Towards modeling a variable architecture for multi-tenant SaaS-applications

Schroeter, Julia; Cech, Sebastian; Götz, Sebastian; Wilke, Claas; Aßmann, Uwe

doi:10.1145/2110147.2110160

Cited by 30 publications

(22 citation statements)

References 19 publications

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“…They refer to such type of variability as customer-driven variability. Such usage of external variability to represent tenant-based variability is also shown in several other research work, e.g., [5,[8][9][10]. One of the commonalities within such varieties of work is that they use a sort of variability model to represent very specific needs of tenants to make the online software product a configurable entity to allow for the varying requirements.…”

Section: Introductionmentioning

confidence: 73%

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Orthogonal Variability Modeling to Support Multi-cloud Application Configuration

Jamshidi

Pahl

2015

Communications in Computer and Information Science

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Abstract. Cloud service providers benefit from a vast majority of customers due to variability and making profit from commonalities between the cloud services that they provide. Recently, application configuration dimensions has been increased dramatically due to multi-tenant, multi-device and multi-cloud paradigm. This challenges the configuration and customization of cloud-based software that are typically offered as a service due to the intrinsic variability. In this paper, we present a model-driven approach based on variability models originating from the software product line community to handle such multi-dimensional variability in the cloud. We exploit orthogonal variability models to systematically manage and create tenant-specific configuration and customizations. We also demonstrate how such variability models can be utilized to take into account the already deployed application parts to enable harmonized deployments for new tenants in a multi-cloud setting. The approach considers application functional and nonfunctional requirements to provide a set of valid multi-cloud configurations. We illustrate our approach through a case study.

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Section: Introductionmentioning

confidence: 73%

“…As a consequence, in order to attract enough customers, cloud service providers are required [5,7,9,10,12]: (i) To cater for the varying requirements of potential tenants by providing tenant-specific configurations.…”

Section: Research Challengesmentioning

confidence: 99%

Orthogonal Variability Modeling to Support Multi-cloud Application Configuration

Jamshidi

Pahl

2015

Communications in Computer and Information Science

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“…For example, in case of a VDI DaaS model a variant of existing application instances should be deployed dynamically, as proposed in [2,19,22,29,30]. While, for a hosted shared DaaS model the single application instance should be modified when possible.…”

Section: Engineering User-centric Adaptationmentioning

confidence: 99%

“…About multi-tenancy and SPLs, Schroeter et al [29,30] use variability and SPLs techniques to assist the configuration of multi-tenant applications. The authors identify configuration requirements and propose a configuration process using EFMs [30], and also define requirements and middleware for a variable multitenant architecture [29]. While this work focuses on the architectural aspects, our approach engineers MAPE loop and proposes a preferences-based analysis.…”

Section: Related Workmentioning

confidence: 99%

“…From the user perspective, this lack of configurability [22] is a major drawback of multi-tenancy, especially when users' preferences are not known in advance. Several approaches [2,19,22,29,30] have been proposed to support dynamic configuration management in multi-tenant services. Nonetheless, these contributions consider an isolated multi-tenant model, and they focus on deploying different variants of existing service instances at runtime.…”

Section: Introductionmentioning

confidence: 99%

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User-centric adaptation of multi-tenant services: preference-based analysis for service reconfiguration

García-Galán

Pasquale

Trinidad

et al. 2014

Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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Multi-tenancy is a key pillar of cloud services. It allows different tenants to share computing resources transparently and, at the same time, guarantees substantial cost savings for the providers. However, from a user perspective, one of the major drawbacks of multi-tenancy is lack of configurability. Depending on the isolation degree, the same service instance and even the same service configuration may be shared among multiple tenants (i.e. shared multi-tenant service). Moreover tenants usually have different -and in most of the cases -conflicting configuration preferences. To overcome this limitation, this paper introduces a novel approach to support user-centric adaptation in shared multi-tenant services. The adaptation objective aims to maximise tenants' satisfaction, even when tenants and their preferences change during the service life-time. This paper describes how to engineer the activities of the MAPE loop to support user-centric adaptation, and focuses on the analysis of tenants' preferences. In particular, we use a game theoretic analysis to identify a service configuration that maximises tenants' preferences satisfaction. We illustrate and motivate our approach by utilising a multi-tenant desktop scenario. Obtained experimental results demonstrate the feasibility of the proposed analysis.

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