Journal ArticleDOI
Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers
Seyedhamid Mashhadi Moghaddam,Michael O'Sullivan,Cameron Walker,Sareh Fotuhi Piraghaj,Charles P. Unsworth +4 more
TLDR
This paper proposes an energy aware VM consolidation algorithm that minimizes SLAVs and develops different fine-tuned Machine Learning prediction models for individual VMs to predict the best time to trigger migrations from hosts.About:
This article is published in Future Generation Computer Systems.The article was published on 2020-01-18. It has received 26 citations till now. The article focuses on the topics: Live migration & Energy consumption.read more
Citations
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Journal ArticleDOI
A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center
TL;DR: The observed results demonstrate that the proposed integrated approach achieves near-optimal performance against optimal VMP and outperforms rest of the VMPs in terms of power saving and resource utilization up to 88.5% and 21.12% respectively.
Journal ArticleDOI
Energy-efficient VM scheduling based on deep reinforcement learning
Bin Wang,Fagui Liu,Weiwei Lin +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a deep reinforcement learning model based on QoS feature learning to optimize data center resource scheduling, which can effectively reduce the energy consumption of cloud data centers while maintaining the lowest SLA violation rate.
Journal ArticleDOI
A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments
JongBeom Lim,Daewon Lee +1 more
TL;DR: The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way and outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.
Journal ArticleDOI
An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing
TL;DR: The experiment shows that proposed algorithms reduced EC and SLAV in cloud data centers and can be used to construct a smart and sustainable environment for Smart Cities.
Journal ArticleDOI
Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds
TL;DR: In this paper, the problem of energy consumption and efficient resource utilization in virtualized cloud data centers is addressed by using Particle Swarm Optimization (PSO) to select the best schedules.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.