## 4 Experimentation and Result Evaluations
### Proposed and Reference Schemes
Moreover, Load Balancing may introduce network overhead, impacting latency and consuming bandwidth, particularly in distributed environments. Despite the goal of evenly distributing the workload, variations in task characteristics and resource capabilities may result in unequal resource utilization, affecting system performance. Furthermore, traditional Load Balancing approaches may struggle to adapt to dynamic changes in workload and resource availability, leading to suboptimal task allocation. In the Greedy Latency offloading scheme, the offloading decision is made 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 concerning too much about energy or cost consumption. One weakness of the Greedy Latency offloading scheme is that it does not consider the available resources on the edge server or the network conditions. The offloading decision is solely based on minimizing latency, and it may result in overloading some edge servers or congested networks. To summarize, the Greedy Latency offloading scheme, while effective in minimizing latency, may also present several drawbacks. Primarily, its locally optimal choices at each step without considering the available resource at edge server can lead to suboptimal resource utilization and cost efficiency. Tasks may be offloaded based solely on immediate latency, potentially resulting in resource imbalances among edge servers and inefficient utilization. Additionally, the scheme's reliance on network latency as the primary criterion for task allocation may make it sensitive to variable network conditions, risking network congestion and degraded performance. Moreover, Greedy Latency schemes may overlook energy consumption considerations, potentially leading to increased energy usage and operational costs. Overall, while effective in improving responsiveness, the Greedy Latency offloading scheme must be employed judiciously considering its potential drawbacks in resource management, network congestion, and computation cost. Finally, the proposed LDROA algorithm can be used to consider on/offloading decisions and find the minimum execution time for a given task at edge node, subject to various constraints such as resource availability resource limits and the requirements of the IoT time constraint. By using Lagrange, the proposed approach can provide an effective way of adjusting the available resources for each task. This can result in a more optimal resource allocation scheme, leading to reduced energy consumption, lower latency costs, and decreased computation costs. For example, if a task requires a large amount of computing power but has a low-latency requirement, the Lagrange method can adjust the resource allocation to favor computing power over latency. The joint offloading decisions and resource allocation by Lagrange, which is called the LDROA, can provide a more effective way of minimizing both computation time and computation cost in the model. The main purpose of our algorithms is to save energy on user devices and optimize resource allocation for offloading tasks to edge servers. This dual objective aims to enhance both the battery life of user devices and the overall latency for tasks executed at edge servers. That is why we consider the computation cost for comparison in our algorithm. First, LDROA outperforms Load Balancing because the goal of Load Balancing is to distribute the workload evenly across multiple servers or nodes in the system to prevent congestion and ensure equal resource allocation for each task. In this scheme, the workload is analyzed to determine the number of resources required to complete the task. The load balancer then evaluates the resources available in the system and decides which node or server is best suited to handle the task.