面向资源受限嵌入式系统的云侧事务编排框架
Cloud-Side Transactional Orchestration Framework for Resource-Constrained Embedded Systems
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中文总结 AI 辅助
本文提出一种云侧事务编排框架（T-BFF），通过将状态机迁移至云端并采用双握手协议，在资源受限设备上降低35%堆使用并提升40%事务延迟。
中文摘要 AI 辅助
随着数字商业生态系统扩展到低端消费电子（CE）领域，硬件限制——特别是有限的CPU占空比和易变的堆碎片化——成为复杂事务流程的重大瓶颈。传统的设备端中间件需要高“网络闲聊”来管理多步状态机，导致延迟增加，并可能在不稳定的住宅网络上导致事务失败。本文提出了一种新颖的面向资源受限嵌入式平台的事务型后端-前端（T-BFF）编排框架。通过将事务状态机和微服务编排迁移到云侧层，我们显著减少了客户端开销。我们的框架引入了一种“双握手”协议，利用非易失性闪存进行硬件重启后的状态恢复。在ARM Cortex-A53平台上的实验结果表明，最大堆使用量减少了35%，端到端事务延迟改善了40%。该框架为在2026年物联网环境中在传统硬件上维护事务完整性提供了可扩展、可持续的蓝图。
英文摘要
As digital commerce ecosystems expand into low-end consumer electronics (CE), hardware constraints-specifically limited CPU duty cycles and volatile heap fragmentation-become significant bottlenecks for complex transactional flows. Traditional on-device middleware requires high "network chattiness" to manage multi-step state machines, leading to increased latency and potential transaction failure on unstable residential networks. This paper proposes a novel Transactional Backend-for-Front-End (T-BFF) Orchestration Framework designed for resource-constrained embedded platforms. By migrating the transactional state machine and microservice orchestration to a cloud-side layer, we achieve a significant reduction in client-side overhead. Our framework introduces a "Double-Handshake" protocol utilizing non-volatile flash memory for state recovery after hardware reboots. Experimental results on an ARM Cortex-A53 platform demonstrate a 35% reduction in maximum heap usage and a 40% improvement in end-to-end transaction latency. This framework provides a scalable, sustainable blueprint for maintaining transactional integrity on legacy hardware in the 2026 IoT landscape.
发表机构
- Binghamton University（宾汉姆顿大学）
机构由 AI 辅助整理，请以论文原文为准。
