Dual-plane Scheduling Model for AI Data Flow in Edge Cluster
WU Ming-jie1，CHEN Qing-kui2
1（School of Management，University of Shanghai for Science and Technology，Shanghai 200093，China）2（School of Optical-Electrical Computer Engineering，University of Shanghai for Science and Technology，Shanghai 200093，China）
Abstract：With the rise of edge AI，edge GPU clusters are widely used for realtime processing of a large number of concurrent AI data flows.AI data flow not only needs to be transmitted in the cluster，but also needs to be queued and calculated on the computing node.In order to reduce the response time，the researchers aim to reduce the queuing waiting time of tasks through excellent scheduling algorithms，while ignoring the time-consuming transmission of scheduling commands.In the traditional single-plane framework，because the scheduling command and data are transmitted on the same physical line，when the amount of data transmitted in the cluster is very high，it is easy to fail scheduling due to the transmission delay and discard of the scheduling command，or even cause the cluster performance to decline or fault.This paper proposes a dual-plane scheduling model for AI data flow in edge clusters.Firstly，this paper proposes a two-plane framework designed to physically separate scheduling commands and data transmission without affecting each other.Secondly，the DPDK-based multi-NIC parallel communication technology is used in the data plane to improve the efficiency and bandwidth of data transmission，and a reliable message-based transmission protocol is designed and implemented for AI data flows.Finally，a task migration scheduling model considering network load and computing load of computing nodes is proposed，aiming to reduce the queuing delay of the data flow in the cluster.In the case of no message loss，the dual-plane architecture transmission scheme of this paper can increase the cluster data flow capacity by about 30%；without the task dropping，the dual-plane architecture scheduling model of this paper can increase the cluster data flow capacity by about 15%.
吴明杰,陈庆奎. 面向边缘集群内AI数据流的双平面调度模型[J]. 小型微型计算机系统, 2021, 42(6): 1332-1339.
WU Ming-jie,CHEN Qing-kui. Dual-plane Scheduling Model for AI Data Flow in Edge Cluster. Journal of Chinese Computer Systems, 2021, 42(6): 1332-1339.
2021, Vol. 42 
