发表机构
Politecnico di Milano（米兰理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Ermes是一个将有状态管理集成到FaaS的分布式平台，通过联合映射集合与函数及细粒度一致性选择，减少边缘到云连续体中的延迟。
AI 中文摘要
函数即服务（FaaS）是一种被广泛采用的范式，用于简化跨边缘到云连续体的应用部署。然而，其无状态特性迫使函数从外部（通常是云中心）的数据存储中检索状态，从而重新引入了边缘计算旨在消除的延迟。这一问题因位置无关的调度器和僵化的一刀切一致性模型而进一步加剧，这些模型未能捕捉边缘应用的多样化需求。在本文中，我们提出了Ermes，一个将状态管理原生集成到FaaS范式中的分布式平台，使得计算工作负载和应用状态能够在边缘到云连续体上联合分布。Ermes将应用状态组织为称为集合的逻辑单元，并采用一种分布式协调算法，该算法将集合和函数联合映射到可用节点上，以最小化客户端感知的延迟。此外，它支持细粒度的复制和每集合的一致性级别，范围从顺序一致性到最终一致性，让开发者自行选择如何权衡一致性与性能。实验评估表明，Ermes能迅速将远程状态访问转变为本地访问，并在工作负载增长以及客户端在边缘移动时维持低延迟。
英文摘要
Function-as-a-Service (FaaS) is a widely adopted paradigm to simplify application deployment across the edge-to-cloud continuum. However, its stateless nature forces functions to retrieve their state from external, typically cloud-centric, data stores, reintroducing the very latency that edge computing aims to eliminate. This issue is further exacerbated by location-agnostic schedulers and rigid, one-size-fits-all consistency models that fail to capture the diverse requirements of edge applications. In this paper, we propose Ermes, a distributed platform that natively integrates state management into the FaaS paradigm, enabling the joint distribution of computational workloads and application state across the edge-to-cloud continuum. Ermes organizes application state into logical units, termed collections, and employs a distributed coordination algorithm that jointly maps collections and functions onto the available nodes seeking to minimize the latency perceived by the clients. In addition, it supports fine-grained replication and per-collection consistency levels, ranging from sequential to eventual consistency, leaving developers the choice of how to resolve the trade-off between consistency and performance. The experimental evaluation shows that Ermes quickly turns remote state accesses into local ones and sustains low latency as the workload grows, and as clients move across the edge.
CommentsUnder revision at Future Generation Computer Systems (FGCS)
