## 2 Related Work
### Task Offloading  
Current task offloading algorithms include three types: minimum energy consumption, minimum latency, and the trade-off between energy consumption and latency. We will compare these algorithms and analyze their advantages and disadvantages.  
For algorithms that optimize execution time, we have found the following two options: [PERSON] et al. [23] adopt a Markov decision process framework to tackle the given problem, wherein the scheduling of computational tasks is dictated by the queuing status of the task buffer, the operational state of the local processing unit, and the status of the transmission unit. Furthermore, they introduce an efficient one-dimensional search algorithm to identify the optimal task scheduling policy. The advantage of this algorithm is the greatly reduced execution time compared to the case of not using computing offloading. The disadvantage is that it is very complex. [PERSON] et al. [24] proposed a low-complexity online algorithm, namely the dynamic computation offloading algorithm based on Lyapunov optimization, which jointly determines the offloading decision, the CPU cycle frequency for mobile execution, and the transmit power for computation offloading. The unique advantage of this algorithm is that the decision-making only relies on the current system state, without the need to calculate the distribution information of task requests, wireless channels, and EH processes. The disadvantage is that it has weak applicability because the data are completely offloaded.  
According to the Computing and Offloading Algorithm Based on Minimization of Energy Consumption, we found that [PERSON] et al. [25] introduced a computing offloading algorithm for distributed mobile cloud computing environments. This algorithm examined the node movement patterns and developed a network access prediction method based on subsequence tail matching. One of its primary advantages lies in its energy-saving capabilities, rendering it suitable for various scenarios. However, it fell short in overlooking the crucial aspects of time delay implementation and data processing efficiency, as well as the instability perceived by mobile devices.  
Lastly, we investigated the model and algorithm for the tradeoff between energy consumption and latency. The algorithm proposed by [PERSON] et al. [26] comprehensively addresses the factors that impact the algorithm's efficiency, incorporating the system's actual conditions to effectively offload task data. One significant advantage of this algorithm lies in its ability to conserve at least 50% of the energy compared to solutions that do not employ partial offloading techniques. However, a potential drawback is that it fails to take into account the energy consumption and time expenditure associated with data transmission. [PERSON] et al. [27] introduced a computing offloading algorithm that leverages specific information within the Internet of Things (IoT) environment to optimize the number of feedback transmissions from nodes to the Mobile Edge Computing (MEC) server. Employing Lyapunov optimization, the algorithm ensures energy equilibrium and stability in the overall system data throughput. This approach transformed the balancing challenge of energy consumption, data backlog, and time consumption into a knapsack problem. A key advantage of this algorithm is its ability to restrict the number of nodes requiring feedback transmission in a wireless sensor network comprising 5000 IoT devices to less than 60. However, a limitation lies in the fact that the solution to the knapsack problem does not guarantee a global optimal solution, potentially rendering the offloading decisions of this algorithm suboptimal in terms of efficiency.  
We can observe that decision-making for task offloading can be achieved from various perspectives, but in order to consider all aspects comprehensively, we chose to create our own energy consumption and execution time trade-off model for handling the offloading of large model inference tasks.