Easily implementable time series forecasting techniques for resource provisioning in cloud computing

Michel Fliess (1, 2) , Cédric Join (3, 2) , Maria Bekcheva (4, 5) , Alireza Moradi (4) , Hugues Mounier (5)

1 LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
2 AL.I.E.N. - ALgèbre pour Identification & Estimation Numériques
3 CRAN - Centre de Recherche en Automatique de Nancy
4 Inagral
5 L2S - Laboratoire des signaux et systèmes

Workload predictions in cloud computing is obviously an important topic. Most of the existing publications employ various time series techniques, that might be difficult to implement. We suggest here another route, which has already been successfully used in financial engineering and photovoltaic energy. No mathematical modeling and machine learning procedures are needed. Our computer simulations via realistic data, which are quite convincing, show that a setting mixing algebraic estimation techniques and the daily seasonality behaves much better. An application to the computing resource allocation, via virtual machines, is sketched out.

Domaines: Performance et fiabilité [cs.PF]; Technologies Émergeantes [cs.ET]; Ingénierie, finance et science [cs.CE]; Méthodologie [stat.ME]; Logique [math.LO]; Pricing [q-fin.PR]

Michel Fliess, Cédric Join, Maria Bekcheva, Alireza Moradi, Hugues Mounier. Easily implementable time series forecasting techniques for resource provisioning in cloud computing. 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019, Apr 2019, Paris, France. ⟨hal-02024835v1⟩