Relay control model for concurrent data flow in edge computing

doi:10.11772/j.issn.1001-9081.2023121812

Relay control model for concurrent data flow in edge computing

In order to improve the data transmission efficiency in edge computing application scenarios and effectively manage the concurrent data traffic, a relay control model for concurrent data flow in edge computing was designed. Firstly, based on the features of data plane development kit such as bypassing kernel, multi-core processing and sending and receiving packets from multiple network ports, the concurrent receive and forwarding processing of data flow was realized; Secondly, by establishing a system model with model predictive control as the core, utilizing state prediction to optimize control inputs and provide timely feedback adjustments, so as to achieve the control of data flow; Finally, a weighted polling algorithm was proposed to allocate weights according to the buffer size and recent usage time to achieve data flow load balancing. Experimental results showed that the model could effectively control the real-time data flow rate in the edge network environment, and the control error floated between -1% and 2%. The data stream sending bit rate of edge nodes in real application scenarios was improved by about 15.9% compared with traditional Linux kernel forwarding, and the transmission quality and packet delay were also improved accordingly. The relay control model can meet the demand for low latency and high bandwidth in edge clusters and IoT data centers, and can optimize critical computing resources while reducing peak loads.

School of Information Science and Engineering, Linyi University, Linyi Shandong 276002, China

Received:2024-01-02 Revised:2024-03-11 Accepted:2024-03-14 Online:2024-03-21
Supported by:
Key Project of Shandong Provincial Foundation of China(2019GGX101003)， Shandong Province Science and Technology-based Small and Medium-sized Enterprises Innovation Capacity Enhancement Project (2023TSGC0449)

面向边缘计算的并发数据流接转控制模型

为提高边缘计算应用场景中数据的传输效率，以及有效管理的并发数据流量，设计一种面向边缘计算的并发数据流接转控制模型。首先，基于数据平面开发套件的绕过内核、多核处理和多网口收发数据包等特点，实现对数据流的并发接收和转发处理；其次，通过以模型预测控制为核心建立系统模型，利用状态预测对控制输入优化并及时反馈调整，从而实现对数据流量的控制；最后提出一种加权轮询算法，根据缓冲区的大小和最近使用时间等因素分配权重，实现数据流负载均衡。实验结果表明，该模型能够有效控制边缘网络环境中的实时数据流速，控制误差在-1%到2%之间。在实际应用场景中边缘节点的数据流发送码率较传统Linux内核转发提高了约15.9%，传输质量和数据包时延也得到相应改善。该接转控制模型能够满足边缘集群和物联网数据中心对低延迟和高带宽的需求，在消减峰值负载的同时可优化关键计算资源。

临沂大学信息科学与工程学院,山东临沂 276002

通讯作者: 王海峰 gadfly7@126.com
基金资助:
山东省重点研发资助项目(2019GGX101003); 山东省科技型中小企业创新能力提升工程项目(2023TSGC0449)。

Abstract

Abstract: In order to improve the data transmission efficiency in edge computing application scenarios and effectively manage the concurrent data traffic, a relay control model for concurrent data flow in edge computing was designed. Firstly, based on the features of data plane development kit such as bypassing kernel, multi-core processing and sending and receiving packets from multiple network ports, the concurrent receive and forwarding processing of data flow was realized; Secondly, by establishing a system model with model predictive control as the core, utilizing state prediction to optimize control inputs and provide timely feedback adjustments, so as to achieve the control of data flow; Finally, a weighted polling algorithm was proposed to allocate weights according to the buffer size and recent usage time to achieve data flow load balancing. Experimental results showed that the model could effectively control the real-time data flow rate in the edge network environment, and the control error floated between -1% and 2%. The data stream sending bit rate of edge nodes in real application scenarios was improved by about 15.9% compared with traditional Linux kernel forwarding, and the transmission quality and packet delay were also improved accordingly. The relay control model can meet the demand for low latency and high bandwidth in edge clusters and IoT data centers, and can optimize critical computing resources while reducing peak loads.

Key words: edge computing, concurrent data flow, data plane development kit, model prediction control, load balancing

摘要： 为提高边缘计算应用场景中数据的传输效率，以及有效管理的并发数据流量，设计一种面向边缘计算的并发数据流接转控制模型。首先，基于数据平面开发套件的绕过内核、多核处理和多网口收发数据包等特点，实现对数据流的并发接收和转发处理；其次，通过以模型预测控制为核心建立系统模型，利用状态预测对控制输入优化并及时反馈调整，从而实现对数据流量的控制；最后提出一种加权轮询算法，根据缓冲区的大小和最近使用时间等因素分配权重，实现数据流负载均衡。实验结果表明，该模型能够有效控制边缘网络环境中的实时数据流速，控制误差在-1%到2%之间。在实际应用场景中边缘节点的数据流发送码率较传统Linux内核转发提高了约15.9%，传输质量和数据包时延也得到相应改善。该接转控制模型能够满足边缘集群和物联网数据中心对低延迟和高带宽的需求，在消减峰值负载的同时可优化关键计算资源。

关键词: 边缘计算, 并发数据流, 数据平面开发套件, 模型预测控制, 负载均衡

CLC Number:

TP393.09

LeFU, Hai-fengWANG, . Relay control model for concurrent data flow in edge computing[J]. Journal of Computer Applications, DOI: 10.11772/j.issn.1001-9081.2023121812.

张明, 付乐, 王海峰, . 面向边缘计算的并发数据流接转控制模型[J]. 《计算机应用》唯一官方网站, DOI: 10.11772/j.issn.1001-9081.2023121812.

[1]	Xuyan ZHAO, Yunhe CUI, Chaohui JIANG, Qing QIAN, Guowei SHEN, Chun GUO, Xianchao LI. CHAIN： edge computing node placement algorithm based on overlapping domination [J]. Journal of Computer Applications, 2023, 43(9): 2812-2818.
[2]	Wanzhen CHEN, En ZHANG, Leiyong QIN, Shuangxi HONG. Privacy-preserving federated learning algorithm based on blockchain in edge computing [J]. Journal of Computer Applications, 2023, 43(7): 2209-2216.
[3]	Xiaolin LI, Yusang JIANG. Task offloading algorithm for UAV-assisted mobile edge computing [J]. Journal of Computer Applications, 2023, 43(6): 1893-1899.
[4]	Tengfei CAO, Yanliang LIU, Xiaoying WANG. Edge computing and service offloading algorithm based on improved deep reinforcement learning [J]. Journal of Computer Applications, 2023, 43(5): 1543-1550.
[5]	Yanan SUN, Jiehong WU, Junling SHI, Lijun GAO. Multi-UAV collaborative task assignment method based on improved self-organizing map [J]. Journal of Computer Applications, 2023, 43(5): 1551-1556.
[6]	Yunbo LONG, Dan TANG. Load balancing method based on local repair code in distributed storage [J]. Journal of Computer Applications, 2023, 43(3): 767-775.
[7]	Li YANG, Jianting CHEN, Yang XIANG. Performance optimization strategy of distributed storage for industrial time series big data based on HBase [J]. Journal of Computer Applications, 2023, 43(3): 759-766.
[8]	Zhe WANG, Qiming WANG, Taoshen LI, Lina GE. Joint optimization method for SWIPT edge network based on deep reinforcement learning [J]. Journal of Computer Applications, 2023, 43(11): 3540-3550.
[9]	Yanpei LIU, Ningning CHEN, Yunjing ZHU, Liping WANG. Review of mobile edge caching optimization technologies for 5G/Beyond 5G [J]. Journal of Computer Applications, 2022, 42(8): 2487-2500.
[10]	Jieqin WANG, Shihyang LIN, Shiming PENG, Shuo JIA, Miaohui YANG. Hierarchical resource allocation mechanism of cooperative mobile edge computing [J]. Journal of Computer Applications, 2022, 42(8): 2501-2510.
[11]	Jie ZHANG, Shanshan XU, Lingyun YUAN. Internet of things access control model based on blockchain and edge computing [J]. Journal of Computer Applications, 2022, 42(7): 2104-2111.
[12]	Huiwen XIA, Zhongyu ZHAO, Zhuoer WANG, Qingyong ZHANG, Feng PENG. Public transportation epidemic monitoring system based on edge computing [J]. Journal of Computer Applications, 2022, 42(7): 2132-2138.
[13]	Shiquan DENG, Xuguo YE. Multi-objective task offloading algorithm based on deep Q-network [J]. Journal of Computer Applications, 2022, 42(6): 1668-1674.
[14]	Jingling YUAN, Huihua MAO, Nana WANG, Yao XIANG. Dynamic service deployment strategy in resource constrained mobile edge computing [J]. Journal of Computer Applications, 2022, 42(6): 1662-1667.
[15]	Yu LI, Xiping HE, Lianggui TANG. Multi-user computation offloading and resource optimization policy based on device-to-device communication [J]. Journal of Computer Applications, 2022, 42(5): 1538-1546.