An Online Incremental Learning Framework for HPC Job Power Consumption Prediction
Authors: 
Xuesen Tian, Xiang Li, Jie Zhang, Zhigang Zhao, Chunxiao Wang, Xiangkai Wang, Jibin WangAuthors Info & Claims
Pages 176 - 183
Abstract
This paper proposes a high-performance computing (HPC) job power consumption prediction framework based on online incremental learning. The framework combines traditional offline models with online incremental update mechanisms, allowing it to quickly adapt to the uncertainty and dynamic changes of power consumption behaviors in HPC jobs. We use NBeats, LSTM, Seq2seq, and GRU models as our offline models and combine them with the proposed online update mechanisms to achieve online incremental updates. We conducted experiments using real HPC job data, and the results show that our online incremental model can predict more accurately than traditional offline models. In addition, We propose an update frequency control mechanism that reduces the running time by about 30 while guaranteeing the accuracy of the prediction. This indicates that the HPC job power consumption prediction framework based on online incremental learning proposed in this paper has a wide range of applications in practical use.
References
[1]
Jiwoo Bang, Chungyong Kim, Kesheng Wu, Alex Sim, Suren Byna, Sunggon Kim, and Hyeonsang Eom. 2020. HPC workload characterization using feature selection and clustering. In Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics. 33–40.
[2]
Shajulin Benedict, RS Rejitha, Philipp Gschwandtner, Radu Prodan, and Thomas Fahringer. 2015. Energy prediction of OpenMP applications using random forest modeling approach. In 2015 IEEE International Parallel and Distributed Processing Symposium Workshop. IEEE, 1251–1260.
[3]
Bruce Bugbee, Caleb Phillips, Hilary Egan, Ryan Elmore, Kenny Gruchalla, and Avi Purkayastha. 2017. Prediction and characterization of application power use in a high-performance computing environment. Statistical Analysis and Data Mining: The ASA Data Science Journal 10, 3 (2017), 155–165.
[4]
Wei Cai, Xiaodong Wen, Chaoen Li, Jingjing Shao, and Jianguo Xu. 2023. Predicting the energy consumption in buildings using the optimized support vector regression model. Energy 273 (2023), 127188.
[5]
Feifei Chen, John Grundy, Yun Yang, Jean-Guy Schneider, and Qiang He. 2013. Experimental analysis of task-based energy consumption in cloud computing systems. In Proceedings of the 4th ACM/SPEC international conference on performance engineering. 295–306.
[6]
Rogério Luís de C Costa. 2022. Convolutional-LSTM networks and generalization in forecasting of household photovoltaic generation. Engineering Applications of Artificial Intelligence 116 (2022), 105458.
[7]
Jiandong Duan, Xuan Tian, Wentao Ma, Xinyu Qiu, Peng Wang, and Lin An. 2019. Electricity consumption forecasting using support vector regression with the mixture maximum correntropy criterion. Entropy 21, 7 (2019), 707.
[8]
Ninad Hogade and Sudeep Pasricha. 2022. A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management. IEEE Transactions on Sustainable Computing (2022).
[9]
Chao Jing and Jiaming Li. 2022. An Improved ANN-based Power Prediction Algorithm for Multi-type Jobs on Heterogeneous Computing Server. (2022).
[10]
Karin Kandananond. 2011. Forecasting electricity demand in Thailand with an artificial neural network approach. Energies 4, 8 (2011), 1246–1257.
[11]
Julian M Kunkel, Pavan Balaji, and Jack Dongarra. 2016. High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings. Vol. 9697. Springer.
[12]
Yoonjae Lee, Byeongmin Ha, and Soonho Hwangbo. 2022. Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea’s energy transition policy. Renewable Energy 200 (2022), 69–87.
[13]
André Luis da Cunha Dantas Lima, Vitor Moraes Aranha, Caio Jordão de Lima Carvalho, and Erick Giovani Sperandio Nascimento. 2021. Smart predictive maintenance for high-performance computing systems: a literature review. The Journal of Supercomputing 77, 11 (2021), 13494–13513.
[14]
Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang. 2018. Learning under concept drift: A review. IEEE transactions on knowledge and data engineering 31, 12 (2018), 2346–2363.
[15]
Kenneth O’brien, Ilia Pietri, Ravi Reddy, Alexey Lastovetsky, and Rizos Sakellariou. 2017. A survey of power and energy predictive models in HPC systems and applications. ACM Computing Surveys (CSUR) 50, 3 (2017), 1–38.
[16]
Lin Qian, Hengmao Pang, Jun Yu, Guangxin Zhu, Mingjie Xu, Lin Wang, and Haiyang Chen. 2019. High load function prediction model based on decision tree. In 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 375–378.
[17]
Mark Sagi, Martin Rapp, Heba Khdr, Yizhe Zhang, Nael Fasfous, Nguyen Anh Vu Doan, Thomas Wild, Jörg Henkel, and Andreas Herkersdorf. 2021. Long short-term memory neural network-based power forecasting of multi-core processors. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 1685–1690.
[18]
Akash Saxena, Ahmad M Alshamrani, Adel Fahad Alrasheedi, Khalid Abdulaziz Alnowibet, and Ali Wagdy Mohamed. 2022. A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines. Mathematics 10, 15 (2022), 2780.
[19]
Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. 253–260.
[20]
Somayyeh Taheri, Maziar Goudarzi, and Osamu Yoshie. 2020. Learning-based power prediction for geo-distributed Data Centers: weather parameter analysis. Journal of Big Data 7, 1 (2020), 1–16.
[21]
Mehul Nalin Vora. 2014. Predicting utilization of server resources from log data. International Journal of Computer Theory and Engineering 6, 2 (2014), 118–123.
[22]
Zhou Zhou, Jemal H Abawajy, Fangmin Li, Zhigang Hu, Morshed U Chowdhury, Abdulhameed Alelaiwi, and Keqin Li. 2017. Fine-grained energy consumption model of servers based on task characteristics in cloud data center. IEEE access 6 (2017), 27080–27090.
Index Terms
- An Online Incremental Learning Framework for HPC Job Power Consumption Prediction
Recommendations
PM100: A Job Power Consumption Dataset of a Large-scale Production HPC System
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisThe power requirements of modern High-Performance Computing (HPC) systems pose environmental and financial challenges, as they contribute to carbon emissions and strain power grids. Optimizing power consumption together with system performance has thus ...
Parallel job scheduling for power constrained HPC systems
Power has become the primary constraint in high performance computing. Traditionally, parallel job scheduling policies have been designed to improve certain job performance metrics when scheduling parallel workloads on a system with a given number of ...
Predicting Job Power Consumption Based on RJMS Submission Data in HPC Systems
High Performance ComputingAbstractPower-aware scheduling is a promising solution to the resource usage monitoring of High-Performance Computing facility electrical power consumption. This kind of solution needs a reliable estimation of job power consumption to feed the Resources ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2023
354 pages
ISBN:9781450399883
DOI:10.1145/3606043
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 November 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
HP3C 2023
HP3C 2023: 2023 7th International Conference on High Performance Compilation, Computing and Communications
June 17 - 19, 2023
Jinan, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 37Total Downloads
- Downloads (Last 12 months)36
- Downloads (Last 6 weeks)4
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format