Dependent task unloading and service placement method based on HDQN

Through the HDQN method, the task offloading and service placement are optimized layer by layer, which solves the problems of low resource utilization and increased latency in multi-task dependency scenarios, and realizes efficient task scheduling and resource allocation, adapting to complex edge computing environments.

CN120491985APending Publication Date: 2025-08-15GUILIN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202510569260.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-01
Publication Date
2025-08-15

AI Technical Summary

Technical Problem

The prior art has failed to effectively combine the coupling relationship between task offloading and service placement, especially in multi-task dependency scenarios, resulting in low computing resource utilization and increased task completion delay.

Method used

The hierarchical deep reinforcement learning (HDQN) method is adopted to optimize task unloading and service placement in layers, the upper layer optimizes service placement, and the lower layer optimizes task unloading, and dynamically adjusts the strategy through dual Q network (DDQN) to achieve joint optimization.

Benefits of technology

Under the premise of considering task dependence, the task completion time is reduced, the resource utilization rate and overall system efficiency are improved, and the complex and changeable edge computing environment is adapted to complex and changeable edge computing environments.

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Abstract

Task unloading and service placement are two key technologies for reducing task completion time delay in the field of edge computing research, however, task unloading decisions and service placement decisions are optimized separately in existing research, the problem is simplified to a certain degree through the method, but the close relation and mutual influence between the task unloading decisions and the service placement decisions are neglected. In order to deal with the challenge, the invention provides a joint optimization scheme, a hierarchical deep reinforcement learning (HDQN) architecture is provided aiming at dual constraints of task dependence and service deployment in a complex scene, a joint optimization problem is decoupled into two decision levels, an upper layer carries out resource allocation through service placement optimization, and a lower layer carries out resource allocation through service deployment optimization. And the lower layer performs real-time task scheduling based on a dynamic environment. Through the layering mechanism, task unloading and resource constraint are effectively coordinated, and the dynamic change of the edge computing environment is adaptively coped with. Experimental results show that the method has excellent performance in the aspect of reducing task completion time.
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