A systematic review on effective energy utilization management strategies in cloud data centers
Published 2022 View Full Article
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Title
A systematic review on effective energy utilization management strategies in cloud data centers
Authors
Keywords
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Journal
Journal of Cloud Computing-Advances Systems and Applications
Volume 11, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-12-17
DOI
10.1186/s13677-022-00368-5
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Note: Only part of the references are listed.- A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques
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- (2022) İlksen Çağlar et al. Journal of Cloud Computing-Advances Systems and Applications
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- (2021) Varun Barthwal et al. Memetic Computing
- The Use of Blockchain Technology in Public Sector Entities Management: An Example of Security and Energy Efficiency in Cloud Computing Data Processing
- (2021) Robert Karaszewski et al. Energies
- Comprehensive survey on energy-aware server consolidation techniques in cloud computing
- (2021) Nisha Chaurasia et al. JOURNAL OF SUPERCOMPUTING
- An energy-efficient cuckoo search algorithm for virtual machine placement in cloud computing data centers
- (2021) Hamza Onoruoiza Salami et al. JOURNAL OF SUPERCOMPUTING
- Energy efficient virtual machine migration approach with SLA conservation in cloud computing
- (2021) Vaneet Garg et al. Journal of Central South University
- A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers
- (2021) Monireh H. Sayadnavard et al. Engineering Science and Technology-An International Journal-JESTECH
- LBPSGORA: Create Load Balancing with Particle Swarm Genetic Optimization Algorithm to Improve Resource Allocation and Energy Consumption in Clouds Networks
- (2021) Seyedeh Maedeh Mirmohseni et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds
- (2021) Nimra Malik et al. Applied Sciences-Basel
- BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting
- (2020) Jitendra Kumar et al. SOFT COMPUTING
- An efficient power-aware VM allocation mechanism in cloud data centers: a micro genetic-based approach
- (2020) Mehran Tarahomi et al. Cluster Computing-The Journal of Networks Software Tools and Applications
- PCVM.ARIMA: predictive consolidation of virtual machines applying ARIMA method
- (2020) Maryam Chehelgerdi-Samani et al. JOURNAL OF SUPERCOMPUTING
- Energy efficiency in cloud computing based on mixture power spectral density prediction
- (2020) Dinh-Mao Bui et al. JOURNAL OF SUPERCOMPUTING
- Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers
- (2020) Xialin Liu et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers
- (2020) Fares Alharbi et al. Cluster Computing
- EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres
- (2020) Saikishor Jangiti et al. Sustainable Computing-Informatics & Systems
- A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center
- (2020) Deepika Saxena et al. NEUROCOMPUTING
- Energy-efficient migration techniques for cloud environment: a step toward green computing
- (2019) Srimoyee Bhattacherjee et al. JOURNAL OF SUPERCOMPUTING
- A comprehensive survey for scheduling techniques in cloud computing
- (2019) Mohit Kumar et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Predicting host CPU utilization in the cloud using evolutionary neural networks
- (2018) Karl Mason et al. Future Generation Computer Systems-The International Journal of eScience
- Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms
- (2018) Zhou Zhou et al. Future Generation Computer Systems-The International Journal of eScience
- Self managed virtual machine scheduling in Cloud systems
- (2018) Stelios Sotiriadis et al. INFORMATION SCIENCES
- A learning-based approach for virtual machine placement in cloud data centers
- (2018) Mostafa Ghobaei-Arani et al. INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
- Energy-Aware Virtual Machine Consolidation Algorithm Based on Ant Colony System
- (2018) Azra Aryania et al. Journal of Grid Computing
- GreenSched: An intelligent energy aware scheduling for deadline-and-budget constrained cloud tasks
- (2018) Tarandeep Kaur et al. SIMULATION MODELLING PRACTICE AND THEORY
- Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters
- (2018) Hui Wang et al. IEEE Access
- Adaptive Resource Utilization Prediction System for Infrastructure as a Service Cloud
- (2017) Qazi Zia Ullah et al. Computational Intelligence and Neuroscience
- Energy-Efficient Algorithms for Dynamic Virtual Machine Consolidation in Cloud Data Centers
- (2017) Mohammad Ali Khoshkholghi et al. IEEE Access
- A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges
- (2016) Sukhpal Singh et al. Journal of Grid Computing
- Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends
- (2016) Alireza Sadeghi Milani et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Energy efficiency of VM consolidation in IaaS clouds
- (2016) Fei Teng et al. JOURNAL OF SUPERCOMPUTING
- Cloud resource provisioning: survey, status and future research directions
- (2016) Sukhpal Singh et al. KNOWLEDGE AND INFORMATION SYSTEMS
- A reinforcement learning approach for the scheduling of live migration from under utilised hosts
- (2016) Martin Duggan et al. Memetic Computing
- Energy Efficiency Techniques in Cloud Computing
- (2015) Tarandeep Kaur et al. ACM COMPUTING SURVEYS
- Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers
- (2011) Anton Beloglazov et al. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
- Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
- (2011) Anton Beloglazov et al. Future Generation Computer Systems-The International Journal of eScience
- Empirical prediction models for adaptive resource provisioning in the cloud
- (2011) Sadeka Islam et al. Future Generation Computer Systems-The International Journal of eScience
- URL: A unified reinforcement learning approach for autonomic cloud management
- (2011) Cheng-Zhong Xu et al. JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
- The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks
- (2011) George Kousiouris et al. JOURNAL OF SYSTEMS AND SOFTWARE
- Energy-efficient algorithms
- (2010) Susanne Albers COMMUNICATIONS OF THE ACM
- CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
- (2010) Rodrigo N. Calheiros et al. SOFTWARE-PRACTICE & EXPERIENCE
- Steps toward self-aware networks
- (2009) Erol Gelenbe COMMUNICATIONS OF THE ACM
- Energy-Efficient Cloud Computing
- (2009) A. Berl et al. COMPUTER JOURNAL
- Virtual Infrastructure Management in Private and Hybrid Clouds
- (2009) Borja Sotomayor et al. IEEE INTERNET COMPUTING
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