Authors:
Rafael Bastos
1
;
Vagner Seibert
1
;
Giovani Maia
1
;
Bruno P. de Moura
2
;
Giancarlo Lucca
3
;
Adenauer Yamin
1
and
Renata Reiser
1
Affiliations:
1
Federal University of Pelotas (UFPel/PPGC), Pelotas, Brazil
;
2
Federal University of Pampa (Unipampa), Bagé, Brazil
;
3
Catholic University of Pelotas (UCPel), Pelotas, Brazil
Keyword(s):
Fuzzy Logic, Server Consolidation, Feature Selection, Fuzzy Rule Learning.
Abstract:
The present work addresses the challenges of flexible resource management in Cloud Computing, emphasizing the critical need for efficient resource utilization. Precisely, we tackle the problem of dynamic server consolidation, supported by the capacity of Fuzzy Logic to deal with uncertainties and imprecisions inherent in cloud environments. In the preprocessing step, we employ a feature selection strategy to perform attribute selection and, better understand the problem. Data classification was performed by fuzzy rule learning approaches. Comparative evaluations of algorithm classification highlight the remarkable accuracy of FURIA, with IVTURS as a close alternative. While FURIA generates 41 rules, indicating a comprehensive model, IVTURS produces only six, introducing an abstract level to model uncertainties as interval-valued fuzzy membership degrees. The study underscores the relevance of parameter adaptation in mapping feature selection and membership functions to achieve optima
l performance for flexible algorithms in the Cloud Computing environment. Our results underlie the structure of a fuzzy system adapted to CloudSim, integrating energy optimization and Service Level Agreements assurance through different server consolidation strategies. This research contributes valuable perspectives to decision-making processes in the Cloud Computing environment.
(More)