The document presents DBaaS-Expert, a recommender system for selecting the appropriate cloud database from various database-as-a-service (DBaaS) providers. It uses a multi-criteria decision making approach, specifically the Analytic Hierarchy Process (AHP), to score and rank DBaaS offerings based on criteria such as performance, availability, security, and cost. The framework includes a DBaaS ontology for describing offerings and the AHP methodology to obtain criteria weights from user preferences and assess each DBaaS based on the weighted criteria. The goal is to maximize quality and capacity while minimizing cost. Future work includes evaluating the framework and incorporating user feedback into the DBaaS ranking.

1. DBaaS-Expert: A Recommender for the
Selection of the Right Cloud Database
presented by
Alfredo Cuzzocrea
21st Intl. Symposium on Methodologies for Intelligent Systems
Roskilde, Denmark
Soror Sahri*, Rim Moussa‡, Darrel D.E. Long†, Salima Benbernou*
* {soror.sahri,salima.benbernou}@parisdescartes.fr, Univ. Paris Descartes, France
‡ rim.moussa@esti.rnu.tn, LaTICE Univ. of Carthage, Tunisia
† darrel@cs.ucsc.edu, Storage Systems Research Center, Univ. of California , USA
26th, June 2014

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Context
DBaaS Providers
(non exhautive list of providers ) Cloud Rationale
Which DBaaS to choose?

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●DBaaS ?
»An on-demand, secure, and scalable self-service database platform that
automates provisioning and administration of databases (Forrester
2012)
»DBaaS Examples: Amazon Relational Database Service (Amazon RDS),
Microsoft SQL Azure, Heroku PostgreSQL as a service, Amazon Dynamo
DB, Google BigQuery, ...
●Why DBaaS ?
»Might be a suitable solution for companies for which in-house
database solutions are cost-prohibitive
»Cost relates to technology licenses' purchase, administration
expertise, hardware purchase, hardware maintenance, ...
»Cloud advantages:
»Cost management: all services are provided on a pay-per-use basis,
»Improved quality of service (assuming that providers hire experts)
»Resources elasticity: auto-provisioning for scalability through fast new
nodes' deployment and fast release of non-needed nodes
DBaaS

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DBaaS adoption
Source: Next-Generation Operational Databases: 2012-2016
https://451research.com/report­long?icid=2852
Conducted by 451Research, 2013

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●Motivation:
»Increasing number of DBaaSs offerings with different services and cost
plans,
»It's hard to compare offers,
»through check of an advertisement-oriented documentation
»through a common benchmark, Indeed, offers target different data
management requirements (OLAP, OLTP, document-oriented, ...)
●DBaaSs' offerings ranking problem is a typical Muti-Criteria Decision
Making (MCDM) problem. Indeed, given
»M DBaaS offerings: DBaaS1
, DBaaS2
, ..., DBaaSM
»e.g.: Amazon RDS, Google BigQuery, ...
»N Decision criteria: C1
, C2
, ...,CN
»e.g.: Performance, High-availability, Security, Elasticity, ...
»Our goal is to assess DBaaSs' offerings in terms of the set of criteria with
two objectives:
»Maximize Quality and Capacity of Service
»Minimize Cost
Problem Statement

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DBaaS-Expert Framework
ISMIS’2014@Roskilde

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DBaaS Ontology
The concepts of DBaaS ontology are divided into four categories:
● Basic concepts:Basic concepts: DBaaS offer, Cloud Service Provider, Workload Type, Storage Model, Data
Model, Consistency Model, System Constraints, Resource, Trial Version
● Quality of service concepts:Quality of service concepts: SLA, Client Support
● Capacity of service concepts:Capacity of service concepts: High Availability, Security, Elasticity, Scalability,
Interoperability
● Cost of service concepts:Cost of service concepts: Cost Model

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DBaaS Ontology

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DBaaS Ontology
Windows Azure SQL Database ontologyWindows Azure SQL Database ontology

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AHP for DBaaS Ranking
●Analytic Hierarchy Process (AHP):
»Developed by Thomas L. Saaty in 1970,
»Structured technique for complex decisions making, based on
mathematics and user-preferences,
»Well-known and extensively used in problems of priority setting,
university faculty members selection (university of Pennsylvania),
quality of software systems quantification (MicroSoft).
●The outline of the solution of using AHP for DBaaS ranking is
summarized below,
»Devise the AHP Tree
● Decision goal at the root of the AHP Tree
● Hierarchy of criteria at internal nodes
● Alternatives (DBaaS offerings) at leafs
»Compute Criteria Weights according to user preferences
»For each criterion, assess DBaaS offers
»Compute the score of each DBaaS offer

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AHP for DBaaSs' Ranking
---AHP Tree
Decision Goal
Hierarchy of
Criteria
Criterion
DBaaS Offers

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»Using Pairwise Comparisons for criteria belonging to same hierarchy and same
level. The global weight of a criterion is the product of the weights of its
parent-criteria.
»The process includes the check of the consistency of weights, and obliges the
user to update initial weights in case of inconsistency.
●Example:
AHP for DBaaSs' Ranking
---Criteria Weighting
Resulting Weights
Initial Matrix
All criteria belong to the same hierarchy, The user considers that,
● Criterion Ci is half important than Cj (inversely Cj is twice more
important than Ci),
● Criterion Ci is 3 times more important than Ck,
Cj is the most important
Criterion (55.8%) followed
by Ci (32%) then
Ck (12.2%)
Iterative Eigenvector calculus through successive matrix squaring and normalization
Stop when  is negligible which denotes that eigenvector is the same than last iteration

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»For each criterion, an assessment matrix is proposed for comparing DBaaSs. It
is also based on pairwise comparisons.
»The Decision matrix allows the calculus of the score of each DBaaS.
AHP for DBaaSs' Ranking
---Scoring DBaaSs

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Related Work
●Comparison through Benchmarking:
»OLTP-bench for the cloud: by Curino et al., proposal of new
requirements and metrics for running an OLTP workload in the cloud,
2012
»Numerous benchmarks exist for different needs: Terasoft for sort of 1TB,
...
●Comparison using Recommenders,
»CloudRecommender for infrastructure services (IaaS) selection: by Zhang
et al. 2012
»Cloud Genius framework, also for IaaS services selection: by Menzel et
al. 2012
»SMI Cloud: for measuring quality of Cloud Service Providers based on
QoS attributes, by Gark et al. 2011
»Different Services selection from multiple cloud providers: by Quinton et
al. 2013

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Conclusion & Future Work
ISMIS’2014@Roskilde
● Contributions:Contributions: proposing DBaaS-Expert framework, which allows a user to
choose the most suitable DBaaS.
● a DBaaS ontology that aims at description of DBaaS offerings.
● application of AHP to DBaaSs' scoring in terms of a hierarchy of criteria.
● Future work:Future work:
● Evaluation of DBaaS-Expert
● take into account experiences and feedbacks in the ranking of DBaaS
offerings.

16. Thank you for Your Attention
Q & A
DBaaS-Expert: A Recommender for the Selection of
the Right Cloud Database
ISMIS'2014@Roskilde
26th
June, 2014