Jul 2026· IEEE Transactions on Services Computing· Vol 19, pp. 2843-2856· 0 citations· 43 references
Abstract
Mobile edge computing (MEC) has accelerated the development of artificial intelligence and Internet of Things technologies, leading to the explosive growth of intelligent applications characterized by resource intensity and latency sensitivity, such as image processing and smart home. In practice, an application typically consists of multiple tasks with execution dependencies, where the output of some tasks serves as the input for specific others. Recently, the design of computation offloading methods for such execution-dependent tasks has received extensive research. However, computation offloading for execution-dependent tasks with service dependencies in resource-constrained multi-user, multi-edge-server cooperative MEC systems has not been thoroughly studied. In this paper, we formulate a cooperative computation offloading problem for dual-dependency tasks in multi-edge-server scenarios with limited service and computing resources, aiming to minimize the long-term average service delay for multiple users. To solve this problem, we propose a recurrent multi-agent reinforcement learning-based dual-dependency task offloading (RMA-DepO) algorithm, which enables users to communicate during training to explore and learn optimal joint task offloading and computing resource allocation strategies, and to make distributed offloading decisions at execution time. Simulation results demonstrate that the proposed RMA-DepO algorithm outperforms several baselines under different network settings, demonstrating its effectiveness in coordinating edge resources for cooperative computation of dual-dependency tasks.
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.
Jingjing Qu, Peiying Zhang, Ruixin Wang et al.· Information· 0 citations
The rapid growth of real-time and computation-intensive applications, such as face recognition, virtual reality, 3D
gaming, augmented reality, and intelligent transportation systems, has significantly increased the demand for efficient data
processing and low-latency services. However, mobile devices are constrained by limited computational capabilities and
battery capacity, making them unsuitable for executing heavy workloads locally. Mobile Edge Computing (MEC) has
emerged as a promising paradigm to offload computation tasks to nearby edge servers, thereby reducing latency and
improving service quality. Despite its advantages, task offloading in MEC environments remains challenging due to the
distributed nature of edge resources, energy constraints of end devices, and dynamic net- work conditions. Existing solutions
based on heuristic methods, genetic algorithms, NOMA-based techniques, and mobility-aware services often suffer from
high latency, excessive energy consumption, and task migration overhead. To address these limitations, this paper
investigates a reinforcement learning-based computation offloading strategy using an improved Deep Deterministic Policy
Gradient (IDDPG) algorithm. The proposed IDDPG approach enables decentralized decision-making by learning optimal
offloading policies from local observations, effectively balancing local execution and task offloading. By minimizing
computation costs, power consumption, and latency, the proposed method outperforms greedy offloading strategies and
demonstrates improved efficiency in dynamic MEC environments.
S. Alam, Sami Ud Din, Imtiaz Ali Shah et al.· International Journal of Inn...· 0 citations
This study jointly optimizes task offloading and system resource scheduling to minimize the long-term delay–energy cost of NOMA-MEC systems using a master-refined multi-agent proximal policy optimization algorithm.
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
A Mixed Integer Nonlinear Programming (MINLP) model with the objective of a weighted sum of long-term average task completion rate, total latency and energy consumption is established, which improves the task completion rate by 4% in high load scenarios and achieves a better balance between latency and energy consumption.