Dynamic Virtual Machine Consolidation Algorithms for Energy-Efficient Cloud Resource Management: A Review

Springer eBooks, Journal Year: 2017, Volume and Issue: unknown, P. 135 - 165

Published: Sept. 20, 2017

Language: Английский

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Optimizing virtual machine placement in IaaS data centers: taxonomy, review and open issues

Cluster Computing, Journal Year: 2019, Volume and Issue: 23(2), P. 837 - 878

Published: July 27, 2019

Language: Английский

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A whale optimization system for energy-efficient container placement in data centers

Expert Systems with Applications, Journal Year: 2020, Volume and Issue: 164, P. 113719 - 113719

Published: July 29, 2020

Language: Английский

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Power consumption prediction in cloud data center using machine learning

International Journal of Electrical and Computer Engineering (IJECE), Journal Year: 2020, Volume and Issue: 10(2), P. 1524 - 1524

Published: March 8, 2020

The flourishing development of the cloud computing paradigm provides several services in industrial business world. Power consumption by data centers is one crucial issues for service providers domain computing. Pursuant to rapid technology enhancements environments and augmentations, power utilization expected grow unabated. A diverse set numerous connected devices, engaged with ubiquitous cloud, results unprecedented centers, accompanied increased carbon footprints. Nearly a million physical machines (PM) are running all over along (5 – 6) virtual (VM). In next five years, needs this spiral up 5% global production. machine reduction impacts diminishing PM’s power, however further changing center year year, aid vendors using prediction methods. sudden fluctuation will cause outage centers. This paper aims forecast VM help regressive predictive analysis, Machine Learning (ML) techniques. potency approach make better predictions future value, Multi-layer Perceptron (MLP) regressor which 91% accuracy during process.

Language: Английский

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Model Predictive Control for Energy-Efficient, Quality-Aware, and Secure Virtual Machine Placement

IEEE Transactions on Automation Science and Engineering, Journal Year: 2018, Volume and Issue: 16(1), P. 420 - 432

Published: May 7, 2018

Modern datacenters rely on virtualization to deliver complex and scalable cloud services. To avoid inflating costs or reducing the perceived service level, suitable resource optimization techniques are needed. Placement can be used prevent inefficient maps between virtual physical machines. In this perspective, we propose a holistic placement framework considering conflicting performance metrics, such as level delivered by cloud, energetic footprint, hardware software outages, security policies. Unfortunately, computing best strategies is nontrivial, it requires ability trade among several goals, possibly in real-time manner. Therefore, approach problem via model predictive control devise optimal Results show effectiveness of our technique comparison with classical heuristics.

Language: Английский

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HOTS: A Containers Resource Allocation Hybrid Method Using Machine Learning and Optimization

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 14 - 27

Published: Jan. 1, 2025

Language: Английский

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Task-Driven Virtual Machine Optimization Placement Model and Algorithm

Future Internet, Journal Year: 2025, Volume and Issue: 17(2), P. 73 - 73

Published: Feb. 7, 2025

In cloud data centers, determining how to balance the interests of user and service provider is a challenging issue. this study, task-loading-oriented virtual machine (VM) optimization placement model algorithm proposed integrating consideration both VM user’s computing requirements. First, modeled as multi-objective problem minimize makespan loading tasks, rental costs, energy consumption centers; then, an improved chaos-elite NSGA-III (CE-NSGAIII) presented by casting logistic mapping-based population initialization (LMPI) elite-guided in NSGA-III; finally, CE-NSGAIII employed solve aforementioned model, further, through combination above sub-algorithms, CE-NSGAIII-based method developed. The experiment results show that Pareto solution set obtained using exhibits better convergence diversity than those compared algorithms yields optimized scheme with shorter makespan, less lower consumption.

Language: Английский

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An efficient prediction-based dynamic resource allocation framework in quantum cloud using knowledge-based offline reinforcement learning

Quantum Machine Intelligence, Journal Year: 2025, Volume and Issue: 7(1)

Published: March 5, 2025

Language: Английский

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An ILP-based exact approach for solving the variable cost and size bin packing problem with time-dependent cost modeling the shared satellite-based last-mile delivery

Computers & Operations Research, Journal Year: 2025, Volume and Issue: unknown, P. 107075 - 107075

Published: March 1, 2025

Language: Английский

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An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization

International Journal of Distributed Sensor Networks, Journal Year: 2018, Volume and Issue: 14(12), P. 155014771879379 - 155014771879379

Published: Dec. 1, 2018

With the rapid development of information technologies and popularization Internet applications, more companies developers pay great attention to cloud computing. As one most significant problems in computing, virtual machine allocation has attracted attention. However, early studies usually ignore load balance issue resources. In this article, we aim at multidimensional resource balancing all physical machines computing platform maximize utilization To achieve goal, leverage ant colony optimization design an efficient algorithm based on NP-hard feature problem. Specifically, customize context introduce improved selection strategy basic order prevent premature convergence or falling into local optima. Through extensive simulations, demonstrate that our proposed can effectively improve for platform.

Language: Английский

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