Electronics

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19 pages, 8260 KiB

Open AccessArticle

Offloading Strategy Based on Graph Neural Reinforcement Learning in Mobile Edge Computing

by Tao Wang, Xue Ouyang, Dingmi Sun, Yimin Chen and Hao Li

Electronics 2024, 13(12), 2387; https://doi.org/10.3390/electronics13122387 - 18 Jun 2024

Cited by 1 | Viewed by 1128

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and [...] Read more.

In the mobile edge computing (MEC) architecture, base stations with computational capabilities are subject to service coverage limitations, and the mobility of devices leads to dynamic changes in their connections, directly impacting the offloading decisions of agents. The connections between base stations and mobile devices, as well as the connections between base stations themselves, are abstracted into an MEC structural diagram due to the difficulty of deep reinforcement learning (DRL) in capturing the complex relationships between nodes and their multi-order neighboring nodes in the graph; decisions solely generated by DRL have limitations. To address this issue, this study proposes a hierarchical mechanism strategy based on Graph Neural Reinforcement Learning (M-GNRL) under multiple constraints. Specifically, the MEC structural graph constructed with the current device as an observation point aggregates to learn node features, thus comprehensively considering the contextual information of nodes, and the learned graph information serves as the environment for deep reinforcement learning, effectively integrating a graph neural network (GNN) with DRL. In the M-GNRL strategy, edge features from GNN are introduced into the architecture of the DRL network to enhance the accuracy of agents’ decision-making. Additionally, this study proposes an updated algorithm to obtain graph data that change with observation points. Comparative experiments demonstrate that the M-GNRL algorithm outperforms other baseline algorithms in terms of system cost and convergence performance. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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20 pages, 683 KiB

Open AccessArticle

Multi-Queue-Based Offloading Strategy for Deep Reinforcement Learning Tasks

by Ruize Huang, Xiaolan Xie and Qiang Guo

Electronics 2024, 13(12), 2307; https://doi.org/10.3390/electronics13122307 - 13 Jun 2024

Cited by 1 | Viewed by 1084

Abstract

With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. [...] Read more.

With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users’ long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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18 pages, 2677 KiB

Open AccessArticle

Deep Reinforcement Learning-Based Task Offloading and Load Balancing for Vehicular Edge Computing

by Zhoupeng Wu, Zongpu Jia, Xiaoyan Pang and Shan Zhao

Electronics 2024, 13(8), 1511; https://doi.org/10.3390/electronics13081511 - 16 Apr 2024

Cited by 5 | Viewed by 2396

Abstract

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this [...] Read more.

Vehicular edge computing (VEC) effectively reduces the computational burden on vehicles by offloading tasks from resource-constrained vehicles to edge nodes. However, non-uniformly distributed vehicles offloading a large number of tasks cause load imbalance problems among edge nodes, resulting in performance degradation. In this paper, we propose a deep reinforcement learning-based decision scheme for task offloading and load balancing with the optimization objective of minimizing the system cost considering the split offloading of tasks and the load dynamics of edge nodes. First, we model the mutual interaction between mobile vehicles and Mobile Edge Computing (MEC) servers using a Markov decision process. Second, the optimal task-offloading and resource allocation decision is obtained by utilizing the twin delayed deep deterministic policy gradient algorithm (TD3), and server load balancing is achieved through edge collaboration using a server selection algorithm based on the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, we have conducted extensive simulation experiments and compared the results with several other baseline schemes. The proposed scheme can more effectively reduce the system cost and increase the system resource utilization. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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16 pages, 3321 KiB

Open AccessArticle

Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority

by Zhiguo Liu, Luxi Zhang, Lin Wang, Xiaoqi Dong and Junlin Rong

Electronics 2024, 13(4), 763; https://doi.org/10.3390/electronics13040763 - 15 Feb 2024

Viewed by 1509

Abstract

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each [...] Read more.

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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32 pages, 2268 KiB

Open AccessArticle

A Fast and Efficient Task Offloading Approach in Edge-Cloud Collaboration Environment

by Linyuan Liu, Haibin Zhu, Tianxing Wang and Mingwei Tang

Electronics 2024, 13(2), 313; https://doi.org/10.3390/electronics13020313 - 10 Jan 2024

Cited by 6 | Viewed by 2754

Abstract

Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to [...] Read more.

Edge-cloud collaboration fully utilizes the advantages of sufficient computing resources in cloud computing and the low latency of edge computing and better meets the needs of various Internet of Things (IoT) application scenarios. An important research challenge for edge-cloud collaboration is how to offload tasks to edge and cloud quickly and efficiently, taking into account different task characteristics, resource capabilities, and optimization objectives. To address the above challenge, we propose a fast and efficient task offloading approach in edge-cloud collaboration systems that can achieve a near-optimal solution with a low time overhead. First, it proposes an edge-cloud collaborative task offloading model that aims to minimize time delay and resource cost while ensuring the reliability requirements of the tasks. Then, it designs a novel Preprocessing-Based Task Offloading (PBTO) algorithm to quickly obtain a near-optimal solution to the Task Offloading problem in Edge-cloud Collaboration (TOEC) systems. Finally, we conducted extended simulation experiments to compare the proposed PBTO algorithm with the optimal method and two heuristic methods. The experimental results show that the total execution time of the proposed PBTO algorithm is reduced by 87.23%, while the total cost is increased by only 0.0004% compared to the optimal method. The two heuristics, although better than PBTO in terms of execution time, have much lower solution quality, e.g., their total costs are increased by 69.27% and 85.54%, respectively, compared to the optimal method. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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14 pages, 1075 KiB

Open AccessArticle

Joint Optimization of Resource Utilization, Latency and UAV Trajectory in the Power Information Acquisition System

by Yong Xiao, Xin Jin, Boyang Huang, Junhao Feng and Zhengmin Kong

Electronics 2023, 12(18), 3861; https://doi.org/10.3390/electronics12183861 - 12 Sep 2023

Viewed by 1225

Abstract

In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by [...] Read more.

In order to reduce the peak–valley difference of the power grid load, reasonably arrange users’ electricity consumption time and realize the intelligent management of the power grid, we construct a user electricity consumption information acquisition system based on unmanned aerial vehicles (UAVs) by using a sensor network. In order to improve the service quality of the system and reduce the system delay, this paper comprehensively considers the factors that affect the user’s electricity consumption information collection system, such as the UAV trajectory, the unloading decision of the data receiving point and so on. Therefore, this paper puts forward an effective iterative optimization algorithm for joint UAV trajectory and unloading decisions based on a deep Q network (DQN), in order to obtain the optimal UAV trajectory and unloading decision design, acquire the optimal solution to minimize the time delay of the monitoring system and maximize the service quality of the user electricity information collection system, thus ensuring the stable operation of the user electricity information collection system. In this paper, different complexity algorithms are used to solve this problem. Compared with the greedy algorithm, the proposed algorithm, CDQN, improves the system service quality by approximately

2 %

and reduces the system delay by approximately

16 %

, so that the user’s electricity consumption information can be analyzed and processed faster. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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22 pages, 2335 KiB

Open AccessArticle

Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology

by Yonghao Wang, Bo Li, Jiahao He, Jiaxing Dai, Yidong Liu and Yuxin Yang

Electronics 2023, 12(17), 3537; https://doi.org/10.3390/electronics12173537 - 22 Aug 2023

Cited by 6 | Viewed by 1709

Abstract

Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the [...] Read more.

Ubiquitous connectivity is envisaged for the space–air–ground-integrated network (SAGIN) of future communication to meet the needs of quality of service (QoS), green communication, and “dual carbon” targeting. However, the offloading and computation of massive latency-sensitive tasks dramatically increase the energy consumption of the network. To address these issues, we first propose a SAGIN architecture with energy-harvesting devices, where the base station (BS) is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power an unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighboring BSs is designed to fully utilize RE to reduce carbon emissions. Secondly, on the basis of task offloading decisions, the UAV trajectory, and the RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emissions. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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15 pages, 1680 KiB

Open AccessArticle

Smart Parking System Based on Edge-Cloud-Dew Computing Architecture

by Yuan-Chih Yu

Electronics 2023, 12(13), 2801; https://doi.org/10.3390/electronics12132801 - 25 Jun 2023

Cited by 9 | Viewed by 3132

Abstract

In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an [...] Read more.

In a smart parking system, the license plate recognition service controls the car’s entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot’s business will also be interrupted. Hence, we proposed an Edge-Cloud-Dew architecture for the mobile industry in order to tackle this critical problem. The architecture has an innovative design, including LAN-level deployment, Platform-as-a-Dew Service (PaaDS), the dew version of license plate recognition, and the dew type of machine learning model training. Based on these designs, the architecture presents many benefits, such as: (1) reduced maintenance and deployment issues and increased dew service reliability and sustainability; (2) effective release of the network constraint on cloud computing and increase in the horizontal and vertical scalability of the system; (3) enhancement of dew computing to resolve the heavy computing process problem; and (4) proposal of a dew type of machine learning training mechanism without requiring periodic retraining, but with acceptable accuracy. Finally, business owners can reduce their burdens when introducing machine learning technology. Our research goal is to make parking systems smarter in edge computing through the integration of cloud and dew architecture technology. Full article

(This article belongs to the Special Issue Emerging and New Technologies in Mobile Edge Computing Networks)

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