边缘计算中分布式流处理应用的动态垂直扩展策略
A Dynamic Vertical Scaling Strategy for Distributed Stream Processing Applications in Edge Computing
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中文总结 AI 辅助
针对边缘流处理资源受限问题，提出基于近端策略优化的动态垂直扩展策略，联合调整任务分配并满足p95延迟目标，实验表明违规率低于对比方法且CPU使用更少。
中文摘要 AI 辅助
边缘环境中的分布式流处理应用必须在有限且异构的资源条件下兼顾低延迟与高吞吐量。本文提出一种基于近端策略优化的动态垂直扩展策略，并将其建模为部分可观测马尔可夫决策过程。该策略联合调整任务分配，并优先满足p95端到端延迟服务等级目标。在EdgeStreamPy模拟实验中，针对两种应用配置文件、两种工作负载以及每种组合下的十组配对放置，所选策略保持了吞吐量，平均违规率在0.03%至0.30%之间，在所有场景中均低于VRebalance，并且在四种组合中的三种情况下使用了更少的CPU。比较涵盖了具有不同决策频率的完整控制器配置。
英文摘要
Distributed Stream Processing applications at the edge must reconcile low latency and high throughput with limited and heterogeneous resources. This paper presents a dynamic vertical scaling strategy based on Proximal Policy Optimization, formulated as a Partially Observable Markov Decision Process. The policy jointly adjusts task allocations and prioritizes compliance with a p95 end-to-end latency Service Level Objective. In EdgeStreamPy simulation experiments with two application profiles, two workloads, and ten paired placements per combination, the selected policies preserved throughput, obtained mean violation rates from 0.03% to 0.30%, below VRebalance in every scenario, and used less CPU in three of four combinations. The comparison covers complete controller configurations with different decision frequencies.
