Machine learning-based management of cloud applications in hybrid clouds: a hadoop case study
| Autor: | Alessandro Pellegrini, Dimiter R. Avresky, Pierangelo Di Sanzo |
|---|---|
| Přispěvatelé: | Avresky Dimiter, R, Pellegrini, Alessandro, DI SANZO, Pierangelo |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Autonomic system
Computer science Best practice availability rejuvenation Cloud computing 02 engineering and technology Machine learning computer.software_genre Software 0202 electrical engineering electronic engineering information engineering cloud Set (psychology) Autonomic systems availability system monitoring hadoop business.industry Scale (chemistry) 020206 networking & telecommunications 020202 computer hardware & architecture system monitoring Work (electrical) Path (graph theory) Artificial intelligence Software rejuvenation business computer |
| Zdroj: | NCA |
| Popis: | This paper illustrates the effort to integrate a machine learning-based framework which can predict the remaining time to failure of computing nodes with Hadoop applications. This work is part of a larger effort targeting the development of a cloud-oriented autonomic framework to increase the availability of applications subject to software anomalies, and to jointly improve their performance. The framework uses machine-learning, software rejuvenation, and load distribution techniques to proactively prevent failures. We believe that this work allows to set a possible path towards the definition of best practices for the development of systems to support autonomic management of cloud applications, illustrating what are the issues that should be addressed by the research community. Indeed, given the scale and the complexity of modern computing infrastructures, effective autonomic management approaches of cloud applications are becoming mandatory. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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