Serverless computing is increasingly adopted for its ability to manage complex, event-driven workloads without the need for infrastructure provisioning. However, traditional resource allocation in serverless platforms couples CPU and memory, which may not be optimal for all functions. Existing decoupling approaches, while offering some flexibility, are not designed to handle the vast configuration space and complexity of serverless workflows. In this paper, we propose AARC, an innovative, automated framework that decouples CPU and memory resources to provide more flexible and efficient provisioning for serverless workloads. AARC is composed of two key components: Graph-Centric Scheduler, which identifies critical paths in workflows, and Priority Configurator, which applies priority scheduling techniques to optimize resource allocation. Our experimental evaluation demonstrates that AARC achieves substantial improvements over state-of-the-art methods, with total search time reductions of 85.8% and 89.6%, and cost savings of 49.6% and 61.7%, respectively, while maintaining SLO compliance.

翻译：无服务器计算因其无需基础设施配置即可管理复杂事件驱动工作负载的能力而日益普及。然而，传统无服务器平台中的资源分配将CPU与内存耦合，这可能并非对所有函数都是最优的。现有的解耦方法虽然提供了一定的灵活性，但并未设计用于处理无服务器工作流庞大的配置空间与复杂性。本文提出AARC，一种创新的自动化框架，通过解耦CPU与内存资源，为无服务器工作负载提供更灵活高效的资源配置。AARC包含两个核心组件：以图为中心的调度器，用于识别工作流中的关键路径；以及优先级配置器，应用优先级调度技术以优化资源分配。我们的实验评估表明，AARC相较于现有先进方法取得了显著改进，在满足服务等级目标的同时，总搜索时间分别降低了85.8%和89.6%，成本分别节省了49.6%和61.7%。