Ephemera: Accelerating I/O-Intensive Serverless Workloads with a Harvested In-memory File System

作者：Lingxiao Jin, Zinuo Cai, Haoxin Wang, Zongpu Zhang, Ruhui Ma, Haibing Guan, Yuan Liu, Rajkumar Buyya · 发表于：ACM Transactions on Architecture and Code Optimization · 年份：2025 · DOI：10.1145/3747846 · 被引用次数：4 · 研究领域：Advanced Data Storage Technologies、Cloud Computing and Resource Management、Cloud Data Security Solutions

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, opt...