ORCID Identifiers
Document Type
Article
Source of Publication
Scientific Programming
Publication Date
10-16-2021
Abstract
In cloud computing, the virtualization technique is a significant technology to optimize the power consumption of the cloud data center. In this generation, most of the services are moving to the cloud resulting in increased load on data centers. As a result, the size of the data center grows and hence there is more energy consumption. To resolve this issue, an efficient optimization algorithm is required for resource allocation. In this work, a hybrid approach for virtual machine allocation based on genetic algorithm (GA) and the random forest (RF) is proposed which belongs to a class of supervised machine learning techniques. The aim of the work is to minimize power consumption while maintaining better load balance among available resources and maximizing resource utilization. The proposed model used a genetic algorithm to generate a training dataset for the random forest model and further get a trained model. The real-time workload traces from PlanetLab are used to evaluate the approach. The results showed that the proposed GA-RF model improves energy consumption, execution time, and resource utilization of the data center and hosts as compared to the existing models. The work used power consumption, execution time, resource utilization, average start time, and average finish time as performance metrics.
DOI Link
Publisher
Hindawi
Volume
2021
Disciplines
Computer Sciences
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
S, Madhusudhan H; Kumar T, Satish; Mustapha, S.M.F D Syed; Gupta, Punit; and Tripathi, Rajan Prasad, "Hybrid Approach for Resource Allocation in Cloud Infrastructure Using Random Forest and Genetic Algorithm" (2021). All Works. 4639.
https://zuscholars.zu.ac.ae/works/4639
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series