###### Abstract  
In IoT (Internet of Things) edge computing, task offloading can lead to additional transmission delays and transmission energy consumption. To reduce the cost of resources required for task offloading and improve the utilization of server resources, in this paper, we model the task offloading problem as a joint decision making problem for cost minimization, which integrates the processing latency, processing energy consumption, and the task throw rate of latency-sensitive tasks. The Online Predictive Offloading (OPO) algorithm based on Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks is proposed to solve the above task offloading decision problem. In the training phase of the model, this algorithm predicts the load of the edge server in real-time with the LSTM algorithm, which effectively improves the convergence accuracy and convergence speed of the DRL algorithm in the offloading process. In the testing phase, the LSTM network is used to predict the characteristics of the next task, and then the computational resources are allocated for the task in advance by the DRL decision model, thus further reducing the response delay of the task and enhancing the offloading performance of the system. The experimental evaluation shows that this algorithm can effectively reduce the average latency by 6.25%, the offloading cost by 25.6%, and the task throw rate by 31.7%. computational offloading; resource allocation; prediction; DRL; LSTM 20221430. [[https://doi.org/10.3390/614020030](https://doi.org/10.3390/614020030)]([https://doi.org/10.3390/614020030](https://doi.org/10.3390/614020030))  
To Appelt  
At present, researchers have conducted a lot of research on the offloading decision problem, such as seeking the optimal solution after formulating the problem based on traditional algorithms [6; 7; 8; 9; 10], maximizing the reward to make appropriate decision actions based on reinforcement learning algorithms [11; 12; 13; 14], etc. However, the above algorithms do not take the dynamic and predictable nature of the task during task offloading into account, thus they may produce unreasonable offloading decisions, resulting in degradation of the task offloading performance. Traditional task offloading methods can only optimize task offloading and resource allocation statically. Although deep reinforcement learning methods can meet the requirement of current IoT dynamic task computing, most algorithms only make the model converge faster and achieve better results by improving the method during model training, while ignoring the optimization of the task inference testing process, which still brings relatively high response delays in real scenarios. In other words, the generation and processing of tasks in real scenarios are more correlated with time series [15; 16], so the prediction can be made by characterizing the tasks. The load of each server can be predicted by collecting the historical data of the system [17; 18], and using the prediction method can efficiently allocate resources and make offload decisions for tasks with limited resources, which can effectively reduce the response delay of tasks when making the decision. To address the above problems and challenges encountered in task offloading, this paper proposes an Online Predictive Offloading (OPO) algorithm. The algorithm combines Long Short-Term Memory (LSTM) and deep reinforcement learning (DRL) to predict task dynamic information in real-time, based on the observed edge network condition and the server load. This algorithm aims to make offloading decisions by taking into account the task processing delays, the task tolerance delays, and the task computation energy consumption, and to avoid causing network congestion and server overloading, thus minimizing the task dropped rate and reduced the computational cost of the task. The main contributions of this paper are summarized as follows. * Modeling the problem of computing offloading in a multi-edge, multi-device computing scenario as a nonlinear optimization problem.