IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v4y2022i5p65-76.html

Double Auction used Artificial Neural Network in Cloud Computing

Author

Listed:
  • Muhammad Adeel Abbasa

    (Department of Computer Science University of Engineering and Technology Taxila, Pakistan)

  • Zeshan Iqbal

    (Department of Computer Science University of Engineering and Technology Taxila, Pakistan)

Abstract

Double auction (DA) algorithm is widely used for trading systems in cloud computing. Distinct buyers request different attributes for virtual machines. On the other hand, different sellers offer several types of virtual machines according to their correspondence bids. In DA, getting multiple equilibrium prices from distinct cloud providers is a difficult task, and one of the major problems is bidding prices for virtual machines, so we cannot make decisions with inconsistent data. To solve this problem, we need to find the best machine learning algorithm that anticipates the bid cost for virtual machines. Analyzing the performance of DA algorithm with machine learning algorithms is to predict the bidding price for both buyers and sellers. Therefore, we have implemented several machine learning algorithms and observed their performance on the bases of accuracy, such as linear regression (83%), decision tree regressor (77%), random forest (82%), gradient boosting (81%), and support vector regressor (90%). In the end, we observed that the Artificial Neural Network (ANN) provided an astonishing result. ANN has provided 97% accuracy in predicting bidding prices in DA compared to all other learning algorithms. It reduced the wastage of resources (VMs attributes) and soared both users' profits (buyers & sellers). Different types of models were analyzed on the bases of individual parameters such as accuracy. In the end, we found that ANN is effective and valuable for bidding prices for both users.

Suggested Citation

  • Muhammad Adeel Abbasa & Zeshan Iqbal, 2022. "Double Auction used Artificial Neural Network in Cloud Computing," International Journal of Innovations in Science & Technology, 50sea, vol. 4(5), pages 65-76, June.
    • Handle: RePEc:abq:ijist1:v:4:y:2022:i:5:p:65-76
    DOI: 10.33411/IJIST/2022040506
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/328/251
    Download Restriction: no
    File URL: https://journal.50sea.com/index.php/IJIST/article/view/328
    Download Restriction: no
    File URL: https://libkey.io/10.33411/IJIST/2022040506?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dawid, Herbert, 1999. "On the convergence of genetic learning in a double auction market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1545-1567, September.
    2. Iqra Khan & Muhammad Zohaib Siddique & Ateeq Ur Rehman Butt & AZHAR IMRAN Mudassir & Muhammad Azeem Qadir & Sundus Munir, 2021. "Towards Skin Cancer Classification Using Machine Learning And Deep Learning Algorithms: A Comparison," International Journal of Innovations in Science & Technology, 50sea, vol. 3(4), pages 110-118, December.
    3. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fernando Venâncio Mucomole & Carlos Augusto Santos Silva & Lourenço Lázaro Magaia, 2025. "Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review," Energies, MDPI, vol. 18(6), pages 1-51, March.
    2. Nedelcu Bogdan, 2017. "Human talent forecasting," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 11(1), pages 437-447, July.
    3. Al-Ghandoor, A. & Al-Hinti, I. & Jaber, J.O. & Sawalha, S.A., 2008. "Electricity consumption and associated GHG emissions of the Jordanian industrial sector: Empirical analysis and future projection," Energy Policy, Elsevier, vol. 36(1), pages 258-267, January.
    4. Naiming Xie & Alan Pearman, 2014. "Forecasting energy consumption in China following instigation of an energy-saving policy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 639-659, November.
    5. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    6. Jihoon Moon & Yongsung Kim & Minjae Son & Eenjun Hwang, 2018. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron," Energies, MDPI, vol. 11(12), pages 1-20, November.
    7. Shwet Ketu, 2022. "Spatial Air Quality Index and Air Pollutant Concentration prediction using Linear Regression based Recursive Feature Elimination with Random Forest Regression (RFERF): a case study in India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 2109-2138, November.
    8. Wu, Xuedong & Mao, Jianxu & Du, Zhaoping & Chang, Yanchao, 2013. "Online training algorithms based single multiplicative neuron model for energy consumption forecasting," Energy, Elsevier, vol. 59(C), pages 126-132.
    9. Shira Fano & Paolo Pellizzari, 2011. "Time-Dependent Trading Strategies in a Continuous Double Auction," Lecture Notes in Economics and Mathematical Systems, in: Sjoukje Osinga & Gert Jan Hofstede & Tim Verwaart (ed.), Emergent Results of Artificial Economics, pages 165-176, Springer.
    10. Constantine Kontokosta, 2015. "A Market-Specific Methodology for a Commercial Building Energy Performance Index," The Journal of Real Estate Finance and Economics, Springer, vol. 51(2), pages 288-316, August.
    11. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    12. Jun Hao & Xiaolei Sun & Qianqian Feng, 2020. "A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 13(3), pages 1-25, January.
    13. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    14. Hannah Goozee, 2017. "Energy, Poverty and Development: A Primer for the Sustainable Development Goals," Working Papers id:11933, eSocialSciences.
    15. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    16. Zhengchao Xie & Xiao Wang & Lijun Zheng & Hao Chang & Fei Wang, 2022. "A Period-Based Neural Network Algorithm for Predicting Building Energy Consumption of District Heating," Energies, MDPI, vol. 15(17), pages 1-22, August.
    17. Wang, Shuai & Yu, Lean & Tang, Ling & Wang, Shouyang, 2011. "A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China," Energy, Elsevier, vol. 36(11), pages 6542-6554.
    18. Voyant, Cyril & Motte, Fabrice & Notton, Gilles & Fouilloy, Alexis & Nivet, Marie-Laure & Duchaud, Jean-Laurent, 2018. "Prediction intervals for global solar irradiation forecasting using regression trees methods," Renewable Energy, Elsevier, vol. 126(C), pages 332-340.
    19. Andrea Vieri & Agostino Gambarotta & Mirko Morini & Costanza Saletti, 2024. "An Integrated Artificial Intelligence Approach for Building Energy Demand Forecasting," Energies, MDPI, vol. 17(19), pages 1-28, October.
    20. Juan D. Montoro-Pons, 2000. "Collective Action, Free Riding And Evolution," Computing in Economics and Finance 2000 279, Society for Computational Economics.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abq:ijist1:v:4:y:2022:i:5:p:65-76. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Iqra Nazeer (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.