A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning
Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/17/5322 |
| _version_ | 1797521456466231296 |
|---|---|
| author | Stanly Jayaprakash Manikanda Devarajan Nagarajan Rocío Pérez de Prado Sugumaran Subramanian Parameshachari Bidare Divakarachari |
| author_facet | Stanly Jayaprakash Manikanda Devarajan Nagarajan Rocío Pérez de Prado Sugumaran Subramanian Parameshachari Bidare Divakarachari |
| author_sort | Stanly Jayaprakash |
| collection | DOAJ |
| description | Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting. |
| first_indexed | 2024-03-10T08:12:52Z |
| format | Article |
| id | doaj.art-9fa158c1642a466b8eec2b5e25c97500 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:12:52Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-9fa158c1642a466b8eec2b5e25c975002023-11-22T10:32:57ZengMDPI AGEnergies1996-10732021-08-011417532210.3390/en14175322A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine LearningStanly Jayaprakash0Manikanda Devarajan Nagarajan1Rocío Pérez de Prado2Sugumaran Subramanian3Parameshachari Bidare Divakarachari4Department of CSE, Mahendra Institute of Technology, Namakkal 637503, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, Malla Reddy Engineering College (Autonomous), Secunderabad 500100, Telangana, IndiaTelecommunication Engineering Department, University of Jaén, 23700 Jaén, SpainDepartment of ECE, Vishnu Institute of Technology, Bimavaram 534202, Andhra Pradesh, IndiaDepartment of Telecommunication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570016, Karnataka, IndiaNowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.https://www.mdpi.com/1996-1073/14/17/5322cloud data centersmachine learningclustering methodsoptimizationenergy consumptionvirtual machines |
| spellingShingle | Stanly Jayaprakash Manikanda Devarajan Nagarajan Rocío Pérez de Prado Sugumaran Subramanian Parameshachari Bidare Divakarachari A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning Energies cloud data centers machine learning clustering methods optimization energy consumption virtual machines |
| title | A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning |
| title_full | A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning |
| title_fullStr | A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning |
| title_full_unstemmed | A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning |
| title_short | A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning |
| title_sort | systematic review of energy management strategies for resource allocation in the cloud clustering optimization and machine learning |
| topic | cloud data centers machine learning clustering methods optimization energy consumption virtual machines |
| url | https://www.mdpi.com/1996-1073/14/17/5322 |
| work_keys_str_mv | AT stanlyjayaprakash asystematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT manikandadevarajannagarajan asystematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT rocioperezdeprado asystematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT sugumaransubramanian asystematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT parameshacharibidaredivakarachari asystematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT stanlyjayaprakash systematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT manikandadevarajannagarajan systematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT rocioperezdeprado systematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT sugumaransubramanian systematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning AT parameshacharibidaredivakarachari systematicreviewofenergymanagementstrategiesforresourceallocationinthecloudclusteringoptimizationandmachinelearning |