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Exploring Machine Learning Models with Spatial-Temporal Information for Interconnect Network Traffic Forecasting

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Xiongxiao XuAuthors Info & Claims

SIGSIM-PADS '23: Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

Pages 56 - 57

https://doi.org/10.1145/3573900.3593635

Published: 21 June 2023 Publication History

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Abstract

Interconnect networks are an essential component of high-performance computing (HPC) systems. To study large-scale networking systems, parallel discrete event simulation (PDES) has been widely used to simulate real-world HPC behaviors. However, PDES simulation requirements and computational complexity are increasing rapidly, making it challenging to achieve accurate results. Therefore, researchers have been exploring a surrogate-ready PDES framework that utilizes machine learning-based surrogate models to accelerate PDES. In this paper, we present our vision and initial step to leverage machine learning models to utilize spatial-temporal information to forecast interconnect network traffic. The preliminary results show that it is promising to explore machine learning models for interconnect network traffic forecasting.

References

[1]

Christopher D Carothers, David Bauer, and Shawn Pearce. 2002. ROSS: A high-performance, low-memory, modular Time Warp system. J. Parallel and Distrib. Comput. 62, 11 (2002), 1648–1669.

Crossref

Google Scholar

[2]

Xingyi Cheng, Ruiqing Zhang, Jie Zhou, and Wei Xu. 2018. Deeptransport: Learning spatial-temporal dependency for traffic condition forecasting. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.

Crossref

Google Scholar

[3]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735–1780.

Digital Library

Google Scholar

[4]

Yao Kang, Xin Wang, and Zhiling Lan. 2022. Study of Workload Interference with Intelligent Routing on Dragonfly. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis (Dallas, Texas) (SC ’22). Article 20, 14 pages.

Digital Library

Google Scholar

[5]

John Kim, Wiliam J Dally, Steve Scott, and Dennis Abts. 2008. Technology-driven, highly-scalable dragonfly topology. ACM SIGARCH Computer Architecture News 36, 3 (2008), 77–88.

Digital Library

Google Scholar

[6]

Boyang Li, Sudheer Chunduri, Kevin Harms, Yuping Fan, and Zhiling Lan. 2019. The effect of system utilization on application performance variability. In Proceedings of the 9th International Workshop on Runtime and Operating Systems for Supercomputers. 11–18.

Digital Library

Google Scholar

[7]

Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. 2015. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies 54 (2015), 187–197.

Crossref

Google Scholar

[8]

Misbah Mubarak, Christopher D Carothers, Robert B Ross, and Philip Carns. 2017. Enabling parallel simulation of large-scale HPC network systems. IEEE Transactions on Parallel and Distributed Systems 28, 1 (2017), 87–100.

Digital Library

Google Scholar

[9]

Kalyan Perumalla, Maximilian Bremer, Kevin Brown, Cy Chan, Stephan Eidenbenz, K Scott Hemmert, Adolfy Hoisie, Benjamin Newton, James Nutaro, Tomas Oppelstrup, 2022. Computer Science Research Needs for Parallel Discrete Event Simulation (PDES). Technical Report. Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States).

Google Scholar

[10]

Igor V Tetko, David J Livingstone, and Alexander I Luik. 1995. Neural network studies. 1. Comparison of overfitting and overtraining. Journal of chemical information and computer sciences 35, 5 (1995), 826–833.

Crossref

Google Scholar

[11]

Xin Wang, Misbah Mubarak, Yao Kang, Robert B Ross, and Zhiling Lan. 2020. Union: An automatic workload manager for accelerating network simulation. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 821–830.

Crossref

Google Scholar

[12]

Xin Wang, Misbah Mubarak, Xu Yang, Robert B Ross, and Zhiling Lan. 2018. Trade-off study of localizing communication and balancing network traffic on a dragonfly system. In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 1113–1122.

Crossref

Google Scholar

[13]

Huaxiu Yao, Xianfeng Tang, Hua Wei, Guanjie Zheng, and Zhenhui Li. 2019. Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 5668–5675.

Digital Library

Google Scholar

Cited By

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Xu XA. Brown KMallick TWang XCruz-Camacho EB. Ross RD. Carothers CLan ZShu K(2024)Surrogate Modeling for HPC Application Iteration Times Forecasting with Network FeaturesProceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3615979.3656055(93-97)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3615979.3656055
Awoleke OSachdev KBrown K(2024)On the utility of probabilistic closed-form proxy models for describing supercomputer network traffic dataInternational Journal of Data Science and Analytics10.1007/s41060-024-00592-zOnline publication date: 23-Aug-2024
https://doi.org/10.1007/s41060-024-00592-z

Index Terms

Exploring Machine Learning Models with Spatial-Temporal Information for Interconnect Network Traffic Forecasting
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques
      1. Discrete-event simulation

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SIGSIM-PADS '23: Proceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

June 2023

173 pages

ISBN:9798400700309

DOI:10.1145/3573900

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 21 June 2023

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SIGSIM-PADS '23: SIGSIM Conference on Principles of Advanced Discrete Simulation

June 21 - 23, 2023

FL, Orlando, USA

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Xu XA. Brown KMallick TWang XCruz-Camacho EB. Ross RD. Carothers CLan ZShu K(2024)Surrogate Modeling for HPC Application Iteration Times Forecasting with Network FeaturesProceedings of the 38th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3615979.3656055(93-97)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3615979.3656055
Awoleke OSachdev KBrown K(2024)On the utility of probabilistic closed-form proxy models for describing supercomputer network traffic dataInternational Journal of Data Science and Analytics10.1007/s41060-024-00592-zOnline publication date: 23-Aug-2024
https://doi.org/10.1007/s41060-024-00592-z

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Machine Learning for Interconnect Network Traffic Forecasting: Investigation and Exploitation

Network Traffic Classification Using Machine Learning for Software Defined Networks

A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network

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