A declarative recommender system for cloud infrastructure services selection

Miranda Zhang*, Rajiv Ranjan, Surya Nepal, Michael Menzel, Armin Haller

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

63 Citations (Scopus)

Abstract

The cloud infrastructure services landscape advances steadily leaving users in the agony of choice. Therefore, we present CloudRecommender, a new declarative approach for selecting Cloud-based infrastructure services. CloudRecommender automates the mapping of users' specified application requirements to cloud service configurations. We formally capture cloud service configurations in ontology and provide its implementation in a structured data model which can be manipulated through both regular expressions and SQL. By exploiting the power of a visual programming language (widgets), CloudRecommender further enables simplified and intuitive cloud service selection. We describe the design and a prototype implementation of CloudRecommender, and demonstrate its effectiveness and scalability through a service configuration selection experiment on most of today's prominent cloud providers including Amazon, Azure, and GoGrid.

Original languageEnglish
Title of host publicationEconomics of Grids, Clouds, Systems, and Services - 9th International Conference, GECON 2012, Proceedings
Pages102-113
Number of pages12
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event9th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2012 - Berlin, Germany
Duration: 27 Nov 201228 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7714 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Economics of Grids, Clouds, Systems, and Services, GECON 2012
Country/TerritoryGermany
CityBerlin
Period27/11/1228/11/12

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