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An ensemble CPU load prediction algorithm using a Bayesian information criterion and smooth filters in a cloud computing environment

https://doi.org/10.1002/spe.2641
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers
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CPU load prediction for cloud environment based on a dynamic ensemble model
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Resource requests prediction in the cloud computing environment with a deep belief network
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The Bayesian information criterion: background, derivation, and applications
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Bayesian Information Criteria
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Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing
inferredcites →https://doi.org/10.1007/s11227-015-1603-9openalex-snapshot
Novel heuristics for consolidation of virtual machines in cloud data centers using multi-criteria resource management solutions
inferredcites →https://doi.org/10.1007/s11227-017-2044-4openalex-snapshot
Host load prediction with long short-term memory in cloud computing
inferredcites →https://doi.org/10.1016/j.future.2011.10.014openalex-snapshot
An effective data aggregation based adaptive long term CPU load prediction mechanism on computational grid
inferredcites →https://doi.org/10.1016/j.future.2014.11.008openalex-snapshot
Assessing and forecasting energy efficiency on Cloud computing platforms
inferredcites →https://doi.org/10.1016/j.future.2017.02.022openalex-snapshot
An autonomic resource provisioning approach for service-based cloud applications: A hybrid approach
inferredcites →https://doi.org/10.1016/j.future.2017.09.049openalex-snapshot
A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment
inferredcites →https://doi.org/10.1016/j.future.2018.03.040openalex-snapshot
Predicting host CPU utilization in the cloud using evolutionary neural networks
inferredcites →https://doi.org/10.1016/j.ijpe.2013.04.032openalex-snapshot
Two-dimensional strip packing problem with load balancing, load bearing and multi-drop constraints
inferredcites →https://doi.org/10.1016/j.jnca.2016.03.002openalex-snapshot
Virtual resource prediction in cloud environment: A Bayesian approach
inferredcites →https://doi.org/10.1016/j.jss.2012.12.023openalex-snapshot
A pattern fusion model for multi-step-ahead CPU load prediction
inferredcites →https://doi.org/10.1016/j.neucom.2011.01.009openalex-snapshot
CPU load prediction using neuro-fuzzy and Bayesian inferences
inferredcites →https://doi.org/10.1016/s1005-8885(14)60314-9openalex-snapshot
Host load prediction in cloud based on classification methods
inferredcites →https://doi.org/10.1017/cbo9780511790485openalex-snapshot
Model Selection and Model Averaging
inferredcites →https://doi.org/10.1021/ac60214a047openalex-snapshot
Smoothing and Differentiation of Data by Simplified Least Squares Procedures.
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Dynamically forecasting network performance using the Network Weather Service
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Applied Regression Analysis
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An efficient approach for improving virtual machine placement in cloud computing environment
inferredcites →https://doi.org/10.1109/dsdis.2015.100openalex-snapshot
CPU Load Prediction Based on a Multidimensional Spatial Voting Model
inferredcites →https://doi.org/10.1109/icdcsw.2013.60openalex-snapshot
CPU Load Prediction Using Support Vector Regression and Kalman Smoother for Cloud
inferredcites →https://doi.org/10.1109/ipdps.2003.1213129openalex-snapshot
Homeostatic and tendency-based CPU load predictions
inferredcites →https://doi.org/10.1145/1113361.1113374openalex-snapshot
CoMon
inferredcites →https://doi.org/10.1155/2015/919805openalex-snapshot
Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network
inferredcites →https://doi.org/10.1201/b17805openalex-snapshot
Essentials of Cloud Computing
inferredcites →https://doi.org/10.18637/jss.v027.i03openalex-snapshot
Automatic Time Series Forecasting: The forecast Package for R

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