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Optimizing Resource Allocation and Request Routing for AI-Generated Content (AIGC) Services in Mobile Edge Networks With Cell Coupling | IEEE Journals & Magazine | IEEE Xplore

Optimizing Resource Allocation and Request Routing for AI-Generated Content (AIGC) Services in Mobile Edge Networks With Cell Coupling


Abstract:

In this paper, we investigate the deployment and service of pre-trained foundation models (PFMs) in mobile edge networks with cell coupling. We formulate a joint resource...Show More

Abstract:

In this paper, we investigate the deployment and service of pre-trained foundation models (PFMs) in mobile edge networks with cell coupling. We formulate a joint resource allocation and request routing optimization problem (RARP) to achieve a trade-off between the accuracy loss and cost of artificial intelligence-generated content (AIGC). For problem solving, we propose an alternating optimization algorithm (AOA) that decomposes RARP into two sub-problems and iteratively optimizes them. Specifically, for the first sub-problem, we reformulate it as a linear programming problem and use the off-the-shelf optimization solver to solve it. For the other sub-problem, we propose a deep reinforcement learning based algorithm to optimize the deployment to PFMs. Performance evaluations validate the efficiency of AOA.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 73, Issue: 11, November 2024)
Page(s): 17911 - 17916
Date of Publication: 11 July 2024

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