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Journal of IoT in Social, Mobile, Analytics, and Cloud

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Live Streaming Architectures for Video Data - A Review
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An Effective Approach of IIoT for Anomaly Detection Using Unsupervised Machine Learning Approach
Volume-4 | Issue-3

Accurate Prediction of Workflow using Dual-Stage Learning to Reduce Task Execution Time
Volume-4 | Issue-4

Hybrid Intrusion Detection System for Internet of Things (IoT)
Volume-2 | Issue-4

Design of Deep Learning Algorithm for IoT Application by Image based Recognition
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Volume - 4 | Issue - 4 | december 2022

Accurate Prediction of Workflow using Dual-Stage Learning to Reduce Task Execution Time
N. Bhalaji   85  256
Pages: 244-256
Cite this article
Bhalaji, N. (2022). Accurate Prediction of Workflow using Dual-Stage Learning to Reduce Task Execution Time. Journal of IoT in Social, Mobile, Analytics, and Cloud, 4(4), 244-256. doi:10.36548/jismac.2022.4.002
Published
11 November, 2022
Abstract

As the number of cloud data centres continues to expand rapidly, one of the biggest worries is how to keep up with the energy demands of all these new servers without negatively impacting system dependability and availability or raising the price of power for service providers. Workflow task performance prediction for variable input data is crucial to several methods, including scheduling and resource provisioning. However, it is challenging to create such estimations in the cloud. The suggested system's two-stage forecasts and parameters that account for runtime data, allow for very precise predictions. The workflow is smooth, and obviously the task execution time is adequate. This strategy beats the state-of-the-art prediction techniques, as shown by empirical data. It is demonstrated that the models of this form, predicting workflow for a given cloud, can be easily transferred to other clouds with little effort and error.

Keywords

Energy consumption execution time prediction learning algorithm cloud computing fog computing run time

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