Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks

• URL: http://arxiv.org/abs/2405.02972v1
• Date: Sun, 5 May 2024 15:31:47 GMT
• Title: Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
• Authors: Siyuan Li, Xi Lin, Hansong Xu, Kun Hua, Xiaomin Jin, Gaolei Li, Jianhua Li,
• Abstract summary: We propose a generative model-driven industrial AIGC collaborative edge learning framework. This framework aims to facilitate efficient few-shot learning by leveraging realistic sample synthesis and edge-based optimization capabilities.
• Score: 19.518346220904732
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Currently, the generative model has garnered considerable attention due to its application in addressing the challenge of scarcity of abnormal samples in the industrial Internet of Things (IoT). However, challenges persist regarding the edge deployment of generative models and the optimization of joint edge AI-generated content (AIGC) tasks. In this paper, we focus on the edge optimization of AIGC task execution and propose GMEL, a generative model-driven industrial AIGC collaborative edge learning framework. This framework aims to facilitate efficient few-shot learning by leveraging realistic sample synthesis and edge-based optimization capabilities. First, a multi-task AIGC computational offloading model is presented to ensure the efficient execution of heterogeneous AIGC tasks on edge servers. Then, we propose an attention-enhanced multi-agent reinforcement learning (AMARL) algorithm aimed at refining offloading policies within the IoT system, thereby supporting generative model-driven edge learning. Finally, our experimental results demonstrate the effectiveness of the proposed algorithm in optimizing the total system latency of the edge-based AIGC task completion.

Related papers

• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks [15.958822667638405]
  The scarcity of available resources on the edge pose significant challenges in deploying generative AI models. We present a collaborative edge-cloud framework aiming to properly manage generative AI model deployment on the edge.
  arXiv  Detail & Related papers  (2024-09-09T03:17:28Z)
• Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II [52.083337333478674]
  This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW) The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL.
  arXiv  Detail & Related papers  (2024-07-18T02:46:06Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Offloading and Quality Control for AI Generated Content Services in 6G Mobile Edge Computing Networks [18.723955271182007]
  This paper proposes a joint optimization algorithm for offloading decisions, computation time, and diffusion steps of the diffusion models in the reverse diffusion stage. Experimental results conclusively demonstrate that the proposed algorithm achieves superior joint optimization performance compared to the baselines.
  arXiv  Detail & Related papers  (2023-12-11T08:36:27Z)
• Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
  In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks. We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
  arXiv  Detail & Related papers  (2023-01-09T09:30:23Z)
• Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model [4.273341750394231]
  We formulate the problem of jointly designing the UAV's trajectory and selecting cluster heads in the Internet-of-Things network. We propose a novel deep reinforcement learning (DRL) with a sequential model strategy that can effectively learn the policy represented by a sequence-to-sequence neural network. Through extensive simulations, the obtained results show that the proposed DRL method can find the UAV's trajectory that requires much less energy consumption.
  arXiv  Detail & Related papers  (2021-12-01T07:59:53Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)
• Sparse Optimization for Green Edge AI Inference [28.048770388766716]
  We present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference. By exploiting the inherent connections between the set of task selection and group sparsity transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. We establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm.
  arXiv  Detail & Related papers  (2020-02-24T05:21:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.