DBaaS-Expert: A Recommender for the Selection of the Right Cloud Database

. The most important beneﬁt of Cloud Computing is that organizations no longer need to expend capital up-front for hardware and software purchases. Indeed, all services are provided on a pay-per-use basis. The cloud services market is forecast to grow, and numerous providers oﬀer database as a service (DBaaS). Nevertheless, as the number of DBaaS’ oﬀerings increases, it becomes diﬃcult to compare various oﬀerings through checking of a documentation ads-oriented. In this paper, we propose and describe DBaaS-Expert – a framework which helps a user to choose the right DBaaS Cloud Provider among DBaaS’ oﬀer-ings. The core components of DBaaS-Expert is ﬁrst an ontology which captures cloud data management systems services concepts, and second a ranking core which scores each DBaaS oﬀer in terms of criteria.


Introduction
Cloud computing has emerged as a new paradigm, which allows enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction [1].Cloud providers typically publish their service description, pricing policies and Service-Level-Agreement (SLA) rules on their websites in various formats.A data management system is one of the applications that are deployed in the cloud, the service is denoted as database-as-a-service (DBaaS).Nevertheless, as the number of DBaaS offerings increases, with different cost plans and different services, it becomes necessary to be able to automate the ranking of DBaaS offerings along a company needs.Therefore, a company should have detailed knowledge of the offerings of cloud providers that can meet its operational needs.This is not obvious, since expertise in cloud computing is required but is lacking.
We propose DBaaS-Expert -a framework addressing the selection of the most suitable cloud-based data management system.In order to develop this framework, first we conducted a thorough DBaaS offerings review.Second, we propose a list of dimensions, which describe DBaaS offerings and an ontology for DBaaS.Third, we compute the ranking values of database service candidates based on user requirements.In our work, we perform the ranking following a known method of multi-criteria decision-making (MCDM): Analytic Hierarchy Process (AHP).To the best of our knowledge, there is no existing work which addressed the problem of selection of the right DBaaS offer.The remainder of the paper is organized as follows.A review of related work is provided in Section 2. In Section 3, DBaaS framework is presented.Section 4 presents DBaaS ontology.Section 5 presents the ranking core based on AHP.In Section 6, a use case is devised for validating our framework.Finally, we conclude the paper.

Related Work
The existence of multiple options and features of cloud services, makes the selection of the appropriate cloud provider very difficult and challenging.Several works have proposed to automate cloud service selection for IaaS and PaaS models.Next, we overview related work and highlight our contribution.
Some reviewed papers are based on benchmarking [2,3].Most of them propose revolving well known TPC benchmarks into benchmarks for data management systems' assessment in the cloud.Curino et al. [4] propose OLTP-Bench, an open-source framework for benchmarking on-line transaction processing (OLTP) and web workloads.Other reviewed papers [5-7] adopted a different approach based on the proposal of a meta-data model for the description of Cloud Service Providers (CSP) offerings.Among ontology-based papers one cite Zhang et al. [7].They implemented CloudRecommender -a system for infrastructure services (IaaS) selection.They propose a Cloud Computing Ontology to facilitate the discovery of IaaS services categorized into functional services and QoS data.Variability modeling is used to understand and define commonalities and variabilities in software product lines and to support product derivation.For instance, Wittern et al. [6] adopt feature modeling to capture aspects and configurations of cloud services.Quinton et al. [5] address services selection from multiple CSPs using Hybrid Modeling.Indeed, within a multi-cloud configuration, a CSP A is selected for hosting the database, while a CSP B is selected for hosting the application.They use feature models to describe cloud systems configurations.For specific description format, Arkaitz et al. [8], have defined an XML schema that guides the description of the different capabilities of cloud storage systems such Amazon, Azure.
Multi-Criteria Decision Making methods take into consideration multiple conflicting criteria (p.e., cost vs.quality) that need to be evaluated in making decisions.Menzel et al. [9] propose CloudGenius framework that provides a multicriteria approach in decision support, namely AHP technique, to automate the selection process focusing on IaaS models.Garg et al. [10] provide a SMICLOUD framework measuring the quality of CSPs based on QoS attributes proposed by Cloud Service Measurement Index Consortium (CSMIC) [11] and ranking the cloud services according to these attributes.SMICloud considers only quantitative attributes (such that response time, cost) in the context of IaaS Clouds.