University of Bahrain
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A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications

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dc.contributor.author Mateen, Muhammad
dc.contributor.author Nasrullah
dc.contributor.author Hayat, Shaukat
dc.contributor.author Tehreem, Tooba
dc.contributor.author Akbar, Muhammad Azeem
dc.date.accessioned 2020-04-29T22:23:48Z
dc.date.available 2020-04-29T22:23:48Z
dc.date.issued 2020-05-01
dc.identifier.uri https://journal.uob.edu.bh:443/handle/123456789/3817
dc.description.abstract The resource adaptation on demand is an important factor in the field of cloud computing. During runtime, autonomic resource provisioning is not an easy task to choose the accurate amount of resources for service-based cloud applications. For this reason, it is required to guess the future demands for self-adaptive resources to deal with the irregular requests based on runtime workload changes of service-based cloud applications. In this paper, an efficient approach to increase the utilization of resources is proposed that is based on self-adaptive computing with fuzzy logic. Additionally, the proposed fuzzy logic approach enhanced the performance of planning phase for better decision making. Based on fuzzy logic, cloud applications having self-learning provisioning resources outperformed the hybrid resource provisioning approach. To calculate the quality of the proposed technique, real-world ClarkNet and NASA workload traces are used. The results of experiments show that the proposed technique has decreased the entire cost and has boosted the utilization of resources as compared to the other contemporary techniques. en_US
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Autonomic computing en_US
dc.subject Cloud computing en_US
dc.subject Fuzzy logic en_US
dc.subject Resource adaptation en_US
dc.title A Self-Adaptive Resource Provisioning Approach using Fuzzy Logic for Cloud-Based Applications en_US
dc.identifier.doi http: //dx.doi.org/10.12785/ijcds/090301
dc.volume Volume 09 en_US
dc.issue Issue 03 en_US
dc.contributor.authorcountry China en_US
dc.contributor.authorcountry China en_US
dc.contributor.authorcountry China en_US
dc.contributor.authorcountry Pakistan en_US
dc.contributor.authorcountry China en_US
dc.contributor.authoraffiliation School of Big Data & Software Engineering, Chongqing University, Chongqing, China en_US
dc.contributor.authoraffiliation School of Big Data & Software Engineering, Chongqing University, Chongqing, China en_US
dc.contributor.authoraffiliation School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China en_US
dc.contributor.authoraffiliation School of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan en_US
dc.contributor.authoraffiliation 1School of Big Data & Software Engineering, Chongqing University, Chongqing, China en_US
dc.source.title International Journal of Computing and Digital Systems en_US


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