SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Dept. of Electrical and Computer Engineering, NTUA, 9 Heroon Polytechniou Str, 15773 Athens and Greece

Context (please use complete words only)

Field

Sort by

1 Papers

Mapping of Quality of Service Requirements to Resource Demands for IaaS

Topics: Cloud Application Scalability and Availability; Cloud Applications Performance and Monitoring; Cloud Services; Cloud Quality and Performance

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

Digital Object Identifier: 10.5220/0007676902630270

Proceedings of CLOSER 2019, ISBN: 978-989-758-365-0

Quick 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.