Computer Science ›› 2022, Vol. 49 ›› Issue (2): 336-341.doi: 10.11896/jsjkx.201200126

• Computer Network • Previous Articles     Next Articles

Load Scheduling Algorithm for Distributed On-board RTs System Based on Machine Learning

TAN Shuang-jie1,2, LIN Bao-jun1,2,3,4,5, LIU Ying-chun2,3,4, ZHAO Shuai2,4   

  1. 1 School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China
    2 Innovation Academy for Microsatellites,Chinese Academy of Sciences,Shanghai 201203,China
    3 School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100094,China
    4 Shanghai Engineering Center for Microsatellites,Shanghai 201210,China
    5 Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China
  • Received:2020-12-14 Revised:2021-04-19 Online:2022-02-15 Published:2022-02-23
  • About author:TAN Shuang-jie,born in 1994,postgra-duate.Her main research interests include distributed computing and embedded software.
    LIN Bao-jun,born in 1963,Ph.D,professor,Ph.D supervisor.His main research interests include computer control technology and satellite overall.

Abstract: The tasks of distributed on-board multi-RTs (remote terminals) system are mainly distributed based on functions,while the burstiness of data processing tasks often leads to unbalanced load among different computers.Using a flexible load scheduling mechanism can effectively adjust the load difference between different computers,thereby improving the overall performance of the computer system to a certain extent.A load scheduling algorithm for distributed on-board RTs system based on machine learning is proposed in this paper,which includes four steps:sample collection,task throughput prediction model construction,throughput prediction and load scheduling.In the process of constructing the task throughput prediction model,the weight of the model is obtained through the linear regression normal equation of machine learning,which reduces the time spent in constructing the model.In the load scheduling link,if the total throughput rate of RTs is greater than the total load data volume of the system,data will be allocated to each RT in proportion to the throughput rate;otherwise,only a certain amount of data will be allocated to RTs whose load data volume is less than their own throughput rate.The test results on the ground simulation system constructed by multiple on-board computers electrical performance products show that the algorithm can increase the average CPU utilization rate of all nodes of the system by 23.78%,and reduce the variance of CPU utilization rate between nodes to 34.59%.The total system throughput of the task is significantly increased by 225.97%.In other words,this method can effectively improve system resource utilization while ensuring system load balance,and improve the real-time data processing performance of the on-board computer system.

Key words: Distributed system, Dynamic load balancing, Machine learning, On-board computer, Task scheduling

CLC Number: 

  • TP393
[1]SAEED N,ELZANATY A,ALMORAD H,et al.CubeSat Communications:Recent Advances and Future Challenges[J].IEEE Communication Survey & Tutorials,2020,22(3):1839-1862.
[2]LI H W,WU Q,XU G,et al.Progress and Tendency of Space and Earth Integrated Network[J].Science & Technology Review,2016,34(14):95-106.
[3]HUANG C.Reliable Reconstruction Technology for On-board Computer Based on Loongson and FLASH[D].Beijing:University of Chinese Academy of Sciences,2017.
[4]LU S Q,LIANG H G,LIU D Y.Thoughts on the Status Quo and Development of Localized On-board Computer Technology[J].Computer Knowledge and Technology,2018,6:126-129.
[5]PATNI J C,ASWAL M S.Distributed Load Balancing Modelfor Grid Computing Environment[C]//2015 1st International Conference on Next Generation Computing Technologies.2015:123-126.
[6]PENG T,HOFLINGER K,WEPS B,et al.A Component-Based Middleware for a Reliable Distributed and Reconfigurable Spacecraft Onboard Computer[C]//2016 IEEE 35th Symposium on Reliable Distributed Systems.2016:337-342.
[7]REN J Y,SUN H Y,ZHANG L X,et al.Development status ofspace laser communication and new method of networking[J].Laser & Infrared,2019,49(2):143-150.
[8]LIU L D,QI D Y.An Independent Task Scheduling Algorithmin Heterogeneous Multi-core Processor Environment[C]//2018 IEEE 3rd Advanced Information Technology,Electronic and Automation Control Conference.2018:142-146.
[9]ZHANG J,SUN S J,FAN H B,et al.Task Scheduling Algorithm in Heterogeneous Multi-core Processor with High Real-time Performance[J].Computer Engineering,2017,43(5):55-59.
[10]AN X,ZHANG Y,KANG A,et al.Machine learning based online mapping approach for heterogeneous multi-core processor system[J].Journal of Computer Applications,2019,39(6):1753-1759.
[11]IBM Cloud Education.Machine Learning Focuses on Applica-tions that Learn from Experience and Improve their Decision-making or Predictive Accuracy over Time[EB/OL].(2020-07-15)[2020-10-01].https://www.ibm.com/cloud/learn/machine-learning.
[12]SAMIE F,BAUER L,HENKEL J.From Cloud Down toThings:An Overview of Machine Learning in Internet of Things[J].IEEE Internet of Things Journal,2019,6(3):4921-4934.
[13]NEMIROVSKY D,ARKOSE T,MARKOVIC N,et al.A ma-chine learning approach for performance prediction and scheduling on heterogeneous CPUs[C]//Proceedings of the 2017 IEEE 29th International Symposium on Computer Architecture and High Performance Computing.Piscataway,NJ:IEEE,2017:121-128.
[14]MICOLET P J,SMITH A,DUBACH C.A machine learning approach to mapping streaming workloads to dynamic multicore processors[C]//LCTES 2016:Proceedings of the 2016 17th ACM SIGPLAN/SIGBED Conference on Languages,Compi-lers,Tools and Theory for Embedded Systems.New York:ACM,2016:113-122.
[15]ROTATION.Machine Learning-A Summary of Linear Regression[EB/OL].(2010-01-19)[2020-10-01].https://blog.csdn.net/fengxinlinux/article/details/86556584.
[16]XIE Y X,LI Y W,XIA Z J,et al.An Improved Forward Regression Variable Selection Algorithm for High-Dimensional Linear Regression Models[J].IEEE Access,2020,8:129032-129042.
[17]YUAN D M,PROUTIERE A,SHI G D.Distributed Online Li-near Regressions[J].Transactions on Information Theory,2021,67(1):616-639.
[18]YU D F,LI H J,TANG H,et al.Dynamic Load Balancing Algorithm Design and Application based on Feedback[J].Application Research of Computers,2012,29(2):527-529.
[19]GAST N,IOANNIDIS S,LOISEAU P,et al.Linear Regression from Strategic Data Sources[J].ACM Transactions on Econo-mics and Computation,2020,8(2):1-24.
[20]LIANG J B,ZHANG H H,JIANG C,et al.Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing[J].Computer Science,2021,48(7):316-323.
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