## 4 Experimentation and Result Evaluations
### Proposed and Reference Schemes  
To comprehensively assess the efficacy of our proposed approach, we undertake a rigorous comparative analysis against a set of reference schemes in our study. This comparative evaluation provides valuable insights into the relative performance and advantages of our proposed idea in contrast to existing methods commonly employed in edge computing environments. The reference schemes considered in our evaluation encompass a diverse range of offloading strategies, each with its unique characteristics and optimization goals. First, the Random Offloading scheme serves as a baseline approach, where tasks are offloaded to edge servers randomly without considering any optimization criteria. This simplistic strategy provides a benchmark for assessing the performance improvements achieved by more sophisticated offloading techniques. Second, the Load Balancing scheme aims to evenly distribute computational tasks among available edge servers to minimize resource contention and maximize resource utilization. This approach prioritizes load balancing across the network, thereby enhancing system scalability and responsiveness. Third, the Greedy Latency Offloading scheme focuses on minimizing latency by offloading tasks to the nearest edge server with the lowest latency. This strategy prioritizes latency optimization, ensuring rapid response times and improved user experience, particularly in latency-sensitive applications. By comparing our proposed idea against these reference schemes, we can evaluate its performance across various dimensions, including latency reduction, cost efficiency, resource utilization, and scalability. Through extensive experimentation and analysis, we aim to demonstrate the superiority of our proposed approach in optimizing task offloading decisions and enhancing the overall effectiveness of edge computing systems. Furthermore, the comparative evaluation provides valuable insights into the strengths and limitations of different offloading strategies, thereby informing future research directions and advancements in the field of edge computing. By systematically evaluating and benchmarking our proposed idea against established reference schemes, we contribute to the ongoing discourse on optimizing resource allocation and improving the performance of edge computing environments. Based on [33], the Random Resource Offloading scheme tasks are randomly offloaded to available resources such as edge servers without considering factors such as resource availability, network conditions, or energy consumption. Indeed, the utilization of simplistic or Random Offloading approaches can leadto several undesirable outcomes within edge computing environments. One notable consequence is inefficient resource utilization, where computational tasks may not be allocated optimally across available resources. As a result, certain edge servers may be underutilized while others are overwhelmed, leading to uneven resource distribution and suboptimal performance. This approach may result in inefficient resource utilization, longer latencies, and greater energy consumption. Overall, the use of Random Offloading strategies poses significant challenges in terms of resource management, latency optimization, and energy efficiency within edge computing environments. The Load Balancing resource offloading scheme is a technique used to optimize the usage of resources on edge servers. The goal of this scheme is to distribute the workload evenly across multiple servers or nodes in the system to prevent overloading and ensure equal resource allocation for each task. A load balancer evaluates the available resources on the system equally and decides which node or server is best suited to handle the task. Implementing Load Balancing mechanisms adds complexity to the system architecture, introducing overhead in terms of computational and management resources. Additionally, reliance on centralized controllers or algorithms can create a single point of failure, undermining system reliability and fault tolerance.