适用于边缘计算的6H并行计算架构

A 6H parallel computing architecture for edge computing

针对云集中式计算模式在延时、安全、环境信息利用等方面的不足,近年来产业界和学术界提出了雾计算、移动边缘计算、移动云计算等不同边缘计算概念,其主要思想是将计算、存储、I/O等资源迁移到网络边缘,以提升各类应用的服务质量。但是,现有的边缘计算架构通常直接采用云计算架构,存在互操作性差、资源利用率不高、资源管理粒度不够细、动态性不强等问题。深入分析边缘计算的特点,基于轻量级虚拟化、软件定义网络、并行计算等基本理念,提出适用于边缘计算环境的6H并行计算架构,即高性能、高可用、高可扩展、高模块化、高可伸缩、高易用。随后采用Python/C++混合编程模式实现了一个6H计算框架。在边缘计算典型硬件条件下,采用物联网典型用例对该计算框架进行了测试。其结果表明:随着计算进程和计算节点数据增加,计算耗时近似成线性下降,说明该计算框架的可扩展性和可伸缩性较好;在高并发条件下,计算框架表现优异,说明该框架具备高性能;在边缘服务器异常情况下,计算框架恢复时间快,说明该框架的可用性较好。另外,该计算框架采用CMD-Worker-Handler编程模型,高度模块化,二次开发简单方便,具有很好的易用性。

The current centralized cloud computing model has shortcomings in terms of latency,security,and utilization of environmental information.In recent years,the industry and academia have proposed various edge computing concepts such as fog computing,mobile edge computing,and mobile cloud computing to address these issues.The main idea is to move computing,storage,I/O,and other resources to the network edge in order to improve the service quality of various applications.However,existing edge computing architectures often directly adopt cloud computing architectures,leading to a series of problems such as poor interoperability,low resource utilization,insufficient granularity of resource management,and lack of dynamism.This paper deeply analyzes the characteristics of edge computing and proposes a 6H parallel computing architecture suitable for edge computing environments based on lightweight virtualization,software-defined networking,parallel computing,and other basic concepts.The 6H parallel computing architecture aims to achieve high performance,high availability,scalability,modularity,scalability,and ease of use.Subsequently,this paper implements a 6H computing framework using a Python/C++hybrid programming model.The framework is tested under typical edge computing hardware conditions with typical IoT use cases.The results show that as the number of computing processes and computing node data increases,the computation time decreases nearly linearly,indicating good scalability and scalability of the framework.Under high-concurrency conditions,the framework performs well,demonstrating high performance.In case of abnormal situations on the edge servers,the framework has a fast recovery time,indicating good availability.In addition,the computing framework adopts the CMD-Worker-Handler programming model,which is highly modular and allows for easy secondary development,showing good usability.