###### Abstract  
Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools--EdgeSimPy in particular--to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools. edge computing; EdgeSimPy; resource management; simulation +
Footnote †: journal: Network  
**Citation:** [PERSON]; [PERSON]; [PERSON] Exploring the Impact of Resource Management Strategies on Simulated Edge Cloud  
**Performance:** An Experimental Study. **Network 2024**, 4, 498-522. [[https://doi.org/10.3390/network40400025](https://doi.org/10.3390/network40400025)]([https://doi.org/10.3390/network40400025](https://doi.org/10.3390/network40400025))  
Academic Editor: [PERSON]  
Received: 29 July 2024  
Revised: 9 October 2024  
Accepted: 4 November 2024  
Published: 6 November 2024  
**Copyright**\(\copy\) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). Furthermore, the dynamic nature of edge networks, as well as the mobility of edge devices, adds complexity to workload distribution and resource allocation. Addressing these difficulties while maximizing energy consumption and lowering delay complicates the development of resource management algorithms by mandating the study of several interacting variables. To solve these challenges, this work uses the EdgeSimPy simulation tool [3], which allows for a systematic and controlled examination of resource management strategies in a simulated environment. In this context, our research focuses on analyzing and comparing alternative resource management algorithms using specific metrics (e.g., CPU, memory, disk utilization, power consumption, and latency) to establish their effectiveness in managing resources in a limited edge environment. The simulation system we use includes realistic scenarios like dynamic network connectivity and changeable user locations, bridging the gap between theoretical models and real-world applications. This approach allows researchers to test the behavior of these algorithms in dynamic contexts, supporting the iterative refining of resource management strategies before their real-world deployment. A variety of edge cloud simulation tools have been developed over the years to solve these difficulties; however, many research articles do not provide a detailed experimental evaluation of alternative algorithms in a simulated environment using standardized performance criteria.