Authors:
Ioannis Bouras
1
;
Fotis Aisopos
1
;
John Violos
1
;
George Kousiouris
1
;
Alexandros Psychas
1
;
Theodora Varvarigou
1
;
Gerasimos Xydas
2
;
Dimitrios Charilas
2
and
Yiannis Stavroulas
2
Affiliations:
1
Dept. of Electrical and Computer Engineering, NTUA, 9 Heroon Polytechniou Str, 15773 Athens and Greece
;
2
Cognity S.A., 42 Kifissias Av., 15125 Marousi, Athens and Greece
Keyword(s):
Infrastructure as a Service, Cloud Computing, Quality of Service, Artificial Neural Networks, Resource Selection.
Abstract:
Deciding and reserving appropriate resources in the Cloud, is a basic initial step for adopters when employing an Infrastructure as a Service to host their application. However, the size and number of Virtual Machines used, along with the expected application workload, will highly influence its operation, in terms of the observed Quality of Service. This paper proposes a machine learning approach, based on Artificial Neural Networks, for mapping Quality of Service required levels and (expected) application workload to concrete resource demands. The presented solution is evaluated through a comercial Customer Relationship Management application, generating a training set of realistic workload and Quality of Service measurements in order to illustrate the effectiveness of the proposed technique in a real-world scenario.