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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

7. Workflow Predictions Through Operational Analytics and Machine Learning

verfasst von : G. Kousalya, P. Balakrishnan, C. Pethuru Raj

Erschienen in: Automated Workflow Scheduling in Self-Adaptive Clouds

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Execution of Workflow Scheduling in Cloud Middleware
Nächstes Kapitel Workflow Integration and Orchestration, Opportunities and the Challenges
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Metadaten
Titel
Workflow Predictions Through Operational Analytics and Machine Learning
verfasst von
G. Kousalya
P. Balakrishnan
C. Pethuru Raj
Copyright-Jahr
2017
Buch
Automated Workflow Scheduling in Self-Adaptive Clouds

Print ISBN: 978-3-319-56981-9

Electronic ISBN: 978-3-319-56982-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-56982-6_7