To address the urgent practical challenge of service migration for connected autonomous vehicles (CAVs) in mobile edge computing (MEC), this study aims to maximize the task completion rate, particularly for safety-critical operations. Existing approaches often overlook the completion status of tasks with different priority levels during frequent service migrations and fail to co-optimize multiple constraints such as energy consumption, latency, and offloading cost. Consequently, it remains difficult to reliably complete highly urgent tasks in resource-constrained edge environments. To tackle this issue, we propose a comprehensive two-stage solution: the Improved Task Offloading and Service Migration (ITOSM) algorithm. In the first stage, a weighted evaluation model based on information entropy is constructed by integrating transmission time, execution time, and offloading cost. Tasks are offloaded to the edge server with either the highest or second-highest weighted sum according to their urgency level. In the second stage, service migration decisions are optimized using an improved binary particle swarm optimization (BPSO) algorithm with enhanced local search capability. Experimental results demonstrate that ITOSM outperforms existing methods, achieving up to 10.00% higher completion rates for Extremely Important Tasks (EITs) with strict deadlines. This improvement directly contributes to safer and more reliable CAV operations, highlighting the practical significance of this work for intelligent transportation systems.
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
A task-driven offloading algorithm based on Balanced Multi-Agent Deep Deterministic Policy Gradient (BMADDPG) that reduces average task processing latency by approximately 22.67% and decreases total system cost by at least 18.32% under high-load scenarios.
A distributed Multi-stage Adaptive Deferred Acceptance (MA-DA) algorithm is proposed that enables a stable and Pareto-optimal assignment of tasks to edge computing nodes (ECNs) and determines a reasonable task execution sequence and ensures the prioritized completion of delay-sensitive tasks.
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.
Zhixiu Yao, Yun Li, Qilie Liu et al.· IEEE Transactions on Service...· 0 citations
The research model not only can effectively improve the vehicle task processing efficiency and reduce the system overhead, but also shows strong adaptability and robustness, which has good prospects for practical applications.
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Tanmay Baidya, S. Moh· Italian National Conference...· 0 citations
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