• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science ›› 2023, Vol. 45 ›› Issue (09): 1544-1552.

• High Performance Computing • Previous Articles     Next Articles

A 6H parallel computing architecture for edge computing

LI Lei1,2,ZHENG Li-ming1,WANG Hong-yi1,CHAI Yong-yi1,LIU Pei-guo1   

  1. (1.College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073;
    2.The 15th Research Institute,China Electronics Technology Group Corporation,Beijing 100083;
    3.Equipment System Evaluation Center of Equipment Development Department,Beijing 100009,China)
  • Received:2023-02-12 Revised:2023-04-13 Accepted:2023-09-25 Online:2023-09-25 Published:2023-09-12

Abstract: 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.

Key words: edge computing, fog computing, mobile-edge computing, six-high, architecture