Survey of Machine Learning Algorithms For Dynamic Resource Pricing In Cloud

@article{Kandpal2018SurveyOM,
  title={Survey of Machine Learning Algorithms For Dynamic Resource Pricing In Cloud},
  author={Meetu Kandpal and Kalyani Patel},
  journal={International journal of scientific research in science, engineering and technology},
  year={2018},
  volume={4},
  pages={93-97}
}
  • Meetu Kandpal, Kalyani Patel
  • Published 2018
  • Computer Science
  • International journal of scientific research in science, engineering and technology
The paper provides insights of various machines learning algorithm that could be helpful in deriving the dynamic pricing of resources in cloud. Currently machine learning has impact on many IT and non IT sectors. At the same time because of great change in computing from on premise to cloud computing many big companies has opted cloud computing in which resources are provided on demand basis via internet. On the basis of resource usage machine learning algorithm help to predict the future… Expand

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References

SHOWING 1-10 OF 12 REFERENCES
Cloud Computing Pricing Models: A Survey
TLDR
This work focuses on comparing many employed and proposed pricing models techniques and highlights the pros and cons of each, and finds that most approaches are theoretical and not implemented in the real market, although their simulation results are very promising. Expand
Energy demand forecasting in smart buildings
Energy demand forecasting has become a relevant subject in the energy management field. Different techniques are being currently applied to forecast the energy demand for different time horizonsExpand
An improved ID3 decision tree algorithm
TLDR
The experiment results show that the proposed algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules. Expand
Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN,Expand
An empirical comparison of supervised learning algorithms
TLDR
A large-scale empirical comparison between ten supervised learning methods: SVMs, neural nets, logistic regression, naive bayes, memory-based learning, random forests, decision trees, bagged trees, boosted trees, and boosted stumps is presented. Expand
Support vector machine learning for interdependent and structured output spaces
TLDR
This paper proposes to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs, and demonstrates the versatility and effectiveness of the method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment. Expand
A few useful things to know about machine learning
TLDR
Tapping into the "folk knowledge" needed to advance machine learning applications is a natural next step in the development of artificial intelligence systems. Expand
Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory
Abstract This editorial suggests moving beyond relying on the dominant logic of multiple regression analysis (MRA) toward thinking and using algorithms in advancing and testing theory in accounting,Expand
Introduction to Linear Regression Analysis
  • J. Gray
  • Mathematics, Computer Science
  • Technometrics
  • 2002
TLDR
Elements of Sampling Theory and Methods is unique in its presentation of materials, and the book’s price is reasonable in comparison to the other four books mentioned in this area. Expand
Hidden Markov model
...
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2
...