In the mobile edge computing (MEC) scenario, numerous complex applications consist of dependent tasks. Efficient offloading of these applications is essential for reducing latency and minimizing terminal energy consumption. However, existing studies typically employ a decoupled decision-making paradigm, where the task scheduling sequence is predetermined before independently determining the offloading location. This approach separates the scheduling and offloading processes, significantly limiting the exploration of the strategy space and hindering the identification of the global optimal solution. To address these limitations, we propose a joint scheduling and offloading algorithm based on Proximal Policy Optimization (JSO-PPO). We construct an integrated Markov Decision Process (MDP) model and introduce an action masking mechanism, unifying task scheduling and location offloading into a single end-to-end decision. Furthermore, to enhance the algorithm’s performance and stability, the JSO-PPO integrates Deep Dense Architectures in Reinforcement Learning (D2RL) for superior state representation and introduces an adaptive penalty term into its objective function for more stable convergence. Simulation results demonstrate that, compared to multiple existing algorithms, the proposed JSO-PPO achieves significant improvements in minimizing the weighted sum of application finish latency and user equipment energy consumption. These findings validate the efficiency and robustness of our joint optimization paradigm in dynamic and complex edge environments.

The proliferation of edge Internet of Things (IoT) devices intensifies congestion challenges in edge IoT networks. Adjusting edge router buffer sizes is a common and effective solution to reduce queuing delay and mitigate congestion. However, designing an optimal buffer sizing strategy that effectively balances low queuing delay with high throughput and minimal packet loss remains a key challenge. To address this, this paper proposes DRL-ABS, an adaptive buffer sizing method based on deep reinforcement learning. DRL-ABS employs a Dueling DQN model to implement its strategy, effectively controlling buffer size to reduce queuing delay. First, we introduce a novel congestion determination mechanism to enhance decision stability against transient traffic fluctuations. Then, we design a reward function based on the congestion status to balance packet loss rate and throughput. Finally, we construct a NS3-PyTorch joint simulation framework to bridge the gap between NS-3 and AI algorithms. Simulation results demonstrate that, compared to static buffer sizing, traditional adaptive buffer sizing, and AI-dreiven adaptive buffer sizing, DRL-ABS significantly reduces the average buffer size, average queuing delay, and jitter while maintaining a high average throughput and a comparable total packet loss rate. Furthermore, DRL-ABS exhibits good stability, maintaining a high average buffer utilization rate across different congestion control systems.

Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

With the rapid growth of the encrypted network traffic, the identification to it becomes a hot topic in information security. Since the existing methods have difficulties in identifying the application which the encrypted traffic belongs to, a new encrypted traffic identification scheme is proposed in this paper. The proposed scheme has two levels. In the first level, the entropy and estimation of Monte Carlo π value as features are used to identify the encrypted traffic by C4.5 decision tree. In the second level, the application types are distinguished from the encrypted traffic selected above. First, the variational automatic encoder is used to extract the layer features, which is combined with the frequently-used stream features. Meanwhile, the mutual information is used to reduce the dimensionality of the combination features. Finally, the random forest classifier is used to obtain the optimal result. Compared with the existing methods, the experimental results show that the proposed scheme not only has faster convergence speed but also achieves better performance in the recognition accuracy, recall rate, and F1-Measure, which is higher than 97%.