Skip to main content
SpringerLink
Log in

Workload Prediction of Cloud Workflow Based on Graph Neural Network

  • Conference paper
  • First Online:
Web Information Systems and Applications (WISA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12999))

Included in the following conference series:

Abstract

With the continuous expansion of cloud computing market, the problem of low utilization rate of cloud computing resource has become increasingly prominent, because cloud computing vendors can not schedule a large number of server cluster effectively as before. Improving the utilization rate of cloud resources can not only improve the net profit of cloud computing manufacturers, but also reduce the time cost and economic cost of cloud computing users. In addition to resource scheduling, the current research on cloud workflow load is still focused on single task or single instance prediction, and even the data sets used are simulation data. This paper aims to predict workload of cloud workflow resources to make the cloud computing resources get better scheduling, and ultimately facilitate all relevant personnel in the cloud computing market. Firstly, compared with task and single instance, cloud workflow can get more context information. Secondly, in order to make this research more practical, this paper selects Alibaba cluster data V2018 released by Alibaba in 2018 as our research object. Thirdly, based on the graph structure characteristics of cloud computing workflow, this paper selects the Graph Neural Network (GNN) architecture which closely fits the graph structure to predict the load of cloud computing workflow, and specifically selects the homogeneous Graph Convolution Neural Network and Graph Attention Neural Network and heterogeneous GCN as our prediction algorithm. And it describes how cloud workflow is modeled as homogeneous graph and heterogeneous graph in detail. Finally, the algorithm in GNN is used to classify and predict Ali data with workflow length ranges from 4 to 12 separately and combined, and predicts the last and penultimate tasks of each length workflow. Besides, all the data from 4 to 12 are combined into one data to predict the last and penultimate tasks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Armbrust, M., Fox, A., et al.: Above the clouds: a Berkeley view of cloud computing. Science (2009)

    Google Scholar 

  2. Creeger, M.: Cloud computing: an overview. Queue 7(5), 2 (2009)

    Google Scholar 

  3. Zheng, Z., Zhang, Y., Lyu, M.R.: CloudRank: a QoS-driven component ranking framework for cloud computing. In: Reliable Distributed Systems, 2010 29th IEEE Symposium on IEEE (2010)

    Google Scholar 

  4. Liu, X., Yuan, D., Zhang, G., et al.: The Design of Cloud Workflow Systems. Springer, New York (2012)

    Book  Google Scholar 

  5. Ali-Eldin, A., Seleznjev, O., Sara Sjöstedt-de, L., et al.: Measuring cloud workload Burstiness. In: IEEE/ACM International Conference on Utility & Cloud Computing. IEEE (2014)

    Google Scholar 

  6. Randles, M., Lamb, D.A., Taleb-Bendiab, A.: A comparative study into distributed load balancing algorithms for cloud computing. In: 24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010, Perth, Australia, 20–13 April 2010. IEEE (2010)

    Google Scholar 

  7. Dhinesh, B., Krishna, P.V., Venkata Krishna, P.: Honey bee behavior inspired load balancing of tasks in cloud computing environments. Appl. Soft Comput. 13(5), 2292–2303

    Google Scholar 

  8. Singh, A., Juneja, D., Malhotra, M.: Autonomous agent based load balancing algorithm in cloud computing. Procedia Comput. Sci. 45, 832–841 (2015)

    Article  Google Scholar 

  9. Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities. IEEE (2009)

    Google Scholar 

  10. Liu, D., Khoukhi, L., Hafid, A.: Prediction-based mobile data offloading in mobile cloud computing. IEEE Trans. Wireless Commun. 17(7), 4660–4673 (2018)

    Article  Google Scholar 

  11. Daetwyler, H.D., Calus, M.P.L., et al.: Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193(2), 347–365 (2013)

    Article  Google Scholar 

  12. Da Bbagh, M., Hamdaoui, B., Guizani, M., et al.: Toward energy-efficient cloud computing: prediction, consolidation, and over commitment. Netw. IEEE 29(2), 56–61 (2015)

    Article  Google Scholar 

  13. Zhang, Y., Zheng, Z., Lyu, M.R.: Exploring latent features for memory-based QoS prediction in cloud computing. In: IEEE International Symposium on Reliable Distributed Systems. IEEE Computer Society (2011)

    Google Scholar 

  14. Jokhio, F.A., Ashraf, A., Lafond, S., et al. Prediction-based dynamic resource allocation for video transcoding in cloud computing. In: Euromicro International Conference on Parallel. IEEE (2013)

    Google Scholar 

  15. Kwok, Y.-K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)

    Article  Google Scholar 

  16. Quaglini, S.: Workflow management—models, methods and systems. Artif. Intell. Med. 27(3), 393–396 (2003)

    Article  Google Scholar 

  17. Yu, J., Buyya, R.: A taxonomy of workflow management systems for grid computing. J. Grid Comput. 3(3), 171–200 (2005)

    Article  Google Scholar 

  18. Cortes, C., Cortes, C., Vapnik, V., et al. Support-vector networks (1995)

    Google Scholar 

  19. Kleinbaum, D.G., Klein, M. Logistic Regression (A Self-Learning Text). Springer, New York (2002). https://doi.org/10.1007/978-1-4419-1742-3

  20. Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (2003)

    Google Scholar 

  21. Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

  22. Scarselli, F., Gori, M., Tsoi, A.C., et al.: The Graph Neural Network Model. IEEE Trans. Neural Netw. 20(1), 61–80 (2009)

    Article  Google Scholar 

  23. Hellerstein, J.L.: Google Cluster Data (2010)

    Google Scholar 

  24. Cortez, E., Bonde, A., Muzio, A., et al.: Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms Symposium. ACM, New York (2017)

    Google Scholar 

  25. Shahrad, M., Fonseca, R., Goiri, I., et al.: Serverless in the wild: characterizing and optimizing the serverless workload at a large cloud provider. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20) (2020)

    Google Scholar 

  26. Lu, C., Ye, K., Xu, G., et al.: Imbalance in the cloud: an analysis on Alibaba cluster trace. In: 2017 IEEE International Conference on Big Data (Big Data). IEEE (2018)

    Google Scholar 

  27. Hao, X., Zhang, G., Ma, S.: Deep learning. Int. J. Semant. Comput. 10(03), 417–439 (2016)

    Article  Google Scholar 

  28. Zhao, B., Xu, Z., Tang, Y., Li, J., Liu, B., Tian, H.: Effective knowledge-aware recommendation via graph convolutional networks. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds.) WISA 2020. LNCS, vol. 12432, pp. 96–107. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60029-7_9

    Chapter  Google Scholar 

  29. Cheng, B., Yang, J., Yan, S., et al.: Learning with l1-graph for image analysis. IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc. 19(4), 858–866 (2010)

    Google Scholar 

  30. Berberidis, D., Nikolakopoulos, A.N., Giannakis, G.B.: Adaptive diffusions for scalable learning over graphs. IEEE Trans. Signal Process. (2019)

    Google Scholar 

  31. Monti, F., Boscaini, D., Masci, J., et al.: Geometric deep learning on graphs and manifolds using mixture model CNNs. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)

    Google Scholar 

  32. Kip, F.T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks (2016)

    Google Scholar 

  33. Velikovi, P., Cucurull, G., Casanova, A., et al.: Graph Attention Networks (2017)

    Google Scholar 

  34. Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A K-means clustering algorithm. Appl. Statis. 28(1), 100 (1979). https://doi.org/10.2307/2346830

    Article  MATH  Google Scholar 

  35. Taylor, P.D., Day, T., Wild, G.: Evolution of cooperation in a finite homogeneous graph. Nature 447(7143), 469–472 (2007)

    Article  Google Scholar 

  36. Bounova, G., Weck, O.D.: Overview of metrics and their correlation patterns for multiple-metric topology analysis on heterogeneous graph ensembles. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 85(1), 016117 (2011)

    Google Scholar 

  37. Dean, J.A., Wong, K.H., Jones, A.B., Harrington, K.J., Nutting, C.M., Gulliford, S.L.: OC-0257: NTCP models for acute dysphagia resulting from (chemo)radiotherapy for head and neck cancer. Radiotherapy and Oncology 115, S131 (2015)

    Article  Google Scholar 

  38. Asch, V.V.: Macro- and micro-averaged evaluation measures

    Google Scholar 

Download references

Acknowledgement

This work is supported by the National Natural Science Foundation of China (71772033, 71831003), Natural Science Foundation of Liaoning Province, China (Joint Funds for Key Scientific Innovation Bases, 2020-KF-11-11), Scientific Research Project of the Education Department of Liaoning Province, China (LN2019Q14).

Authors’ Contributions. All authors have been contributed equally to this work.

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China

    Ming Gao & Yuchan Li

  2. School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian, China

    Jixiang Yu

  3. Center for Post-Doctoral Studies of Computer Science, Northeastern University, Shenyang, China

    Ming Gao

Authors
  1. Ming Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuchan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jixiang Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ming Gao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Chunxiao Xing

  2. Institute of Computer Science, University of Göttingen, Goettingen, Germany

    Xiaoming Fu

  3. Tsinghua University, Beijing, China

    Yong Zhang

  4. Chinese Academy of Sciences, Beijing, China

    Guigang Zhang

  5. Renmin University of China, Beijing, China

    Chaolemen Borjigin

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gao, M., Li, Y., Yu, J. (2021). Workload Prediction of Cloud Workflow Based on Graph Neural Network. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87571-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87570-1

  • Online ISBN: 978-3-030-87571-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation