Relay control model for concurrent data flow in edge computing

doi:10.11772/j.issn.1001-9081.2023121812

Abstract

Abstract:

In order to improve the data transmission efficiency in edge computing application scenarios and manage the concurrent data traffic effectively， a relay control model for concurrent data flow in edge computing was designed. Firstly， based on the features of Data Plane Development Kit （DPDK） such as bypassing kernel， multi-core processing， and sending and receiving packets from multiple network ports， the concurrent receiving and forwarding processing of data flow was realized. Secondly， by establishing a system model with Model Predictive Control （MPC） as the core， state prediction was used to optimize control inputs and provide timely feedback and adjustment， so as to achieve the control of data traffic. Finally， a Weighted Round-Robin （WRR） algorithm was proposed to allocate weights according to buffer size and recent usage time in order to achieve load balancing of data flow. Experimental results show that the proposed model is able to control the real-time data flow rate effectively in edge network environment， and has the control error between -1% and 2%. The proposed model improves the data flow sending bit rate of edge nodes in real application scenarios compared with traditional Linux kernel forwarding， and the transmission quality and packet delay are also improved accordingly. It can be seen that the proposed model can meet the demands for low latency and high bandwidth in edge clusters and internet of things data centers， and can optimize critical computing resources while reducing peak loads.

Key words: edge computing, concurrent data flow, Data Plane Development Kit (DPDK), Model Predictive Control (MPC), load balancing

摘要：

为提高边缘计算应用场景中数据的传输效率，有效管理并发数据流量，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（DPDK）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，通过建立以模型预测控制（MPC）为核心的系统模型，利用状态预测优化控制输入并及时反馈调整，从而实现数据流量控制；最后，提出一种加权轮询（WRR）算法，根据缓冲区的大小和最近使用时间等因素分配权重，进而实现数据流负载均衡。实验结果表明，所提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%~2%。所提模型在实际应用场景中边缘节点的数据流发送码率相较于传统Linux内核转发有所提高，传输质量和数据包时延也得到相应改善。可见，所提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

Ming ZHANG, Le FU, Haifeng WANG. Relay control model for concurrent data flow in edge computing[J]. Journal of Computer Applications, 2024, 44(12): 3876-3883.

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, 2024, 44(12): 3876-3883.

Figures/Tables 12

Fig. 1 Receiving and forwarding control model

Fig. 2 Principle of MPC

Fig. 3 Experimental environment

Tab. 1 Configuration of server

配置项	配置信息
操作系统	Ubuntu16.04
内核版本	4.4.240
CPU版本	Intel Core i5-10500
DPDK版本	Version19.08
网卡	X520
内存	16 GB DDR4 3200 * 2

Fig. 4 MPC control results

Fig. 5 MPC control error

Fig. 6 Memory optimization

Fig. 7 System delay

Fig. 8 Sending bit rate of live streaming

Fig. 9 VMAF score of live streaming

Fig. 10 Sending bit rate of video on demand streaming

Fig. 11 VMAF score of video on demand streaming

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