## 2 Related Work  
Several issues of resource allocation, optimization, and efficiency in edge computing deployments have been the subject of numerous research. Here, we present a summary of relevant research that has advanced our knowledge of resource-management techniques in edge cloud settings. In [4], the authors focus on resource allocation. Resource scheduling and resource provisioning included two portions of some of the metrics for resource management that were examined. Their scheduling metrics are allocation, monitoring, and mapping; their provisioning metrics are detection, selection, and mapping of the distribution of the load. A number of other factors, or Key Performance Indicators (KPIs), were also looked into,including Service Level Agreement (SLA), QoS, scalability, latency, VM placement, failure rates, accuracy, resource utilization, cost, and energy consumption. This paper survey makes a very extensive contribution, although it only offers a small number of research publications that deal with edge, cloud, and fog computing. In [5], the authors examine the state of edge cloud offloading algorithms and focus on the difficulties, approaches, and potential paths forward in this quickly developing field. The authors address latency, energy usage, network bandwidth, and resource limitations in their thorough examination of the problems associated with shifting computation-intensive workloads from edge devices to the cloud. They examine current offloading algorithms and classify them according to their goals, methods for making decisions, and standards for optimization. Based on the collected literature, the authors cover a range of offloading algorithm approaches in single and multiple servers, offline and online algorithms, machine learning strategies, and heuristic approaches. Moreover, the writers offer viewpoints regarding the prospects of edge cloud offloading, emphasizing new developments, avenues for investigation, and issues that require attention. Similar to [6], the authors offer a taxonomy of resource-management techniques in fog computing. The following categories are taken into account by the following taxonomy: resource allocation, load balancing, task offloading, resource scheduling, resource provisioning, and application placement. Their main goal was to organize the material using resource-management theories. They included information on the case study, strategy, performance metric, evaluation instrument, benefits, and drawbacks for each resource-management approach. Overall, this study gave information about previous research on each resource-management strategy, but it only looked at fog computing, excluding the potentially fascinating inclusion of edge computing. Furthermore, the study simply took an exploratory approach to discussing the solution possibilities. Put another way, the research project is an analytical analysis and discussion of previous resource management studies. The authors of [7] propose two optimal edge server deployment schemes and three collaborative resource allocation optimization algorithms. The research aims to minimize the access delay of edge servers and optimize resource allocation in MEC. It discusses the impact of network nodes, requested data volume, and mobile devices on access delay, as well as the comparison of different algorithms. The study also introduces the concept of SDN technology and its role in optimizing edge server deployment. Additionally, it highlights the significance of MEC in providing real-time services with low transmission delay and its potential applications in various fields. The authors of [8] look into where various servers should be placed in a cloud computing system. It suggests a two-step method: an online stage for the dynamic aspects of user movement and an offline stage for the best server location. According to experimental data, the suggested method increases base station response time fairness by 71.60% and decreases system reaction time by 47.37%, respectively.