Multi-agent deep reinforcement learning edge task scheduling algorithm with migratable service environment

doi:10.1504/IJSNET.2026.151234

Multi-agent deep reinforcement learning edge task scheduling algorithm with migratable service environment

2026-01-01

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DOI：10.1504/IJSNET.2026.151234

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The multi-edge collaborative computing approach stores task service environments in edge nodes closer to end-users and uses multi-edge networks for collaborative offloading, overcoming long transmission distances and slow response times in traditional cloud computing. However, existing fixed-storage task offloading methods cannot dynamically schedule service environments for regional task preferences, leading to unbalanced multi-edge loads and reduced execution efficiency. We propose a container-based migratable service environment scheduling model to dynamically meet regional service demands via real-time task and environment scheduling. To address process coupling and storage replacement issues, we integrate offloading, environment migration, and content replacement into a unified scheduling action using reinforcement learning. Our improved multi-agent deep RL algorithm employs centralised training with distributed execution and an attention mechanism to optimise policy learning. Simulations show the approach enhances multi-edge load balancing and reduces average task delay.

Keywords:

multi-agent reinforcement learning

edge computing

task scheduling

migratable service environment

Multi-agent deep reinforcement learning edge task scheduling algorithm with migratable service environment

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