Proactive caching in 6 G cloud-edge collaboration scenarios, intelligently and periodically updating the cached contents, can either alleviate the traffic congestion of backhaul link and edge cooperative link or bring multimedia services to mobile users. To further improve the network performance of 6 G cloud-edge, we consider the issue of multi-objective joint optimization, i.e. , maximizing edge hit ratio while minimizing content access latency and traffic cost. To solve this complex problem, we focus on the distributed deep reinforcement learning (DRL)-based method for proactive caching, including content prediction and content decision-making. Specifically, since the prior information of user requests is seldom available practically in the current time period, a novel method named temporal convolution sequence network (TCSN) based on the temporal convolution network (TCN) and attention model is used to improve the accuracy of content prediction. Furthermore, according to the value of content prediction, the distributional deep Q network (DDQN) seeks to build a distribution model on returns to optimize the policy of content decision-making. The generative adversarial network (GAN) is adapted in a distributed fashion, emphasizing learning the data distribution and generating compelling data across multiple nodes. In addition, the prioritized experience replay (PER) is helpful to learn from the most effective sample. So we propose a multivariate fusion algorithm called PG-DDQN. Finally, faced with such a complex scenario, a distributed learning architecture, i.e. , multi-agent learning architecture is efficiently used to learn DRL-based methods in a manner of centralized training and distributed inference. The experiments prove that our proposal achieves satisfactory performance in terms of edge hit ratio, traffic cost and content access latency.

Energy distribution networks represent crucial infrastructures for modern society, and various simulation tools have been widely used by energy suppliers to manage these intricate networks. However, simulation calculations include a large number of fluid control equations, and computational overhead limits the performance of simulation software. This paper proposes a universal parallel simulation framework for energy pipeline networks that takes advantages of data parallelism and computational independence between network elements. A non-pipe model of an energy supply network is optimized, and the input and output of the network model in the proposed framework are modified, which can reduce the development burden during the numerical computations of the pipeline network and weaken the computational correlation between different simulated components. In addition, independent computations can be performed concurrently through periodic data exchange procedures between component instances, improving the parallelism and efficiency of simulation computations. Further, a parallel water pipelines network simulation computing paradigm based on a heterogeneous computer hardware architecture is used to evaluate the proposed framework’s performance. A series of tests are conducted to verify the accuracy of the proposed framework, and simulation errors of less than 5% are achieved. The results of multi-threaded simulation experiments have demonstrated the feasibility of the proposed framework in a parallel computing approach. Moreover, an Advanced Micro Devices (AMD) Deep Computing Unit (DCU)-parallel program is implemented into a water supply network simulation system; the computational efficiency of this system is compared with that of its serial counterpart. The experimental results show that the proposed framework is appropriate for high-performance computer architectures, and the 18x speed-up ratio demonstrates that the parallel program based on the proposed universal framework outperforms the serial program. That provides the basis for the application of pipe network simulation on high-performance computers.

Tracking continuous objects in the ocean, such as oil spills, is a significant challenge, and one that Underwater Acoustic Sensor Networks (UASNs) are well-suited to address. While prevalent approaches often employ complex, multi-phase processes to delineate the boundaries of these objects, enabling effective monitoring, they may not be ideally suited for time-sensitive situations, where a rapid response often outweighs the need for extreme precision. To address this gap, we introduce a streamlined boundary tracking algorithm, the Centroid Computation of Feasible Region for Continuous Object Tracking (CCFR-COT), intended for Unmanned Surface Vessel (USV)-assisted UASNs. This algorithm utilizes a fundamental full binary tree-structured network configuration along with two of its variations, designed to handle cases where continuous objects disperse in either free space or two semi-infinite spaces. The chosen network configuration aids in the selection of boundary mapping cells, thereby sketching a coarse-grained feasible region that envelops the actual boundary. Sequentially visiting these cells, the USV systematically constructs and deconstructs convex hulls of sensor nodes to progressively reduce location uncertainty within the feasible region. Only the centroid of the final feasible region in each boundary mapping cell is output for boundary fitting. Both experimental and simulated results validate the streamlined design of the CCFR-COT, which ensures timely tracking results and provides a balance between rapid response and tracking accuracy.

Frequent activities in marine energy exploration and transportation have led to the ongoing presence of continuous objects, such as oil spills and radioactive waste, in the ocean. This article focuses on enhancing the understanding of these objects’ boundaries for accurate assessment of their shape, coverage, and evolution. We introduce a continuous object tracking algorithm named JGL-COT, based on joint global-local binary tree topological transformations and specifically designed for underwater acoustic sensor networks. The contribution of JGL-COT lies in its ability to leverage the correlation between the morphologies of a continuous object's boundaries over time, alternating between global and local binary tree topological transformations. When the present boundary features a strong resemblance to its previous form, JGL-COT switches to a local transformation by establishing a semi-infinite region. Otherwise, it transitions to a global transformation. Following this, JGL-COT chooses a group of binary tree-structured cells for boundary mapping, creating virtual boundary nodes as sampling points for boundary fitting. Building upon graph theory, we derive the lower bound on the effectiveness and time complexity of the proposed joint global and local binary tree topological transformations when applied to object boundary tracking. Experiments in both realistic and simulated settings confirm that JGL-COT provides highly accurate tracking and significantly reduces network energy consumption.

The SCHISM model is widely used for ocean numerical simulations, but its computational efficiency is constrained by the substantial resources it requires. To enhance its performance, this study develops GPU–SCHISM, a GPU-accelerated parallel version of SCHISM using the CUDA Fortran framework, and this study evaluates its acceleration performance on a single GPU-enabled node. The research results demonstrate that the GPU–SCHISM model achieves computational acceleration while maintaining high simulation accuracy. For small-scale classical experiments, a single GPU improves the efficiency of the Jacobi solver—identified as a performance hotspot—by 3.06 times and accelerates the overall model by 1.18 times. However, increasing the number of GPUs reduces the computational workload per GPU, which hinders further acceleration improvements. The GPU is particularly effective for performing higher-resolution calculations, leveraging its computational power. For large-scale experiments with 2,560,000 grid points, the GPU speedup ratio is 35.13; however, CPU has more advantages in small-scale calculations. Moreover, a comparison between CUDA and OpenACC-based GPU acceleration shows that CUDA outperforms OpenACC under all experimental conditions. This study marks the first successful GPU acceleration of the SCHISM model within the CUDA Fortran framework, laying a preliminary foundation for lightweight GPU-accelerated parallel processing in ocean numerical simulations.