findings improve edge computing knowledge by helping to design strong and effective resource management strategies that can be used for a wide range of applications. This study establishes the framework for future research efforts focused on developing high-performance, sustainable, and adaptable edge infrastructures that fully realize the promise of next-generation services and applications. Conceptualization, N.K. ; methodology, N.K. ; software, N.K. ; validation, N.K. ; writing--original draft preparation, N.K. and D.G.K. ; writing--review and editing, D.G.K. and C.Z.P. ; visualization, N.K. ; supervision, C.Z.P. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.