Research Article
An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment
@ARTICLE{10.4108/eai.13-7-2018.165501, author={Md. Shahidul Hasan and Balamurugan E and Md. Shawkat Akbar Almamun and Sangeetha K}, title={An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment}, journal={EAI Endorsed Transactions on Cloud Systems}, volume={6}, number={18}, publisher={EAI}, journal_a={CS}, year={2020}, month={6}, 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}, doi={10.4108/eai.13-7-2018.165501} }- Md. Shahidul Hasan
Balamurugan E
Md. Shawkat Akbar Almamun
Sangeetha K
Year: 2020
An Intelligent Machine Learning and Self Adaptive Resource Allocation Framework for Cloud Computing Environment
CS
EAI
DOI: 10.4108/eai.13-7-2018.165501
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.
Copyright © 2020 Md. Shahidul Hasan et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
