Abstract
Workflow execution employs predictive analytics to extract significant, unidentified as well as precious insights from several stages of execution. Further, the operational analytics integrates these valuable insights directly into decision engine which enables analytical as well as machine learning-driven decision-making for an efficient workflow execution. This chapter highlights several analytical and machine learning approaches that are practiced in workflow predictions. Additionally, it explains the significance of hybrid approach which includes both analytical and machine learning models for workflow prediction. Finally, it describes the hybrid approach employed in PANORAMA architecture using two workflow applications.
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Kousalya, G., Balakrishnan, P., Pethuru Raj, C. (2017). Workflow Predictions Through Operational Analytics and Machine Learning. In: Automated Workflow Scheduling in Self-Adaptive Clouds. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-56982-6_7
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DOI: https://doi.org/10.1007/978-3-319-56982-6_7
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