Ultra-dense networks are a key technology for 5G, characterized by having more cells than active users. However, as the density of small cell base stations (SBS) increases, ultra-dense networks introduce challenges in the exploration of resource allocation methods, particularly with respect to computational complexity and network scalability. To address these issues, this paper proposes a computation offloading and scheduling model for each SBS in ultra-dense networks. Specifically, in the proposed model, each SBS receives computing tasks from mobile devices and stores them in a queue buffer. Subsequently, each SBS first determines how many tasks need to be processed locally and whether offloading them is necessary. To tackle the complexity of this decision-making process, we employ game theory abstraction to reduce the large-scale state space and design a fast, adaptive computation offloading algorithm (FACOA). This algorithm enables each SBS to learn efficiently and quickly adapt to strategy changes made by other SBSs. Finally, simulation and experiments show that the proposed algorithm shorten the convergence time by 6 times.

With the rapid development of vehicle-to-everything communication technologies, many emerging compute-intensive in-vehicle applications have emerged. Vehicle edge computing (VEC) leverages the computational resources available at edge nodes to alleviate the strain on public network transmission and reduce task processing latency. However, the dynamic nature of the vehicle environment, the challenge of incentivizing vehicles to share idle resources, and the uncertainty surrounding the number of resources shared by vehicles present significant obstacles in designing task offloading and resource allocation methods for VEC systems. In this paper, we propose a hybrid offloading model wherein task vehicles can offload tasks to roadside units (RSUs) or other vehicles sharing resources. To maximize the benefits derived from task vehicles, RSUs, and shared resource vehicles, we first introduce an adaptive type selection algorithm (ALTS) for shared resource vehicles based on the multi-armed bandit (MAB) theory. Furthermore, we model the three-party interaction as a multi-stage Stackelberg game involving a computational resource lease contract. Experimental results demonstrate the superiority of the proposed ALTS algorithm over existing learning algorithms, thereby showcasing the effectiveness of the lease contract and the three-party transaction mechanism. Comparative experiments also reveal that integrating RSUs and idle vehicle resources offers better services compared to mechanisms relying solely on edge servers or shared resource vehicles.

Edge computing provides efficient and low-latency computing services for Internet of Things applications. Container virtualization technology is widely used as an indispensable key technology in edge computing. However, the creation of the container requires reading the corresponding image file. If the image file is not stored locally, it will take a lot of time to download, which increases the user’s extremely high service delay. Aiming at decreasing the download time of image files, we develop a two-stage optimization storage strategy of image files to decrease its download time based on edge computing. This strategy optimizes the image file placement in the initialization stage and the runtime stage, respectively. In the initialization stage, we propose a pseudo-polynomial time algorithm to filter all image files and select the image file combination, which best meets the capacity of the edge node for placement. In the runtime stage, we continue to optimize the local image repository based on the historical access records of the edge node. This operation can reduce the number of downloads of image files, thereby further reducing the user’s service delay. In addition, we created a real data set according to the service requirements and the structure of image files on the smart factory and the access records on the DockerHub. A large number of experiments are carried out based on the data set. Experimental results show that the two-stage optimization storage strategy can greatly reduce the download time of image files, thus reducing the service delay of edge nodes and improving the service quality of edge nodes.

Edge computing is envisioned as a prominent technology that provides high computing demand services by offloading computation-intensive and delay-sensitive task from mobile or Internet of Things(IoT) devices to nearby edge servers. However, more and more edge servers are deployed near the terminal devices. The uneven distribution of devices will lead to insufficient resource utilization of edge servers, so the social benefits of the edge computing system decrease. In this paper, we propose an effective task offloading strategy in the scenario of multi-users and multi-edge servers. Terminal devices broadcast task offloading request to edge servers, and the edge servers compete for tasks to improve their resource utilization. We use a non-cooperative game to describe the competition of edge servers, and model an optimization problem as the multi-edge servers resource allocation problem. We then design an iterative algorithm to solve the optimization problem and prove that the problem has an unique Nash equilibrium. Simulation results show that the proposed offloading strategy not only improves the resource utilization of edge servers, but also guarantees the demand of terminal devices.

Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.