A Review On Estimating Cloud Performance Metrics Using Machine Learning And Deep Learning Models

• Author(s):

  Surbhi Jhariya | Prof. Pawan Panchole

• Keywords:

  Cloud Computing, Service-level Agreements (SLAs). Regression Learning, Neural Network, MAPE.

• Abstract:

  Data Driven Cloud Computing Model Have Resulted In Unprecedented Paradigm Shifts In Cloud Application Development. Many Applications Have Found Data Driven Cloud Computing Models Indispensable Due To The Need For High Performance Computing. Performance Prediction Is Essential For Both Cloud Service Providers And Users. Providers Rely On Accurate Predictions To Manage Resources Effectively, Prevent Over-provisioning Or Under-provisioning, And Maintain Service-level Agreements (SLAs). Users, On The Other Hand, Benefit From Performance Prediction When Selecting Cloud Services That Meet Their Application Requirements. Inadequate Performance Prediction Can Lead To Increased Operational Costs, Degraded Service Quality, And Customer Dissatisfaction. Thus, Robust Prediction Mechanisms Are Indispensable In Ensuring The Efficient Operation Of Cloud Systems. This Work Presents A Regression Learning Based Model For Performance Prediction In Cloud Environments. This Paper Presents A Review On The Contemporary Machine Learning And Deep Learning Models For Estimating Cloud Performance Metrics.

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Other Details

• Paper id:

  IJSARTV12I4104888

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-06

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