LAFS: Learning-Based Application-Agnostic Flow Scheduling for Datacenters

Han，Feixue1,2; Li，Qing2,3; Zhu，Keke1; Zhou，Jianer2,3; Jiang，Yong1,2; Qi，Zhuyun3; Li，Fuliang4,5

10.1109/IPCCC51483.2021.9679437

2021

1097-2641

978-1-6654-4332-6

Conference Proceedings of the IEEE International Performance, Computing, and Communications Conference

2021-October

1-8

29-31 Oct. 2021

Austin, TX, USA

Many cloud applications in modern datacenters have very demanding latency requirements, making flow completion time (FCT) an important metric for evaluating the network performance. Existing network flow scheduling methods either base on pre-known information or have poor performance. Therefore, we present LAFS, an efficient learning-based flow scheduling approach which minimizes the FCT with estimated information of flows. LAFS combines system call monitoring and learning methods to learn the flow size and implements the Shortest Remaining Processing Time (SRPT) principle with in-network priorities. Moreover, LAFS adopts flowlets to alleviate the packets disorder problem in fine-grained flow scheduling. Our theoretical analysis and extensive simulations show that LAFS is a practical design and significantly outperforms other information-agnostic designs like DCTCP and PIAS under diverse workloads.

Learning systems Measurement Analytical models Processor scheduling Conferences Computational modeling Prototypes

通讯

英语

EI

National Key Research and Development Program of China[2020YFB1804704];National Natural Science Foundation of China[61972189];

20220911710140

Scheduling

Management:912.2

2-s2.0-85125166713

Scopus

1.Tsinghua Shenzhen International Graduate School,Shenzhen,China
2.Peng Cheng Laboratory,Shenzhen,China
3.Southern University Of Science And Technology,Shenzhen,China
4.Northeastern University,Shenyang,China
5.Beijing National Research Center For Information Science And Technology,Beijing,China

Han，Feixue,Li，Qing,Zhu，Keke,et al. LAFS: Learning-Based Application-Agnostic Flow Scheduling for Datacenters[C],2021:1-8.