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Technical Study of Deep Learning in Cloud Computing for Accurate Workload Prediction

https://doi.org/10.3390/electronics12030650

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What this work says

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CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

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Encyclopedia of Machine Learning

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A survey and classification of the workload forecasting methods in cloud computing

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RePro-Active: a reactive–proactive scheduling method based on simulation in cloud computing

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E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction

inferredcites →https://doi.org/10.1016/j.compeleceng.2015.07.021openalex-snapshot

Resource management in cloud computing: Taxonomy, prospects, and challenges

inferredcites →https://doi.org/10.1016/j.future.2017.10.047openalex-snapshot

Workload prediction in cloud using artificial neural network and adaptive differential evolution

inferredcites →https://doi.org/10.1016/j.ijforecast.2004.10.001openalex-snapshot

The accuracy of intermittent demand estimates

inferredcites →https://doi.org/10.1016/j.ins.2018.12.027openalex-snapshot

SGW-SCN: An integrated machine learning approach for workload forecasting in geo-distributed cloud data centers

inferredcites →https://doi.org/10.1016/j.jnca.2017.01.016openalex-snapshot

Survey on prediction models of applications for resources provisioning in cloud

inferredcites →https://doi.org/10.1016/j.jpdc.2014.06.013openalex-snapshot

Experience with using the Parallel Workloads Archive

inferredcites →https://doi.org/10.1016/j.jpdc.2017.10.009openalex-snapshot

A learning automata-based algorithm for energy and SLA efficient consolidation of virtual machines in cloud data centers

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Holistic resource management for sustainable and reliable cloud computing: An innovative solution to global challenge

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A review on prediction based autoscaling techniques for heterogeneous applications in cloud environment

inferredcites →https://doi.org/10.1016/j.neucom.2015.12.114openalex-snapshot

Mean Absolute Percentage Error for regression models

inferredcites →https://doi.org/10.1016/j.procs.2017.12.087openalex-snapshot

Long Short Term Memory Recurrent Neural Network (LSTM-RNN) Based Workload Forecasting Model For Cloud Datacenters

inferredcites →https://doi.org/10.1016/j.trechm.2020.12.004openalex-snapshot

Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and Best Practices for Machine Learning in Chemistry

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Accuracy in Parameter Estimation for the Root Mean Square Error of Approximation: Sample Size Planning for Narrow Confidence Intervals

inferredcites →https://doi.org/10.1109/access.2019.2905634openalex-snapshot

Large-Scale Computing Systems Workload Prediction Using Parallel Improved LSTM Neural Network

inferredcites →https://doi.org/10.1109/confluence47617.2020.9057938openalex-snapshot

A Literature Review and Taxonomy on Workload Prediction in Cloud Data Center

inferredcites →https://doi.org/10.1109/hpcc-smartcity-dss.2016.0162openalex-snapshot

Use of Reactive and Proactive Elasticity to Adjust Resources Provisioning in the Cloud Provider

inferredcites →https://doi.org/10.1109/icccn49398.2020.9209730openalex-snapshot

Machine Learning Based Workload Prediction in Cloud Computing

inferredcites →https://doi.org/10.1109/icnsc.2019.8743188openalex-snapshot

Deep Neural Networks for Predicting Task Time Series in Cloud Computing Systems

inferredcites →https://doi.org/10.1109/ipdps.2006.1639311openalex-snapshot

Instability in parallel job scheduling simulation: the role of workload flurries

inferredcites →https://doi.org/10.1109/ispass.2006.1620806openalex-snapshot

Workload sanitation for performance evaluation

inferredcites →https://doi.org/10.1109/snpd.2016.7515919openalex-snapshot

A deep learning approach for VM workload prediction in the cloud

inferredcites →https://doi.org/10.1109/tcc.2014.2350475openalex-snapshot

Workload Prediction Using ARIMA Model and Its Impact on Cloud Applications’ QoS

inferredcites →https://doi.org/10.1109/tii.2018.2808910openalex-snapshot

An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

inferredcites →https://doi.org/10.1109/tpds.2019.2953745openalex-snapshot

Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning

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Paragon

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Resource Central

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2nd International Workshop on Data Quality Assessment for Machine Learning

inferredcites →https://doi.org/10.1145/3447548.3470817openalex-snapshot

Data Quality for Machine Learning Tasks

inferredcites →https://doi.org/10.1186/s13638-019-1605-zopenalex-snapshot

A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment

inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot

Scikit-learn: Machine Learning in Python

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Predictive Analytics and Optimization of Cloud Computing Performance Using Deep Learning

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Improving cloud resource management: an ensemble learning approach for workload prediction

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A comprehensive survey and taxonomy of workload forecasting techniques in cloud computing

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From reactive to predictive: A pattern-Aware framework for kubernetes autoscaling with large language model integration

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Forecasting CPU and Memory Usage in Cloud Workloads Using Task Metadata: A Machine Learning Approach on Google Cluster Data

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MSTH-Former: Optimizing Workload Prediction in Edge-Cloud Continuum with Multi-Scale Temporal and Hierarchical Knowledge Convergence and Distillation

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Machine Learning-Driven Resource Utilization Prediction for Scalable Cloud Infrastructure Management

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Navigating Spatio-Temporal Uncertainty: Adaptive Cloud Resource Management Based on Quantile Central Limit Theorem and Kalman Filtering

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AS-ODB: Multivariate Attention Supervised Learning Based Optimized DBN Approach for Cloud Workload Prediction

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