Double Auction used Artificial Neural Network in Cloud Computing

Muhammad Adeel Abbasa () and Zeshan Iqbal
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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

International Journal of Innovations in Science & Technology, 2022, vol. 4, issue 5, 65-76

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.

Keywords: Cloud Computing; Double Auction; Artificial Neural Network; Regression Problem; Supervised Machine Learning (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:abq:ijist1:v:4:y:2022:i:5:p:65-76

DOI: 10.33411/IJIST/2022040506

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