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.

Serverless computing has gained popularity for its ability to shift the burden of server management from developers to cloud providers, which allows providers to exercise greater control over resource management, optimizing configurations to enhance efficiency and performance. The diversity of serverless computing tasks, from short-lived, event-driven tasks to more complex workloads, highlights the growing importance of efficient file I/O performance for I/O-intensive workloads, yet effectively handling ephemeral storage for I/O-intensive tasks remains a challenge. Traditional file system approaches often introduce substantial latency and fail to fully leverage available memory resources within the execution environment, limiting performance and efficiency. Our work stems from the observation of the under-utilization of memory resources in serverless computing platforms and the potential efficiency improvement of I/O operations using an in-memory file system. Based on this observation, we propose Ephemera , a system designed to enhance ephemeral storage efficiency and memory utilization. Ephemera satisfies three design goals: transparent memory I/O integration , heterogeneous tasks resource synergy , and harmonized cluster workload orchestration . Ephemera integrates three components: the Runtime Daemon, responsible for managing a container’s in-memory file system; the Tenant Manager, facilitating memory configuration sharing across containers; and the Cluster Controller, optimizing workload balancing. Our experiments demonstrate that Ephemera significantly improves performance for I/O-intensive tasks compared to traditional file systems. Specifically, Ephemera decreases I/O processing time by 50% on average and reduces latency by up to 95.73% in certain scenarios with negligible overhead.