Mobile Edge Computing (MEC) is a promising paradigm that deploys cloud-based services at the network edge to process computationally intensive tasks with low service delay. However, optimizing MEC systems remains challenging due to the random task arrival patterns of mobile edge devices and the necessity for dynamic service placement. Consequently, decisions regarding service placement significantly influence task offloading choices, and vice versa. This interdependence has been relatively overlooked, limiting the overall performance of MEC systems. To address this challenge, we formulate the cooperative service placement and task offloading problem as a Partially Observable Markov Decision Process (POMDP). We propose a simple yet efficient approach named HAC-LSTM, which integrates Long Short-Term Memory (LSTM) networks with a hybrid actor-critic (HAC) algorithm. HAC-LSTM employs an LSTM-based state encoder to effectively extract hidden information and manage partial future information of the MEC system. Subsequently, the HAC algorithm leverages hybrid actors—a discrete actor for service placement and a continuous actor for task offloading—to make optimized joint decisions. We evaluate our approach through extensive experiments using real-world mobility datasets and varying key parameters. The results demonstrate that HACLSTM outperforms baseline algorithms by significantly minimizing average delay, thereby enhancing the overall efficiency and responsiveness of MEC systems.

Autonomous vehicles (AVs) are expected to handle complex tasks in dynamic environments. However, the limited onboard computing capacity of AVs and heterogeneous tasks under strict delay constraints bring performance bottlenecks. To efficiently complete the onboard tasks, we formulate a dynamic Vehicle-to-Vehicle Task Offloading Problem (V2TOP) in Vehicular Ad Hoc Network (VANET), aiming to jointly minimize end-to-end task delay and maximize the average speed of connected vehicles subject to safety constraints. Then, we design a Mobility-Aware Distributed Primal-Dual Offloading Algorithm (MDPDOA) to optimize global resource utilization, explicitly modeling both task diversity and heterogeneous computing capabilities of participating vehicles. Furthermore, a dynamic triggering mechanism is introduced to timely re-allocation in response to topology or resource variations, guaranteeing consistent performance in realtime vehicular networks. Simulation results demonstrate that the V2V collaboration policy efficiently utilizes vehicle computing resources and enhances driving safety.

Mobile Edge Computing (MEC) plays an increasingly important role in the rapidly increasing mobile applications by providing high-quality computing services. The majority of current research has focused on designing efficient computing task offloading schemes to ensure the effectiveness of the MEC system. However, the configuration and resource management of the MEC system, which are crucial for its scattered feature, have not received due attention. This paper investigates the configuration and computation resource management problem for the MEC system by formulating a profit maximization problem. To address this problem, we first analyze the relationship among mobile users' offloading decisions, the configuration and computation management of the MEC system, and the service quality. Then, we design an optimal configuration and computation management scheme of the MEC system, which can not only maintain the efficiency of computing processes but also make a good trade-off between the profitability and the service quality. In such a way, the total expected profit of the MEC system can be maximized. Numerical evaluations show that the proposed optimal configuration and computation management scheme can efficiently improve the total profit of the MEC system.

As wireless communication and Internet of Things (IoT) technologies advance, edge computing brings computing and storage capabilities closer to users, providing low-latency and high-quality services. However, the limited resources of edge servers and the diverse resource demands of heterogeneous tasks may result in high latency and poor energy efficiency. To address this challenge, we investigate a Docker-based resource management framework for edge servers, involving the allocation of heterogeneous tasks and the configuration of the Docker container clusters. Then, we formulate the problem as one of minimizing energy consumption and task latency, concerning task allocation and server resource management, subject to server resource constraints. To solve this problem, we propose an effective task assignment and resource management strategy, which is developed based on convex optimization theory, aiming to achieve an approximate optimal solution. Simulation results demonstrate that, compared to other algorithms, the proposed algorithm significantly reduces server operating power consumption and task response latency.

Traditional workload-based task scheduling has been well studied in edge computing. However, computing tasks with different types may have different sensitivities of processing latency to memory and CPU resources, which makes it challenging to design an efficient task scheduling strategy for edge servers with different configurations to guarantee the quality of service (QoS). To address this challenge, we first conduct the extensive testing on the processing latency of memory and CPU resources in the multi-container environment, and formulate the task scheduling problem as a long-term QoS optimization problem. Secondly, we transform the original problem into a task scheduling sub-problem for each time slot based on Lyapunov optimization theory, and propose an online task scheduling strategy using Genetic Simulated Annealing Algorithm, which can obtain an approximate optimal solution that minimizes makespan while ensuring the queue stability. Thirdly, based on matching theory, we use the backlog tasks information to update the approximate solution and further minimize makespan in real-time. Finally, the simulation results show that the proposed scheme can minimize the makespan comparing to other schemes.

Integrating the advantages of both the edge and the cloud, the edge-cloud computing system emerges to provide high-quality computing services for mobile users. To improve system efficiency, we investigate a hybrid mode of resource collaboration and expansion for the edge-cloud computing system, in which edge servers not only can collaborate with the cloud by purchasing high-priority computation resources temporarily but also can expand their local computation resources permanently. In such a way, the edge server can maximize its long-term profit by making a trade-off between the purchasing cost and the expanding cost. By formulating the resource management problem as a long-term profit maximization one, we first analyze the relationships among the expected minimal purchasing cost, the computation delay, and the available computation resources. Then, we design an efficient resource reserving and expanding scheme to determine the optimal expected amounts of reserving resources and expansion resources. Next, we propose an efficient real-time resource purchasing scheme to obtain the optimal amount of real-time purchasing resources dynamically. Finally, simulation results show that the proposed efficient resource collaboration and expanding scheme can maximize the long-term profit while guaranteeing the computation delay.

As a high-quality computing service provider for mobile users, the task offloading scheme for existing mobile edge computing (MEC) has been widely studied in recent years. However, how to establish an efficient MEC system for Edge Service Providers (ESPs), especially building on the existing Base Stations (BSs) of Mobile Network Operators (MNOs), has not been well studied. In this paper, we formulate the BS rental problem for MNOs and the edge server deployment problem for ESPs as an auction-based profit optimization problem. Firstly, we model the competition for BSs among the ESPs as a sequential first-price auction and the edge server deployment problem as a profit maximization problem for each ESP. Then, we prove that both of these problems are convex and propose an efficient auction-based edge sever deployment scheme for ESPs. Finally, extensive simulations are conducted to show the effectiveness of the proposed edge server deployment scheme for ESPs.

With the explosive growth of mobile applications, the development of mobile edge computing (MEC) has been greatly promoted since it can ably improve the quality of service for mobile applications by providing low latency and high-quality computation services. Most existing works focus on improving the efficiency of MEC with an assumption that the MEC servers have already been deployed. However, without appropriate deployment of MEC servers, the profitability of the MEC system can be significantly restrained, which hinders the rapid promotion of the MEC. To address this issue, we formulate an MEC server deployment problem for the MEC operator as a revenue maximization problem. Firstly, we model and analyze the various factors that affect the revenue. Secondly, we formulate a revenue maximization problem, which is NP-hard, but it is proved to be convex with respect to the total available computation units. Based on this feature, we propose a three-layer optimization algorithm, named EDM, in which the location, the deployed computation units, and the wholesaled computation resources are determined gradually, to maximize the total revenue. Experimental results demonstrate that the proposed EDM algorithm has significant advantages on revenue improvement compared to competitive benchmarks.

Owing to the ability to provide better services for latency-intensive tasks than the cloud paradigm, Mobile Edge Computing (MEC) has attracted increasing attention recently. However, due to limited resources, MEC cannot handle large amount of computation tasks as the cloud paradigm. Most of the existing works design offload strategies for MEC by sharing the responsibility of total computation tasks with the cloud to provide more services, but neglecting the fact that the profit can be shared when sharing responsibility, which decreases the profit. To address this issue, we propose a trade collaboration framework for the MEC and the cloud paradigm, where the MEC can purchase resources from the cloud paradigm to process computation tasks under latency constraints. Without accurate information about required resources, this paper has designed an efficient resource trade scheme for the MEC to achieve their optimal purchased resources, such that the expected profit of the MEC can be maximized. Simulation results show that the proposed scheme can maximize the profit of the MEC and guarantee latency requirements.

The Internet of Vehicles (IoV) is composed of road side units (RSUs) nodes and vehicle nodes. Because the limited bandwidth of RSUs can not satisfy massive requests from vehicle nodes, offloading technology is proposed. Generally, spare vehicle nodes (SVs) with redundant resources are motivated to cache data from RSUs. However, due to the selfishness, the participation of SVs is generally low. Hence, an effective incentive mechanism to motivate SVs to offload is important. There are two problems with current incentive mechanisms: 1) all of them ignore loss aversion, which is the act of preferring avoidance of loss over acquiring equivalent gains. This act can lead to deviation in the final decision and 2) no attention is paid to the critical effects that initial allocation of property have on the final resource allocation. However, researches on behavior economics have found that loss aversion exists and the final resource allocation is affected by the initial configuration. Therefore, we propose an incentive mechanism called reverse auction based on endowment effect (RABEE). To the first problem, we introduce the endowment effect from behavior economics, and propose a term called endowment compensation for increasing the participation rate of SVs. For the second problem, by changing the initial allocation of endowment compensation, we illustrate through theoretical analysis and simulations the significance the initial configuration has in terms of the final resource allocation. Simulations show that RABEE increases the average utility of SVs by 6.8% compared with the conventional scheme, and it also improves participation rate by 2%.