A method for dual-timescale task offloading and resource allocation in a multi-timeslot MEC system

By employing a dual-timescale method and the D3QN algorithm based on deep reinforcement learning in a multi-timeslot MEC system, the problem of high computational complexity in task unloading and resource allocation is solved, and the optimal solution is obtained quickly.

CN117354934BActive Publication Date: 2026-05-26SOUTHEAST UNIV
2 Cites 0 Cited by

Patent Information

Application Number
CN202311385000.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-10-24
Publication Date
2026-05-26
Estimated Expiration
2043-10-24

AI Technical Summary

Technical Problem

In multi-timeslot mobile edge computing systems, existing technologies struggle to effectively solve the mixed-integer nonlinear programming problem of task offloading and resource allocation, resulting in high computational complexity and slow solution speed. In particular, it is difficult to compute an optimal solution within a reasonable timeframe when dealing with large-scale base stations and users.

Method used

A dual-timescale approach is adopted, which decouples the task offloading and resource allocation problems through the Lyapunov method, and combines the D3QN algorithm of deep reinforcement learning to learn server mode selection and user-server association decisions, thereby reducing computational complexity.

Benefits of technology

It effectively reduces the computational complexity of multi-timeslot systems, improves the solution speed, and ensures that the optimal solution is obtained within the effective time.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

This invention relates to a dual-timescale task offloading and resource allocation method for multi-timeslot MEC systems, comprising: establishing a long-term average energy consumption minimization model for a multi-user, multi-server MEC network; solving the long-term average energy consumption minimization model at two timescales; on the one hand, at a smaller timescale, decoupling the long-term stochastic task offloading and resource allocation problem into a series of online optimization deterministic problems by invoking the Lyapunov method, obtaining closed-form solutions for offloading decisions and resource allocation at each timeslot; on the other hand, at a larger timescale, formulating the server active / sleep mode selection and user-server association decision problem as a constrained Markov decision process, and employing a bi-adversarial deep Q-network (D3QN) from deep reinforcement learning to learn the mode selection and association decisions. Compared with existing technologies, this invention can effectively reduce the computational complexity of multi-timeslot systems and improve the solution speed.
Need to check novelty before this filing date? Find Prior Art