## 4 Experimentation and Result Evaluations
### Proposed and Reference Schemes
For example, in our work, some tasks need to be offloaded to the same edge server, which provides the resources for the tasks to be executed equally by not considering local execution on the user device at all. Therefore, it is not as effective as our proposed approach. Load Balancing can save energy on user devices, but it can take much time to execute the task, leading to higher computation costs as well as computation latency. So, our proposed algorithm can make intelligent offloading decisions and optimize resources for tasks on edge servers, thereby reducing the total cost and total latency. This approach ensures that the total available MEC server resources meet the task requirements and time constraints while saving energy on user devices. Second, the proposed LDROA can be superior to the method of Random Offloading, because in the random resource offloading scheme that is mentioned in references [32; 33], tasks are assigned to available resources such as edge servers without considering factors such as resource availability, network conditions, or power consumption. This approach can result in inefficient resource utilization, longer latencies, and higher power consumption because tasks may be assigned to resources that are not optimized for their execution. This approach can result in the complex task being offloaded to the MEC server with congestion or with a very little number of available resources for each task. This leads to high congestion and increased execution time as well as computation costs. However, our algorithm can dynamically adjust with the minimum required resource for each task so that the remaining resource of the edge server can be well utilized for the next coming task. Our algorithms not only elastically adjust the resource to minimize the cost and latency, but also proactively estimating the total communication and computation cost of the tasks on the edge server and computation cost on the user device and make the optimal decision of whether the task should be uploaded or just computed locally by considering the maximum time constraint of the IoT application utilized in this work. According to this process of algorithm work, that is why we clarify that our algorithms are good. Third, the proposed LDROA can be superior to the method of Greedy Latency offloading because the Greedy Latency offloading mentioned in [31; 32] makes decisions based on the estimated latency of executing the task locally on the user device or offloading the task to the edge server. The offloading decision is greedy in the sense that it always chooses the option with the lowest estimated latency without worrying too much about energy or cost. If the latency of executing the task locally is lower than the latency of offloading the task to the edge server, the task will be executed locally on the user device, even if the power or cost is higher on the edge server. If the estimated latency of offloading the task to the edge server is lower than executing the task locally, the task is offloaded to the edge server for execution without regard for compute cost or energy. A weakness of the Greedy Latency offloading scheme is that it does not take into account the available resources on the edge server or network conditions. The offloading decision is based solely on minimizing latency, and it may result in overloading some edge servers or congesting networks. This means that all tasks appear to be offloaded to the edge server with the highest computing capacity rather than considering its available resources. In contrast, our proposed algorithms can dynamically adapt to each task with the minimum required resource, so that the remaining resource of the edge server can be well utilized for the next coming task.