Workload Characterization Prediction through Hybrid model

Author(s)	Mr. Aniket Dattatrey Deshmukh, Dr. N S Bagal
Country	India
Abstract	An all-inclusive approach to workload characterisation that makes use of supervised and unsupervised deep learning methods to optimize system performance and resource usage. In order to uncover latent workload patterns, the suggested method combines data preprocessing with feature selection using Pearson correlation and grouping through Fuzzy C-Means. For precise prediction, a CNN-LSTM hybrid model is used to grasp the temporal and geographical dependencies in the workload data. The approach works well with dynamic, high-dimensional datasets that are typical in distributed and cloud-based settings. Optimal work allocation and balanced resource distribution are also achieved by use of the Hungarian algorithm. Clustering and deep learning, when combined, increase the precision of predictions and the flexibility of the system. Efficient workload scheduling and intelligent decision-making are supported by this system. Results from experiments show that, compared to more conventional approaches, this one is more scalable and performs better overall. Data centers, edge systems, and cloud computing are all good places to use the suggested system.
Keywords	Workload Characterization, Performance Analysis, Feature Extraction, Performance Analysis, Machine learning (ML).
Field	Computer > Data / Information
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-14

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International Journal For Multidisciplinary Research