面向高性价比移动生成式服务的模型分配与资源分配联合优化
Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services
AI总结：
针对移动AIGC服务的高计算需求挑战，提出边缘赋能的服务系统，通过概率模型分配与启发式自适应资源配置算法，实现生成质量提升与延迟降低。
摘要：
人工智能生成内容（Artificial Intelligence Generated Content, AIGC）服务能够高效满足用户指定的内容创作需求，但其高计算量为大规模支持移动用户带来了诸多挑战。本文提出了一种基于边缘计算的AIGC服务供给系统设计，可将生成式模型的计算任务合理分配至边缘服务器，从而提升整体用户体验并降低内容生成延迟。具体而言，边缘服务器收到用户请求的任务提示后，会根据每类提示的特征动态分配适配的模型并调配计算资源，随后将生成的内容下发给用户。该系统的核心是提出的概率模型分配方法，它基于类别标签估算每条提示对应生成内容的质量得分。此外，我们还引入了一种启发式算法，可根据边缘侧每个生成式模型接收的不同任务请求，自适应配置生成步数与资源分配。仿真结果表明，与基准方法相比，所设计的系统可有效将生成内容质量最高提升4.7%，同时将响应延迟最高降低39.1%。
英文摘要：
Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale. In this paper, we present our design of an edge-enabled AIGC service provisioning system to properly assign computing tasks of generative models to edge servers, thereby improving overall user experience and reducing content generation latency. Specifically, once the edge server receives user requested task prompts, it dynamically assigns appropriate models and allocates computing resources based on features of each category of prompts. The generated contents are then delivered to users. The key to this system is a proposed probabilistic model assignment approach, which estimates the quality score of generated contents for each prompt based on category labels. Next, we introduce a heuristic algorithm that enables adaptive configuration of both generation steps and resource allocation, according to the various task requests received by each generative model on the edge.Simulation results demonstrate that the designed system can effectively enhance the quality of generated content by up to 4.7% while reducing response delay by up to 39.1% compared to benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
