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. 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.
    2. 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.
    3. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    4. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
    5. Kelly, Scott, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector," Energy, Elsevier, vol. 36(9), pages 5610-5620.
    6. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    7. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    8. Cabrales, Antonio & Serrano, Roberto, 2011. "Implementation in adaptive better-response dynamics: Towards a general theory of bounded rationality in mechanisms," Games and Economic Behavior, Elsevier, vol. 73(2), pages 360-374.
    9. Ting Jin & Rui Xu & Kunqi Su & Jinrui Gao, 2025. "A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction," Mathematics, MDPI, vol. 13(4), pages 1-23, February.
    10. Smith, Peter, 2008. "New perspectives on realism, tractability, and complexity in economics," MPRA Paper 10899, University Library of Munich, Germany.
    11. Turki Alajmi & Patrick Phelan, 2020. "Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach," Energies, MDPI, vol. 13(8), pages 1-19, April.
    12. Anufriev, Mikhail & Arifovic, Jasmina & Donmez, Anil & Ledyard, John & Panchenko, Valentyn, 2025. "IEL-CDA model: A more accurate theory of behavior in continuous double auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 172(C).
    13. Saez, Yago & Mochon, Asuncion & Corona, Luis & Isasi, Pedro, 2019. "Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region," Energy Policy, Elsevier, vol. 132(C), pages 549-566.
    14. Ankit Kumar Srivastava & Ajay Shekhar Pandey & Rajvikram Madurai Elavarasan & Umashankar Subramaniam & Saad Mekhilef & Lucian Mihet-Popa, 2021. "A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 14(24), pages 1-16, December.
    15. Hannah Goozee, 2017. "Energy, poverty and development: a primer for the Sustainable Development Goals," Working Papers 156, International Policy Centre.
    16. Shira Fano & Marco LiCalzi & Paolo Pellizzari, 2013. "Convergence of outcomes and evolution of strategic behavior in double auctions," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 513-538, July.
    17. Valerio Veglio & Rubina Romanello & Torben Pedersen, 2025. "Employee turnover in multinational corporations: a supervised machine learning approach," Review of Managerial Science, Springer, vol. 19(3), pages 687-728, March.
    18. Yuehjen E. Shao & Yi-Shan Tsai, 2018. "Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables," Energies, MDPI, vol. 11(7), pages 1-22, July.
    19. Michiel Leur & Mikhail Anufriev, 2018. "Timing under individual evolutionary learning in a continuous double auction," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 609-631, August.
    20. Chalal, Moulay Larbi & Benachir, Medjdoub & White, Michael & Shahtahmassebi, Golnaz & Cumberbatch, Miranda & Shrahily, Raid, 2017. "The impact of the UK household life-cycle transitions on the electricity and gas usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 505-518.

    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.