An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment

Hasan, Md. and E, Balamurugan and Almamun, Md. Shawkat Akbar and K, Sangeetha (2020) An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment. EAI Endorsed Transactions on Cloud Systems, 6 (18): e2. ISSN 2410-6895

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Abstract

Resource allocation is one of the major concern in cloud computing model. When several problems exists in rendering a useful resource allocator. In this research, a self adaptive resource allocation frame work based on machine learning is proposed for modelling and analysing the problem of multi-dimensional cloud resource. This novel self-adaptive resource allocation architecture consists of three stages, QoS prediction model, Improved Bat Algorithm (IBA) and Energy Efficient Model (EEM). The first one, the QoS prediction model, which depends on the same scale of system’s past events data, can attain a comparable accuracy with regard to QoS prediction. Secondly, an Energy Efficient Model, which is based on Modified Clonal Selection Algorithm (MCSA) is introduced for minimizing the energy depletion. Thirdly, a runtime decision-making algorithm that depends on improved bat algorithm can rapidly decide on a suitable function for resource allocation.

Item Type: Article
Uncontrolled Keywords: Cloud Computing, Resource Allocation, QoS prediction model, Improved Bat Algorithm (IBA), Energy Efficient Model (EEM), Modified Clonal Selection Algorithm (MCSA), Enhanced Recurrent Neural Network
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 09 Sep 2020 11:52
Last Modified: 09 Sep 2020 11:52
URI: https://eprints.eudl.eu/id/eprint/134

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